Publikationen im NUM
Hier finden Sie eine Liste der Publikationen, die im Zusammenhang mit dem Netzwerk Universitätsmedizin in der ersten und zweiten Förderphase entstanden sind.
K. O. Yusuf,
I. Chaplinskaya-Sobol,
A. Schoneberg,
S. Hanss,
H. Valentin,
B. Lorenz-Depiereux,
S. Hansch,
K. Fiedler,
M. Scherer,
S. Sikdar,
O. Miljukov,
J. Reese,
P. Wagner,
I. Bröhl,
R. Geisler,
J. J. Vehreschild,
S. Blaschke,
C. Bellinghausen,
M. Milovanovic and
D. Krefting,
"Impact of Clinical Study Implementation on Data Quality Assessments - using Contradictions within Interdependent Health Data Items as a Pilot Indicator",
eingereicht bei GMDS Jahrestagung Heilbronn,
Sep.
2023.
K. O. Yusuf,
I. Chaplinskaya-Sobol,
A. Schoneberg,
S. Hanss,
H. Valentin,
B. Lorenz-Depiereux,
S. Hansch,
K. Fiedler,
M. Scherer,
S. Sikdar,
O. Miljukov,
J. Reese,
P. Wagner,
I. Bröhl,
R. Geisler,
J. J. Vehreschild,
S. Blaschke,
C. Bellinghausen,
M. Milovanovic and
D. Krefting,
"Impact of Clinical Study Implementation on Data Quality Assessments – Using Contradictions within Interdependent Health Data Items as a Pilot Indicator, German Medical Data Sciences 2023 – Science close to People",
Sep.
2023.
| DOI: | 10.3233/SHTI230707 |
G. L. Baumgärtner,
C. A. Hamm,
S. Schulze-Weddige,
R. Ruppel,
N. L. Beetz,
M. Rudolph,
F. Dräger,
K. P. Froböse,
H. Posch,
J. Lenk,
F. Biessmann and
T. Penzkofer,
"Metadata-independent classification of MRI sequences using convolutional neural networks: Successful application to prostate MRI",
European journal of radiology,
vol. 166,
pp. 110964,
Sep.
2023.
Abstract:
The ever-increasing volume of medical imaging data and interest in Big Data research brings challenges to data organization, categorization, and retrieval. Although the radiological value chain is almost entirely digital, data structuring has been widely performed pragmatically, but with insufficient naming and metadata standards for the stringent needs of image analysis. To enable automated data management independent of naming and metadata, this study focused on developing a convolutional neural network (CNN) that classifies medical images based solely on voxel data. A 3D CNN (3D-ResNet18) was trained using a dataset of 31,602 prostate MRI volumes with 10 different sequence types of 1243 patients. A five-fold cross-validation approach with patient-based splits was chosen for training and testing. Training was repeated with a gradual reduction in training data assessing classification accuracies to determine the minimum training data required for sufficient performance. The trained model and developed method were tested on three external datasets. The model achieved an overall accuracy of 99.88 % ± 0.13 % in classifying typical prostate MRI sequence types. When being trained with approximately 10 % of the original cohort (112 patients), the CNN still achieved an accuracy of 97.43 % ± 2.10 %. In external testing the model achieved sensitivities of > 90 % for 10/15 tested sequence types. The herein developed CNN enabled automatic and reliable sequence identification in prostate MRI. Ultimately, such CNN models for voxel-based sequence identification could substantially enhance the management of medical imaging data, improve workflow efficiency and data quality, and allow for robust clinical AI workflows.
| DOI: | 10.1016/j.ejrad.2023.110964 |
| Datei: | https://pubmed.ncbi.nlm.nih.gov/37453274/ |
Abstract:
The ever-increasing volume of medical imaging data and interest in Big Data research brings challenges to data organization, categorization, and retrieval. Although the radiological value chain is almost entirely digital, data structuring has been widely performed pragmatically, but with insufficient naming and metadata standards for the stringent needs of image analysis. To enable automated data management independent of naming and metadata, this study focused on developing a convolutional neural network (CNN) that classifies medical images based solely on voxel data. A 3D CNN (3D-ResNet18) was trained using a dataset of 31,602 prostate MRI volumes with 10 different sequence types of 1243 patients. A five-fold cross-validation approach with patient-based splits was chosen for training and testing. Training was repeated with a gradual reduction in training data assessing classification accuracies to determine the minimum training data required for sufficient performance. The trained model and developed method were tested on three external datasets. The model achieved an overall accuracy of 99.88 % ± 0.13 % in classifying typical prostate MRI sequence types. When being trained with approximately 10 % of the original cohort (112 patients), the CNN still achieved an accuracy of 97.43 % ± 2.10 %. In external testing the model achieved sensitivities of > 90 % for 10/15 tested sequence types. The herein developed CNN enabled automatic and reliable sequence identification in prostate MRI. Ultimately, such CNN models for voxel-based sequence identification could substantially enhance the management of medical imaging data, improve workflow efficiency and data quality, and allow for robust clinical AI workflows.
[en]
M. S. Woo,
M. Shafiq,
A. Fitzek,
M. Dottermusch,
B. Mohammadi,
C. Mayer,
L. C. Bal,
L. Raich,
J. Matschke,
S. Krasemann,
S. Pfefferle,
T. T. Brehm,
M. Lütgehetmann,
J. Schädler,
J. Wiesch,
B. Ondruschka,
M. A. Friese and
M. Glatzel,
"Vagus nerve inflammation contributes to dysautonomia in COVID-19",
Acta Neuropathologica,
vol. 146,
no. 3,
pp. 387—394,
Sep.
2023.
Abstract:
Dysautonomia has substantially impacted acute COVID-19 severity as well as symptom burden after recovery from COVID-19 (long COVID), yet the underlying causes remain unknown. Here, we hypothesized that vagus nerves are affected in COVID-19 which might contribute to autonomic dysfunction. We performed a histopathological characterization of postmortem vagus nerves from COVID-19 patients and controls, and detected SARS-CoV-2 RNA together with inflammatory cell infiltration composed primarily of monocytes. Furthermore, we performed RNA sequencing which revealed a strong inflammatory response of neurons, endothelial cells, and Schwann cells which correlated with SARS-CoV-2 RNA load. Lastly, we screened a clinical cohort of 323 patients to detect a clinical phenotype of vagus nerve affection and found a decreased respiratory rate in non-survivors of critical COVID-19. Our data suggest that SARS-CoV-2 induces vagus nerve inflammation followed by autonomic dysfunction which contributes to critical disease courses and might contribute to dysautonomia observed in long COVID.
| DOI: | 10.1007/s00401-023-02612-x |
| Datei: | https://doi.org/10.1007/s00401-023-02612-x |
Abstract:
Dysautonomia has substantially impacted acute COVID-19 severity as well as symptom burden after recovery from COVID-19 (long COVID), yet the underlying causes remain unknown. Here, we hypothesized that vagus nerves are affected in COVID-19 which might contribute to autonomic dysfunction. We performed a histopathological characterization of postmortem vagus nerves from COVID-19 patients and controls, and detected SARS-CoV-2 RNA together with inflammatory cell infiltration composed primarily of monocytes. Furthermore, we performed RNA sequencing which revealed a strong inflammatory response of neurons, endothelial cells, and Schwann cells which correlated with SARS-CoV-2 RNA load. Lastly, we screened a clinical cohort of 323 patients to detect a clinical phenotype of vagus nerve affection and found a decreased respiratory rate in non-survivors of critical COVID-19. Our data suggest that SARS-CoV-2 induces vagus nerve inflammation followed by autonomic dysfunction which contributes to critical disease courses and might contribute to dysautonomia observed in long COVID.
[en]
B. Salzberger,
A. Mellmann,
A. Bludau,
S. Ciesek,
V. Corman,
A. Dilthey,
T. Donker,
T. Eckmanns,
R. Egelkamp,
S. Gatermann,
H. Grundmann,
G. Häcker,
M. Kaase,
B. Lange,
M. Mielke,
M. Pletz,
T. Semmler,
A. Thürmer,
L. Wieler,
T. Wolff,
A. Widmer and
S. Scheithauer,
"An appeal for strengthening genomic pathogen surveillance to
improve pandemic preparedness and infection prevention: the
German perspective",
Infection,
vol. 51,
no. 4,
pp. 805—811,
Aug.
2023.
| DOI: | 10.1007/s15010-023-02040-9 |
P. Jacobs,
C. Ehrengut,
A. M. Bucher,
T. Penzkofer,
M. Lukas,
J. Kleesiek and
T. Denecke,
"Challenges in Implementing the Local Node Infrastructure for a National Federated Machine Learning Network in Radiology.",
Healthcare (Basel, Switzerland),
Aug.
2023.
Abstract:
Datengetriebenes maschinelles Lernen in der medizinischen Forschung und Diagnostik benötigt von klinischen Experten kuratierte großformatige Datensätze. Die Generierung großer Datensätze kann im Hinblick auf den Ressourcenverbrauch und den Zeitaufwand herausfordern, während die Verallgemeinerlichkeit und Validierung der entwickelten Modelle deutlich von der Vielfalt der Datenquellen profitieren. Schulungsalgorithmen zu kleineren dezentralen Datensätzen durch föderiertes Lernen können den Aufwand reduzieren, erfordern aber die Implementierung einer spezifischen und ambitionierten Infrastruktur, um Daten, Algorithmen und Rechenzeit zu teilen. Darüber hinaus bietet es die Möglichkeit, die Daten lokal zu pflegen und zu halten. Somit können Datensicherheitsprobleme vermieden werden, da Patientendaten nicht geteilt werden dürfen. Machine Learning Modelle werden auf lokalen Daten durch das Teilen des Modells und durch ein etabliertes Netzwerk trainiert. Neben kommerziellen Anwendungen gibt es auch zahlreiche akademische und maßgeschneiderte Implementierungen von Netzwerkinfrastrukturen. Die Konfiguration dieser Netzwerke unterscheidet sich in erster Linie, hält sich aber an einen Standardrahmen aus Grundkomponenten. In dieser technischen Anmerkung schlagen wir grundlegende Infrastrukturanforderungen für die Datenverwaltung, Datenwissenschafts-Workflows und lokale Knoten-Setup vor und berichten über die Vorteile und erfahrene Fallstricke bei der Umsetzung der lokalen Infrastruktur mit der deutschen Radiologischen kooperativen Netzwerkinitiative als Anwendungsbeispiel. Wir zeigen, wie die Infrastruktur auf einigen Basiskomponenten aufgebaut werden kann, um die Bedürfnisse eines föderierten Lernnetzwerks zu reflektieren und wie sie sowohl unter Berücksichtigung lokaler als auch globaler Netzwerkanforderungen umgesetzt werden können. Nach der Analyse des Bereitstellungsprozesses in verschiedenen Einstellungen und Szenarien empfehlen wir, den lokalen Knoten in eine bestehende klinische IT-Infrastruktur zu integrieren. Dieser Ansatz bietet Vorteile in Bezug auf Wartungs- und Bereitstellungsaufwand im Vergleich zur externen Integration in einer separaten Umgebung (z.B. der Radiologieabteilung). Diese vorgeschlagene Grundarbeit kann als beispielhafte Entwicklungsleitlinie für zukünftige Anwendungen von föderierten Lernnetzwerken in klinischen und wissenschaftlichen Umgebungen betrachtet werden.
| DOI: | 10.3390/healthcare11172377 |
Abstract:
Datengetriebenes maschinelles Lernen in der medizinischen Forschung und Diagnostik benötigt von klinischen Experten kuratierte großformatige Datensätze. Die Generierung großer Datensätze kann im Hinblick auf den Ressourcenverbrauch und den Zeitaufwand herausfordern, während die Verallgemeinerlichkeit und Validierung der entwickelten Modelle deutlich von der Vielfalt der Datenquellen profitieren. Schulungsalgorithmen zu kleineren dezentralen Datensätzen durch föderiertes Lernen können den Aufwand reduzieren, erfordern aber die Implementierung einer spezifischen und ambitionierten Infrastruktur, um Daten, Algorithmen und Rechenzeit zu teilen. Darüber hinaus bietet es die Möglichkeit, die Daten lokal zu pflegen und zu halten. Somit können Datensicherheitsprobleme vermieden werden, da Patientendaten nicht geteilt werden dürfen. Machine Learning Modelle werden auf lokalen Daten durch das Teilen des Modells und durch ein etabliertes Netzwerk trainiert. Neben kommerziellen Anwendungen gibt es auch zahlreiche akademische und maßgeschneiderte Implementierungen von Netzwerkinfrastrukturen. Die Konfiguration dieser Netzwerke unterscheidet sich in erster Linie, hält sich aber an einen Standardrahmen aus Grundkomponenten. In dieser technischen Anmerkung schlagen wir grundlegende Infrastrukturanforderungen für die Datenverwaltung, Datenwissenschafts-Workflows und lokale Knoten-Setup vor und berichten über die Vorteile und erfahrene Fallstricke bei der Umsetzung der lokalen Infrastruktur mit der deutschen Radiologischen kooperativen Netzwerkinitiative als Anwendungsbeispiel. Wir zeigen, wie die Infrastruktur auf einigen Basiskomponenten aufgebaut werden kann, um die Bedürfnisse eines föderierten Lernnetzwerks zu reflektieren und wie sie sowohl unter Berücksichtigung lokaler als auch globaler Netzwerkanforderungen umgesetzt werden können. Nach der Analyse des Bereitstellungsprozesses in verschiedenen Einstellungen und Szenarien empfehlen wir, den lokalen Knoten in eine bestehende klinische IT-Infrastruktur zu integrieren. Dieser Ansatz bietet Vorteile in Bezug auf Wartungs- und Bereitstellungsaufwand im Vergleich zur externen Integration in einer separaten Umgebung (z.B. der Radiologieabteilung). Diese vorgeschlagene Grundarbeit kann als beispielhafte Entwicklungsleitlinie für zukünftige Anwendungen von föderierten Lernnetzwerken in klinischen und wissenschaftlichen Umgebungen betrachtet werden.
H. Meyer,
B. Melekh,
A. Wienke,
J. Borggrefe and
A. Surov,
"Clinical importance of thoracal lymphadenopathy in COVID-19.",
Journal of infection and public health,
Aug.
2023.
Abstract:
Thorakale Lymphadenopathie kann Prognosen bei Patienten mit Koronavirus-Krankheit 2019 (COVID-19) vorhersagen, wenngleich die gemeldeten Daten unschlüssig sind. Ziel der vorliegenden Analyse war es, die betroffenen Lymphknotenstationen und die kumulative Lymphknotengröße, die sich aus der berechneten Tomographie (CT) ergibt, zur Vorhersage von 30-tägiger Mortalität bei Patienten mit COVID-19 zu analysieren. Die klinische Datenbank wurde für Patienten mit COVID-19 zwischen 2020 und 2022 rückwirkend untersucht. Insgesamt wurden 177 Patienten (63 weiblich, 35,6%) in die Analyse aufgenommen. Thoracal Lymphadenopathie wurde durch einen kurzen Achsdurchmesser über 10 mm definiert. Die kumulative Lymphknotengröße der größten Lymphknoten wurde berechnet und die Menge der betroffenen Lymphknotenstationen quantifiziert. Insgesamt starben 53 Patienten (29,9%) innerhalb der 30-tägigen Beobachtungszeit. Bei der ICU wurden 108 Patienten (61,0%) zugelassen und 91 Patienten mussten intubiert werden (51.4%). Insgesamt gab es 130 Patienten mit Lymphadenopathie (73,4%). Die mittlere Anzahl der betroffenen Lymphknotenwerte lag bei Nicht-Überlebenden höher als bei Überlebenden (also 4,0 vs 2,2, p < 0,001). Die kumulative Größe war auch bei Nichtüberlebenden höher als bei Überlebenden (Mittel 55,9 mm gegenüber 44.1 mm, p = 0,006). Die Anwesenheit von Lymphadenopathie wurde mit 30-tägiger Mortalität in einer multivariablen Analyse verbunden, OR = 2,99 (95% CI 1,20 - 7,43), p = 0.02. Eine Thorax-Lymphadenopathie mit kumulativer Größe und von CT-Bildern abgeleiteten Betroffenen ist bei Patienten mit COVID-19 mit 30-tägiger Mortalität verbunden.
| DOI: | 10.1016/j.jiph.2023.05.031 |
Abstract:
Thorakale Lymphadenopathie kann Prognosen bei Patienten mit Koronavirus-Krankheit 2019 (COVID-19) vorhersagen, wenngleich die gemeldeten Daten unschlüssig sind. Ziel der vorliegenden Analyse war es, die betroffenen Lymphknotenstationen und die kumulative Lymphknotengröße, die sich aus der berechneten Tomographie (CT) ergibt, zur Vorhersage von 30-tägiger Mortalität bei Patienten mit COVID-19 zu analysieren. Die klinische Datenbank wurde für Patienten mit COVID-19 zwischen 2020 und 2022 rückwirkend untersucht. Insgesamt wurden 177 Patienten (63 weiblich, 35,6%) in die Analyse aufgenommen. Thoracal Lymphadenopathie wurde durch einen kurzen Achsdurchmesser über 10 mm definiert. Die kumulative Lymphknotengröße der größten Lymphknoten wurde berechnet und die Menge der betroffenen Lymphknotenstationen quantifiziert. Insgesamt starben 53 Patienten (29,9%) innerhalb der 30-tägigen Beobachtungszeit. Bei der ICU wurden 108 Patienten (61,0%) zugelassen und 91 Patienten mussten intubiert werden (51.4%). Insgesamt gab es 130 Patienten mit Lymphadenopathie (73,4%). Die mittlere Anzahl der betroffenen Lymphknotenwerte lag bei Nicht-Überlebenden höher als bei Überlebenden (also 4,0 vs 2,2, p < 0,001). Die kumulative Größe war auch bei Nichtüberlebenden höher als bei Überlebenden (Mittel 55,9 mm gegenüber 44.1 mm, p = 0,006). Die Anwesenheit von Lymphadenopathie wurde mit 30-tägiger Mortalität in einer multivariablen Analyse verbunden, OR = 2,99 (95% CI 1,20 - 7,43), p = 0.02. Eine Thorax-Lymphadenopathie mit kumulativer Größe und von CT-Bildern abgeleiteten Betroffenen ist bei Patienten mit COVID-19 mit 30-tägiger Mortalität verbunden.
[en]
Z. I. Kolabas,
L. B. Kuemmerle,
R. Perneczky,
B. Förstera,
S. Ulukaya,
S. Kapoor,
L. M. Bartos,
M. Büttner,
O. S. Caliskan,
Z. Rong,
H. Mai,
L. Höher,
D. Jeridi,
M. Molbay,
I. Khalin,
I. K. Deligiannis,
M. Negwer,
K. Roberts,
A. Simats,
O. Carofiglio,
M. I. Todorov,
I. Horvath,
F. Ozturk,
S. Hummel,
G. Biechele,
A. Zatcepin,
M. Unterrainer,
J. Gnörich,
J. Roodselaar,
J. Shrouder,
P. Khosravani,
B. Tast,
L. Richter,
L. Díaz-Marugán,
D. Kaltenecker,
L. Lux,
Y. Chen,
S. Zhao,
B. Rauchmann,
M. Sterr,
I. Kunze,
K. Stanic,
V. W. Y. Kan,
S. Besson-Girard,
S. Katzdobler,
C. Palleis,
J. Schädler,
J. C. Paetzold,
S. Liebscher,
A. E. Hauser,
O. Gokce,
H. Lickert,
H. Steinke,
C. Benakis,
C. Braun,
C. P. Martinez-Jimenez,
K. Buerger,
G. Höglinger,
J. Levin,
C. Haass,
A. Kopczak,
M. Dichgans,
J. Havla,
T. Kümpfel,
M. Kerschensteiner,
M. Schifferer,
M. Simons,
A. Liesz,
N. Krahmer,
O. A. Bayraktar,
N. Franzmeier,
N. Plesnila,
S. Erener,
V. G. Puelles,
C. Delbridge,
H. S. Bhatia,
F. Hellal,
M. Elsner,
I. Bechmann,
B. Ondruschka,
M. Brendel,
F. J. Theis and
A. Erturk,
"Distinct molecular profiles of skull bone marrow in health and neurological disorders",
Cell,
vol. 186,
no. 17,
pp. 3706-3725,
Aug.
2023.
| DOI: | 10.1016/j.cell.2023.07.009 |
| Datei: | https://linkinghub.elsevier.com/retrieve/pii/S0092867423007420 |
[en]
B. Lange,
V. Jaeger,
M. Harries,
V. R"ucker,
H. Streeck,
S. Blaschke,
A. Petersmann,
N. Toepfner,
M. Nauck,
M. J. Hassenstein,
M. Dreier,
I. von Holt,
A. Budde,
A. Bartz,
J. Ortmann,
M. Kurosinski,
R. Berner,
M. Borsche,
G. Brandhorst,
M. Brinkmann,
K. Budde,
M. Deckena,
G. Engels,
M. Fenzlaff,
C. Härtel,
O. Hovardovska,
A. Katalinic,
K. Kehl,
M. Kohls,
S. Krüger,
W. Lieb,
K. M. Meyer-Schlinkmann,
T. Pischon,
D. Rosenkranz,
N. Rübsamen,
J. Rupp,
C. Schäfer,
M. Schattschneider,
A. Schlegtendal,
S. Schlinkert,
L. Schmidbauer,
K. Schulze-Wundling,
S. Störk,
C. Tiemann,
H. V"olzke,
T. Winter,
C. Klein,
J. Liese,
F. Brinkmann,
P. F. Ottensmeyer,
J. Reese,
P. Heuschmann and
A. Karch,
"Estimates of protection levels against SARS-CoV-2 infection and
severe COVID-19 in Germany before the 2022/2023 winter season:
the IMMUNEBRIDGE project",
Infection,
Aug.
2023.
Abstract:
PURPOSE: Despite the need to generate valid and reliable estimates of protection levels against SARS-CoV-2 infection and severe course of COVID-19 for the German population in summer 2022, there was a lack of systematically collected population-based data allowing for the assessment of the protection level in real time. METHODS: In the IMMUNEBRIDGE project, we harmonised data and biosamples for nine population-/hospital-based studies (total number of participants n = 33,637) to provide estimates for protection levels against SARS-CoV-2 infection and severe COVID-19 between June and November 2022. Based on evidence synthesis, we formed a combined endpoint of protection levels based on the number of self-reported infections/vaccinations in combination with nucleocapsid/spike antibody responses ("confirmed exposures"). Four confirmed exposures represented the highest protection level, and no exposure represented the lowest. RESULTS: Most participants were seropositive against the spike antigen; 37% of the participants $\geq$ 79 years had less than four confirmed exposures (highest level of protection) and 5% less than three. In the subgroup of participants with comorbidities, 46-56% had less than four confirmed exposures. We found major heterogeneity across federal states, with 4-28% of participants having less than three confirmed exposures. CONCLUSION: Using serological analyses, literature synthesis and infection dynamics during the survey period, we observed moderate to high levels of protection against severe COVID-19, whereas the protection against SARS-CoV-2 infection was low across all age groups. We found relevant protection gaps in the oldest age group and amongst individuals with comorbidities, indicating a need for additional protective measures in these groups.
| DOI: | https://doi.org/10.1007/s15010-023-02071-2 |
Abstract:
PURPOSE: Despite the need to generate valid and reliable estimates of protection levels against SARS-CoV-2 infection and severe course of COVID-19 for the German population in summer 2022, there was a lack of systematically collected population-based data allowing for the assessment of the protection level in real time. METHODS: In the IMMUNEBRIDGE project, we harmonised data and biosamples for nine population-/hospital-based studies (total number of participants n = 33,637) to provide estimates for protection levels against SARS-CoV-2 infection and severe COVID-19 between June and November 2022. Based on evidence synthesis, we formed a combined endpoint of protection levels based on the number of self-reported infections/vaccinations in combination with nucleocapsid/spike antibody responses ("confirmed exposures"). Four confirmed exposures represented the highest protection level, and no exposure represented the lowest. RESULTS: Most participants were seropositive against the spike antigen; 37% of the participants $\geq$ 79 years had less than four confirmed exposures (highest level of protection) and 5% less than three. In the subgroup of participants with comorbidities, 46-56% had less than four confirmed exposures. We found major heterogeneity across federal states, with 4-28% of participants having less than three confirmed exposures. CONCLUSION: Using serological analyses, literature synthesis and infection dynamics during the survey period, we observed moderate to high levels of protection against severe COVID-19, whereas the protection against SARS-CoV-2 infection was low across all age groups. We found relevant protection gaps in the oldest age group and amongst individuals with comorbidities, indicating a need for additional protective measures in these groups.
M. Granai,
V. Warm,
A. Vogelsberg,
J. Milla,
K. Greif,
U. Vogel,
T. Bakchoul,
P. Rosenberger,
L. Quintanilla-Martinez,
C. Schürch,
K. Klingel,
F. Fend and
H. Bösmüller,
"Impact of P-selectin-PSGL-1 Axis on Platelet-Endothelium-Leucocyte Interactions in Fatal COVID-19",
Laboratory Investigation; a Journal of Technical Methods and Pathology,
vol. 103,
no. 8,
pp. 100179,
Aug.
2023.
| DOI: | 10.1016/j.labinv.2023.100179 |
| Datei: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202465/ |
[en]
J. Lücke,
F. Heinrich,
J. Malsy,
N. Meins,
J. Schnell,
M. Böttcher,
M. Nawrocki,
T. Zhang,
F. Bertram,
M. Sabihi,
J. Kempski,
T. Blankenburg,
A. Duprée,
M. Reeh,
S. Wolter,
O. Mann,
J. R. Izbicki,
A. W. Lohse,
N. Gagliani,
M. Lütgehetmann,
M. J. Bunders,
M. Altfeld,
G. Sauter,
A. D. Giannou,
S. Krasemann,
B. Ondruschka and
S. Huber,
"Intestinal IL-1β Plays a Role in Protecting against SARS-CoV-2 Infection",
The Journal of Immunology,
vol. 211,
no. 6,
pp. 1052–1061,
Aug.
2023.
Abstract:
Abstract The intestine is constantly balancing the maintenance of a homeostatic microbiome and the protection of the host against pathogens such as viruses. Many cytokines mediate protective inflammatory responses in the intestine, among them IL-1β. IL-1β is a proinflammatory cytokine typically activated upon specific danger signals sensed by the inflammasome. SARS-CoV-2 is capable of infecting multiple organs, including the intestinal tract. Severe cases of COVID-19 were shown to be associated with a dysregulated immune response, and blocking of proinflammatory pathways was demonstrated to improve patient survival. Indeed, anakinra, an Ab against the receptor of IL-1β, has recently been approved to treat patients with severe COVID-19. However, the role of IL-1β during intestinal SARS-CoV-2 infection has not yet been investigated. Here, we analyzed postmortem intestinal and blood samples from patients who died of COVID-19. We demonstrated that high levels of intestinal IL-1β were associated with longer survival time and lower intestinal SARS-CoV-2 RNA loads. Concurrently, type I IFN expression positively correlated with IL-1β levels in the intestine. Using human intestinal organoids, we showed that autocrine IL-1β sustains RNA expression of IFN type I by the intestinal epithelial layer. These results outline a previously unrecognized key role of intestinal IL-1β during SARS-CoV-2 infection.
| DOI: | 10.4049/jimmunol.2200844 |
| Datei: | https://journals.aai.org/jimmunol/article/doi/10.4049/jimmunol.2200844/265832/Intestinal-IL-1-Plays-a-Role-in-Protecting-against |
Abstract:
Abstract The intestine is constantly balancing the maintenance of a homeostatic microbiome and the protection of the host against pathogens such as viruses. Many cytokines mediate protective inflammatory responses in the intestine, among them IL-1β. IL-1β is a proinflammatory cytokine typically activated upon specific danger signals sensed by the inflammasome. SARS-CoV-2 is capable of infecting multiple organs, including the intestinal tract. Severe cases of COVID-19 were shown to be associated with a dysregulated immune response, and blocking of proinflammatory pathways was demonstrated to improve patient survival. Indeed, anakinra, an Ab against the receptor of IL-1β, has recently been approved to treat patients with severe COVID-19. However, the role of IL-1β during intestinal SARS-CoV-2 infection has not yet been investigated. Here, we analyzed postmortem intestinal and blood samples from patients who died of COVID-19. We demonstrated that high levels of intestinal IL-1β were associated with longer survival time and lower intestinal SARS-CoV-2 RNA loads. Concurrently, type I IFN expression positively correlated with IL-1β levels in the intestine. Using human intestinal organoids, we showed that autocrine IL-1β sustains RNA expression of IFN type I by the intestinal epithelial layer. These results outline a previously unrecognized key role of intestinal IL-1β during SARS-CoV-2 infection.
[eng]
A. T. Azeke,
J. Schädler,
B. Ondruschka,
S. Steurer,
D. Möbius and
A. Fitzek,
"Minimally Invasive Tissue Sampling via Post Mortem Ultrasound: A Feasible Tool (Not Only) in Infectious Diseases-A Case Report",
Diagnostics (Basel, Switzerland),
vol. 13,
no. 16,
pp. 2643,
Aug.
2023.
Abstract:
In the past years the number of hospital autopsies have declined steadily, becoming almost excluded from medical training. Medicolegal (forensic) autopsies account for almost all autopsies, whereas hospital autopsies are becoming increasingly rare. Minimally invasive tissue sampling (MITS) using post mortem ultrasound offers the opportunity to increase the number of post mortem examinations in a clinical and even forensic context. MITS is a needle-based post mortem procedure that uses (radiological) imaging techniques to examine major organs of the body, acquire tissue samples and aspirate fluid from the body cavities or hollow organs. In this study, MITS was used to determine the presence of other co-existing diseases in a deceased infected 97-year-old woman with severe acute respiratory syndrome coronavirus 2. The examination of her body was carried out using ultrasound as an imaging tool and to gather ultrasound-guided biopsies as conventional autopsy was rejected by the next of kin. Ultrasound and histology identified an intravesical mass leading to an obstruction of the urinary outlet resulting in bilateral hydronephrosis and purulent pyelonephritis, which was unknown during her lifetime. Histopathological examination revealed the tumor mass to be a squamous cell carcinoma. This study has shown that MITS can be used to determine the cause of death and the presence of concomitant diseases in the infectious deceased.
| DOI: | 10.3390/diagnostics13162643 |
| Datei: | https://doi.org/10.3390%2Fdiagnostics13162643 |
Abstract:
In the past years the number of hospital autopsies have declined steadily, becoming almost excluded from medical training. Medicolegal (forensic) autopsies account for almost all autopsies, whereas hospital autopsies are becoming increasingly rare. Minimally invasive tissue sampling (MITS) using post mortem ultrasound offers the opportunity to increase the number of post mortem examinations in a clinical and even forensic context. MITS is a needle-based post mortem procedure that uses (radiological) imaging techniques to examine major organs of the body, acquire tissue samples and aspirate fluid from the body cavities or hollow organs. In this study, MITS was used to determine the presence of other co-existing diseases in a deceased infected 97-year-old woman with severe acute respiratory syndrome coronavirus 2. The examination of her body was carried out using ultrasound as an imaging tool and to gather ultrasound-guided biopsies as conventional autopsy was rejected by the next of kin. Ultrasound and histology identified an intravesical mass leading to an obstruction of the urinary outlet resulting in bilateral hydronephrosis and purulent pyelonephritis, which was unknown during her lifetime. Histopathological examination revealed the tumor mass to be a squamous cell carcinoma. This study has shown that MITS can be used to determine the cause of death and the presence of concomitant diseases in the infectious deceased.
A. Surov,
M. Thormann,
C. Bär,
A. Wienke and
J. Borggrefe,
"Validation of clinical-radiological scores for prognosis of mortality in acute pulmonary embolism.",
Respiratory research,
Aug.
2023.
Abstract:
Akute Lungenembolie (APE) ist eine gefährliche Erkrankung mit hoher Sterblichkeit. Die Kombination von klinischen, radiologischen und serologischen Parametern kann die Risikoschichtung von APE verbessern. Die meisten der vorgeschlagenen kombinierten Punkte wurden in unabhängigen Kohorten nicht validiert. Unser Ziel war es, die vorgeschlagenen klinisch-radiologischen Ergebnisse zur Prognose von 7- und 30-tägiger Sterblichkeit in APE zu validieren. Unsere Probe umfasste 531 Patienten mit APE, mittleres Alter 64,8 ± 15,6 Jahre, 221 (41.6%) Frauen und 310 (58,4%) Männer. Die folgenden Parameter wurden gesammelt: Alter und Geschlecht der Patienten, Mortalität innerhalb der Beobachtungszeit von 30 Tagen, vereinfachter Lungenprägegrad (sPESI), pH-Poponinspiegel (pg/ml), minimaler systolischer und diastolischer Blutdruck (mmHg), Herzfrequenz, O<sub>2>/sub> Sättigung, Synkopsequenzen und Notwendigkeit für Vasokpressoren. Bei CT-Pulmonalangiographie (CTPA) wurden kurzes Achsverhältnis rechts Ventrikel/linkes Ventrikel (RV/LV) und Rückfluss von Kontrastmittel in die minderwertige Venenkava erhalten. Die folgenden klinisch-radiologischen Punkte wurden berechnet: BOVA-Score, Lungenembolismus-Mortalitäts-Score (PEMS), European Society of Cardiology (ESC)-Score, Kumamaru-Score und Calgary akuter Lungenembolismus (CAPE). Insgesamt starben 31 Patienten (5,8%) innerhalb von sieben und 64 Patienten (12%) innerhalb von 30 Tagen. Alle Punkte zeigten hohe negative Prognosewerte von 89,0 bis 99,0%. PEMS und CAPE haben die höchste Spezifität für die 7-Tage-Sterblichkeit gezeigt (93,4% und 85,0%), PEMS und BOVA für 30-Tage-Sterblichkeit (94,2% und 90,4%). Die höchste Empfindlichkeit wurde für das ESC 2019 (96,8% und 95,3%) beobachtet. Kumamaru und CAPE erzielten eine geringe Empfindlichkeit. Alle Punkte hatten niedrige positive und hohe negative Vorhersagewerte. Zur Prognose der 7- und 30-tägigen Sterblichkeit in APE hat PEMS die höchste Spezifität. ESC 2019 zeigt die höchste Empfindlichkeit. Alle Punkte hatten niedrige positive und hohe negative Vorhersagewerte.
| DOI: | 10.1186/s12931-023-02489-0 |
Abstract:
Akute Lungenembolie (APE) ist eine gefährliche Erkrankung mit hoher Sterblichkeit. Die Kombination von klinischen, radiologischen und serologischen Parametern kann die Risikoschichtung von APE verbessern. Die meisten der vorgeschlagenen kombinierten Punkte wurden in unabhängigen Kohorten nicht validiert. Unser Ziel war es, die vorgeschlagenen klinisch-radiologischen Ergebnisse zur Prognose von 7- und 30-tägiger Sterblichkeit in APE zu validieren. Unsere Probe umfasste 531 Patienten mit APE, mittleres Alter 64,8 ± 15,6 Jahre, 221 (41.6%) Frauen und 310 (58,4%) Männer. Die folgenden Parameter wurden gesammelt: Alter und Geschlecht der Patienten, Mortalität innerhalb der Beobachtungszeit von 30 Tagen, vereinfachter Lungenprägegrad (sPESI), pH-Poponinspiegel (pg/ml), minimaler systolischer und diastolischer Blutdruck (mmHg), Herzfrequenz, O<sub>2>/sub> Sättigung, Synkopsequenzen und Notwendigkeit für Vasokpressoren. Bei CT-Pulmonalangiographie (CTPA) wurden kurzes Achsverhältnis rechts Ventrikel/linkes Ventrikel (RV/LV) und Rückfluss von Kontrastmittel in die minderwertige Venenkava erhalten. Die folgenden klinisch-radiologischen Punkte wurden berechnet: BOVA-Score, Lungenembolismus-Mortalitäts-Score (PEMS), European Society of Cardiology (ESC)-Score, Kumamaru-Score und Calgary akuter Lungenembolismus (CAPE). Insgesamt starben 31 Patienten (5,8%) innerhalb von sieben und 64 Patienten (12%) innerhalb von 30 Tagen. Alle Punkte zeigten hohe negative Prognosewerte von 89,0 bis 99,0%. PEMS und CAPE haben die höchste Spezifität für die 7-Tage-Sterblichkeit gezeigt (93,4% und 85,0%), PEMS und BOVA für 30-Tage-Sterblichkeit (94,2% und 90,4%). Die höchste Empfindlichkeit wurde für das ESC 2019 (96,8% und 95,3%) beobachtet. Kumamaru und CAPE erzielten eine geringe Empfindlichkeit. Alle Punkte hatten niedrige positive und hohe negative Vorhersagewerte. Zur Prognose der 7- und 30-tägigen Sterblichkeit in APE hat PEMS die höchste Spezifität. ESC 2019 zeigt die höchste Empfindlichkeit. Alle Punkte hatten niedrige positive und hohe negative Vorhersagewerte.
F. Steinbeis,
C. Thibeault,
S. Steinbrecher,
I. A. Haack,
D. August,
B. Balzuweit,
C. Bellinghausen,
S. Berger,
I. Chaplinskaya-Sobol,
O. Cornely,
P. Doeblin,
M. Endres,
C. Fink,
C. Finke,
S. Frank,
S. Hanss,
T. J. Hartung,
J. C. Hellmuth,
S. Herold,
P. Heuschmann,
J. Heyckendorf,
R. Heyder,
S. Hippenstiel,
W. Hoffmann,
S. U. Kelle,
P. Knape,
P. Koehler,
L. Kretzler,
D. M. Leistner,
J. Lienau,
R. Lorbeer,
B. Lorenz-Depiereux,
C. D. Lüttke,
K. Mai,
U. Merle,
L. Meyer-Arndt,
O. Miljukov,
M. Muenchhoff,
M. Müller-Plathe,
J. Neuhann,
H. Neuhauser,
A. Nieters,
C. Otte,
D. Pape,
R. Pinto,
C. Pley,
A. Pudszuhn,
P. Reuken,
S. Rieg,
P. Ritter,
G. Rohde,
M. Rönnefarth,
M. Ruzicka,
J. Schaller,
A. Schmidt,
S. Schmidt,
V. Schwachmeyer,
G. Schwanitz,
W. Seeger,
D. Stahl,
N. Stobäus,
H. C. Stubbe,
N. Suttorp,
B. Temmesfeld,
S. Thun,
P. Triller,
F. Trinkmann,
I. Vadasz,
H. Valentin,
M. J. G. T. Vehreschild,
C. Kalle,
M. Lilienfeld-Toal,
J. Weber,
T. Welte,
C. Wildberg,
R. Wizimirski,
S. Zvork,
L. E. Sander,
J. Vehreschild,
T. Zoller,
F. Kurth and
M. Witzenrath,
"Analysis of acute COVID-19 including chronic morbidity: protocol for the deep phenotyping National Pandemic Cohort Network in Germany (NAPKON-HAP). Infection",
Jul.
2023.
| DOI: | 10.1007/s15010-023-02057-0 |
F. Haag,
M. Pech and
A. Surov,
"[Chronic-intermittent diffuse alveolar hemorrhage of unknown origin]",
RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin,
vol. 195,
no. 7,
pp. 620-622,
Jul.
2023.
Abstract:
N/A
| DOI: | 10.1055/a-2018-3464 |
| Datei: | https://pubmed.ncbi.nlm.nih.gov/36863372/ |
Abstract:
N/A
[en]
F. Steinbeis,
C. Thibeault,
S. Steinbrecher,
I. A. Haack,
D. August,
B. Balzuweit,
C. Bellinghausen,
S. Berger,
I. Chaplinskaya-Sobol,
O. Cornely,
P. Doeblin,
M. Endres,
C. Fink,
C. Finke,
S. Frank,
S. Hanß,
T. Hartung,
J. C. Hellmuth,
S. Herold,
P. Heuschmann,
J. Heyckendorf,
R. Heyder,
S. Hippenstiel,
W. Hoffmann,
S. U. Kelle,
P. Knape,
P. Koehler,
L. Kretzler,
D. M. Leistner,
J. Lienau,
R. Lorbeer,
B. Lorenz-Depiereux,
C. D. Lüttke,
K. Mai,
U. Merle,
L. A. Meyer-Arndt,
O. Miljukov,
M. Muenchhoff,
M. Müller-Plathe,
J. Neuhann,
H. Neuhauser,
A. Nieters,
C. Otte,
D. Pape,
R. Pinto,
C. Pley,
A. Pudszuhn,
P. Reuken,
S. Rieg,
P. Ritter,
G. Rohde,
M. Rönnefarth,
M. Ruzicka,
J. Schaller,
A. Schmidt,
S. Schmidt,
V. Schwachmeyer,
G. Schwanitz,
W. Seeger,
D. Stahl,
N. Stobäus,
H. C. Stubbe,
N. Suttorp,
B. Temmesfeld,
S. Thun,
P. Triller,
F. Trinkmann,
I. Vadasz,
H. Valentin,
M. Vehreschild,
C. Kalle,
M. Lilienfeld-Toal,
J. Weber,
T. Welte,
C. Wildberg,
R. Wizimirski,
S. Zvork,
L. E. Sander,
J. Vehreschild,
T. Zoller,
F. Kurth and
M. Witzenrath,
"Analysis of acute COVID-19 including chronic morbidity:
protocol for the deep phenotyping National Pandemic Cohort
Network in Germany (NAPKON-HAP)",
Infection,
vol. 52,
no. 1,
pp. 93-104,
Jul.
2023.
Abstract:
BACKGROUND: The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) pandemic causes a high burden of acute and long-term morbidity and mortality worldwide despite global efforts in containment, prophylaxis, and therapy. With unprecedented speed, the global scientific community has generated pivotal insights into the pathogen and the host response evoked by the infection. However, deeper characterization of the pathophysiology and pathology remains a high priority to reduce morbidity and mortality of coronavirus disease 2019 (COVID-19). METHODS: NAPKON-HAP is a multi-centered prospective observational study with a long-term follow-up phase of up to 36 months post-SARS-CoV-2 infection. It constitutes a central platform for harmonized data and biospecimen for interdisciplinary characterization of acute SARS-CoV-2 infection and long-term outcomes of diverging disease severities of hospitalized patients. RESULTS: Primary outcome measures include clinical scores and quality of life assessment captured during hospitalization and at outpatient follow-up visits to assess acute and chronic morbidity. Secondary measures include results of biomolecular and immunological investigations and assessment of organ-specific involvement during and post-COVID-19 infection. NAPKON-HAP constitutes a national platform to provide accessibility and usability of the comprehensive data and biospecimen collection to global research. CONCLUSION: NAPKON-HAP establishes a platform with standardized high-resolution data and biospecimen collection of hospitalized COVID-19 patients of different disease severities in Germany. With this study, we will add significant scientific insights and provide high-quality data to aid researchers to investigate COVID-19 pathophysiology, pathology, and chronic morbidity.
| DOI: | 10.1007/s15010-023-02057-0 |
| Pubmed: | 37434025 |
Abstract:
BACKGROUND: The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) pandemic causes a high burden of acute and long-term morbidity and mortality worldwide despite global efforts in containment, prophylaxis, and therapy. With unprecedented speed, the global scientific community has generated pivotal insights into the pathogen and the host response evoked by the infection. However, deeper characterization of the pathophysiology and pathology remains a high priority to reduce morbidity and mortality of coronavirus disease 2019 (COVID-19). METHODS: NAPKON-HAP is a multi-centered prospective observational study with a long-term follow-up phase of up to 36 months post-SARS-CoV-2 infection. It constitutes a central platform for harmonized data and biospecimen for interdisciplinary characterization of acute SARS-CoV-2 infection and long-term outcomes of diverging disease severities of hospitalized patients. RESULTS: Primary outcome measures include clinical scores and quality of life assessment captured during hospitalization and at outpatient follow-up visits to assess acute and chronic morbidity. Secondary measures include results of biomolecular and immunological investigations and assessment of organ-specific involvement during and post-COVID-19 infection. NAPKON-HAP constitutes a national platform to provide accessibility and usability of the comprehensive data and biospecimen collection to global research. CONCLUSION: NAPKON-HAP establishes a platform with standardized high-resolution data and biospecimen collection of hospitalized COVID-19 patients of different disease severities in Germany. With this study, we will add significant scientific insights and provide high-quality data to aid researchers to investigate COVID-19 pathophysiology, pathology, and chronic morbidity.
[en]
E. Pappe,
R. Hammerich,
J. Saccomanno,
T. Sgarbossa,
A. Pohrt,
B. Schmidt,
C. Grah,
S. Eisenmann,
A. Holland,
S. Eggeling,
F. Stanzel,
M. Witzenrath and
R. Hübner,
"Impact of Coronavirus disease 2019 on hospital admissions,
health status, and behavioral changes of patients with COPD",
Chronic Obstr. Pulm. Dis.,
vol. 10,
no. 3,
pp. 211—223,
Jul.
2023.
COPD Foundation.
Abstract:
Introduction: Patients with chronic obstructive pulmonary disease (COPD) have an increased risk of acquiring severe coronavirus disease 2019 (COVID-19), which is why self-isolation was recommended. However, long periods of social isolation, accompanied by limited access to health care systems, might influence the outcome of patients with severe COPD negatively. Methods: Data from COPD and pneumonia patients at Charité-Universit"atsmedizin Berlin and the volume of endoscopic lung volume reduction (ELVR) surgeries from the German Lung Emphysema Registry (Lungenemphysem Register e.V.) were analyzed from pre-pandemic (2012 to 2019) to the pandemic period (2020 and 2021). In addition, 52 patients with COPD Global initiative for chronic Obstructive Lung Disease (GOLD) stage 4 status included in the lung emphysema registry received questionnaires during lockdowns from June 2020 to April 2021. Results: Admissions and ventilation therapies administered to COPD patients significantly decreased during the COVID-19 pandemic. Likewise, there was a reduction in ELVR treatments and follow-ups registered in German emphysema centers. Mortality was slightly higher among patients hospitalized with COPD during the pandemic. Increasing proportions of COPD patients with GOLD stage 3 and GOLD stage 4 status reported behavioral changes and subjective feelings of increasing COPD symptoms the longer the lockdown lasted. However, COPD symptom questionnaires revealed stable COPD symptoms over the pandemic time period. Summary: This study reveals reduced COPD admissions and elective treatment procedures of COPD patients during the pandemic, but a slight increase in mortality among patients hospitalized with COPD, irrespective of COVID-19. Correspondingly, patients with severe COPD reported subjective deterioration of their health status, probably caused by their very strict compliance with lockdown measures.
| DOI: | 10.15326/jcopdf.2022.0383 |
Abstract:
Introduction: Patients with chronic obstructive pulmonary disease (COPD) have an increased risk of acquiring severe coronavirus disease 2019 (COVID-19), which is why self-isolation was recommended. However, long periods of social isolation, accompanied by limited access to health care systems, might influence the outcome of patients with severe COPD negatively. Methods: Data from COPD and pneumonia patients at Charité-Universit"atsmedizin Berlin and the volume of endoscopic lung volume reduction (ELVR) surgeries from the German Lung Emphysema Registry (Lungenemphysem Register e.V.) were analyzed from pre-pandemic (2012 to 2019) to the pandemic period (2020 and 2021). In addition, 52 patients with COPD Global initiative for chronic Obstructive Lung Disease (GOLD) stage 4 status included in the lung emphysema registry received questionnaires during lockdowns from June 2020 to April 2021. Results: Admissions and ventilation therapies administered to COPD patients significantly decreased during the COVID-19 pandemic. Likewise, there was a reduction in ELVR treatments and follow-ups registered in German emphysema centers. Mortality was slightly higher among patients hospitalized with COPD during the pandemic. Increasing proportions of COPD patients with GOLD stage 3 and GOLD stage 4 status reported behavioral changes and subjective feelings of increasing COPD symptoms the longer the lockdown lasted. However, COPD symptom questionnaires revealed stable COPD symptoms over the pandemic time period. Summary: This study reveals reduced COPD admissions and elective treatment procedures of COPD patients during the pandemic, but a slight increase in mortality among patients hospitalized with COPD, irrespective of COVID-19. Correspondingly, patients with severe COPD reported subjective deterioration of their health status, probably caused by their very strict compliance with lockdown measures.
J. Kolck,
Z. A. Rako,
N. L. Beetz,
T. A. Auer,
L. K. Segger,
C. Pille,
T. Penzkofer,
U. Fehrenbach and
D. Geisel,
"Intermittent body composition analysis as monitoring tool for muscle wasting in critically ill COVID-19 patients.",
Annals of intensive care,
Jul.
2023.
Abstract:
SARS-CoV-2 Virusinfektion kann zu akutem Atemnotsyndrom (ARDS) führen, das durch schwere Muskelverschwendung kompliziert sein kann. Bisher sind Daten über den Muskelverlust von kritisch kranken COVID-19-Patienten begrenzt, während berechnete Tomographie (CT)-Scans für klinische Nachfolge verfügbar sind. Wir wollten die Parameter der Muskelverschwendung bei diesen Patienten untersuchen, indem wir als erstes die klinische Anwendung der Körperzusammensetzungsanalyse (BCA) als intermittierendes Monitoring-Tool testen. BCA wurde an 54 Patienten durchgeführt, mit mindestens drei Messungen während der Hospitalisierung, insgesamt 239 Bewertungen. Veränderungen der psoas- (PMA) und der gesamten Bauchmuskelfläche (TAMA) wurden durch lineare Mischmodellanalyse bewertet. PMA wurde als relativer Muskelverlust pro Tag für die gesamte Überwachungsperiode sowie für das Intervall zwischen jedem aufeinanderfolgenden Scan berechnet. Cox-Regression wurde angewendet, um Assoziationen mit Überleben zu analysieren. Die Receiver-Betriebskennlinie (ROC)-Analyse und Youden-Index wurden verwendet, um einen Abkling-Ausschnitt zu definieren. Intermittent BCA ergab deutlich höhere langfristige PMA-Verlustraten von 2,62% (vs. 1.16%, p < 0.001) und maximale Muskelverfall von 5.48% (vs. 3.66%, p = 0.039) pro Tag in Nicht-Überlebenden. Die erste verfügbare Abklingrate unterscheidet sich nicht wesentlich zwischen Überlebensgruppen, zeigte jedoch signifikante Überlebensvereinigungen in der Cox-Regression (p = 0,011). In der ROC-Analyse hatte PMA-Verlust im Durchschnitt über den Aufenthalt die größte diskriminierende Macht (AUC = 0,777) für das Überleben. Ein langfristiger PMA-Abfall pro Tag von 1,84% wurde als Schwelle definiert; Muskelverlust jenseits dieser Absenkung erwies sich als ein signifikanter BCA-abgeleiteter Prädiktor der Sterblichkeit. Muskelverschwendung bei kritisch kranken COVID-19-Patienten ist schwer und korreliert mit Überleben. Intermittierende BCA aus klinisch angezeigten CT-Scans erwies sich als wertvolles Monitoring-Tool, das die Identifizierung von Personen mit Risiko für negative Ergebnisse ermöglicht und großes Potenzial hat, kritische Pflegeentscheidungen zu unterstützen.
| DOI: | 10.1186/s13613-023-01162-5 |
Abstract:
SARS-CoV-2 Virusinfektion kann zu akutem Atemnotsyndrom (ARDS) führen, das durch schwere Muskelverschwendung kompliziert sein kann. Bisher sind Daten über den Muskelverlust von kritisch kranken COVID-19-Patienten begrenzt, während berechnete Tomographie (CT)-Scans für klinische Nachfolge verfügbar sind. Wir wollten die Parameter der Muskelverschwendung bei diesen Patienten untersuchen, indem wir als erstes die klinische Anwendung der Körperzusammensetzungsanalyse (BCA) als intermittierendes Monitoring-Tool testen. BCA wurde an 54 Patienten durchgeführt, mit mindestens drei Messungen während der Hospitalisierung, insgesamt 239 Bewertungen. Veränderungen der psoas- (PMA) und der gesamten Bauchmuskelfläche (TAMA) wurden durch lineare Mischmodellanalyse bewertet. PMA wurde als relativer Muskelverlust pro Tag für die gesamte Überwachungsperiode sowie für das Intervall zwischen jedem aufeinanderfolgenden Scan berechnet. Cox-Regression wurde angewendet, um Assoziationen mit Überleben zu analysieren. Die Receiver-Betriebskennlinie (ROC)-Analyse und Youden-Index wurden verwendet, um einen Abkling-Ausschnitt zu definieren. Intermittent BCA ergab deutlich höhere langfristige PMA-Verlustraten von 2,62% (vs. 1.16%, p < 0.001) und maximale Muskelverfall von 5.48% (vs. 3.66%, p = 0.039) pro Tag in Nicht-Überlebenden. Die erste verfügbare Abklingrate unterscheidet sich nicht wesentlich zwischen Überlebensgruppen, zeigte jedoch signifikante Überlebensvereinigungen in der Cox-Regression (p = 0,011). In der ROC-Analyse hatte PMA-Verlust im Durchschnitt über den Aufenthalt die größte diskriminierende Macht (AUC = 0,777) für das Überleben. Ein langfristiger PMA-Abfall pro Tag von 1,84% wurde als Schwelle definiert; Muskelverlust jenseits dieser Absenkung erwies sich als ein signifikanter BCA-abgeleiteter Prädiktor der Sterblichkeit. Muskelverschwendung bei kritisch kranken COVID-19-Patienten ist schwer und korreliert mit Überleben. Intermittierende BCA aus klinisch angezeigten CT-Scans erwies sich als wertvolles Monitoring-Tool, das die Identifizierung von Personen mit Risiko für negative Ergebnisse ermöglicht und großes Potenzial hat, kritische Pflegeentscheidungen zu unterstützen.
[en]
G. Lichtner,
T. Haese,
S. Brose,
L. Röhrig,
L. Lysyakova,
S. Rudolph,
M. Uebe,
J. Sass,
A. Bartschke,
D. Hillus,
F. Kurth,
L. E. Sander,
F. Eckart,
N. Toepfner,
R. Berner,
A. Frey,
M. Dörr,
J. J. Vehreschild,
C. Kalle and
S. Thun,
"Interoperable, domain-specific extensions for the German Corona Consensus (GECCO) COVID-19 research data set using an interdisciplinary, consensus-based workflow: Data set development study",
JMIR Med. Inform.,
vol. 11,
pp. e45496,
Jul.
2023.
Abstract:
Background: The COVID-19 pandemic has spurred large-scale, interinstitutional research efforts. To enable these efforts, researchers must agree on data set definitions that not only cover all elements relevant to the respective medical specialty but also are syntactically and semantically interoperable. Therefore, the German Corona Consensus (GECCO) data set was developed as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As the GECCO data set is a compact core data set comprising data across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include data elements that are the most relevant to the research performed in those individual medical specialties. Objective: We aimed to (1) specify a workflow for the development of interoperable data set definitions that involves close collaboration between medical experts and information scientists and (2) apply the workflow to develop data set definitions that include data elements that are the most relevant to COVID-19-related patient research regarding immunization, pediatrics, and cardiology. Methods: We developed a workflow to create data set definitions that were (1) content-wise as relevant as possible to a specific field of study and (2) universally usable across computer systems, institutions, and countries (ie, interoperable). We then gathered medical experts from 3 specialties-infectious diseases (with a focus on immunization), pediatrics, and cardiology-to select data elements that were the most relevant to COVID-19-related patient research in the respective specialty. We mapped the data elements to international standardized vocabularies and created data exchange specifications, using Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR). All steps were performed in close interdisciplinary collaboration with medical domain experts and medical information specialists. Profiles and vocabulary mappings were syntactically and semantically validated in a 2-stage process. Results: We created GECCO extension modules for the immunization, pediatrics, and cardiology domains according to pandemic-related requests. The data elements included in each module were selected, according to the developed consensus-based workflow, by medical experts from these specialties to ensure that the contents aligned with their research needs. We defined data set specifications for 48 immunization, 150 pediatrics, and 52 cardiology data elements that complement the GECCO core data set. We created and published implementation guides, example implementations, and data set annotations for each extension module. Conclusions: The GECCO extension modules, which contain data elements that are the most relevant to COVID-19-related patient research on infectious diseases (with a focus on immunization), pediatrics, and cardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for developing further data set definitions. The GECCO extension modules provide standardized and harmonized definitions of specialty-related data sets that can help enable interinstitutional and cross-country COVID-19 research in these specialties.
| DOI: | 10.2196/45496 |
| Pubmed: | 37490312 |
Abstract:
Background: The COVID-19 pandemic has spurred large-scale, interinstitutional research efforts. To enable these efforts, researchers must agree on data set definitions that not only cover all elements relevant to the respective medical specialty but also are syntactically and semantically interoperable. Therefore, the German Corona Consensus (GECCO) data set was developed as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As the GECCO data set is a compact core data set comprising data across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include data elements that are the most relevant to the research performed in those individual medical specialties. Objective: We aimed to (1) specify a workflow for the development of interoperable data set definitions that involves close collaboration between medical experts and information scientists and (2) apply the workflow to develop data set definitions that include data elements that are the most relevant to COVID-19-related patient research regarding immunization, pediatrics, and cardiology. Methods: We developed a workflow to create data set definitions that were (1) content-wise as relevant as possible to a specific field of study and (2) universally usable across computer systems, institutions, and countries (ie, interoperable). We then gathered medical experts from 3 specialties-infectious diseases (with a focus on immunization), pediatrics, and cardiology-to select data elements that were the most relevant to COVID-19-related patient research in the respective specialty. We mapped the data elements to international standardized vocabularies and created data exchange specifications, using Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR). All steps were performed in close interdisciplinary collaboration with medical domain experts and medical information specialists. Profiles and vocabulary mappings were syntactically and semantically validated in a 2-stage process. Results: We created GECCO extension modules for the immunization, pediatrics, and cardiology domains according to pandemic-related requests. The data elements included in each module were selected, according to the developed consensus-based workflow, by medical experts from these specialties to ensure that the contents aligned with their research needs. We defined data set specifications for 48 immunization, 150 pediatrics, and 52 cardiology data elements that complement the GECCO core data set. We created and published implementation guides, example implementations, and data set annotations for each extension module. Conclusions: The GECCO extension modules, which contain data elements that are the most relevant to COVID-19-related patient research on infectious diseases (with a focus on immunization), pediatrics, and cardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for developing further data set definitions. The GECCO extension modules provide standardized and harmonized definitions of specialty-related data sets that can help enable interinstitutional and cross-country COVID-19 research in these specialties.
F. Khader,
J. N. Kather,
G. Müller-Franzes,
T. Wang,
T. Han,
S. Tayebi Arasteh,
K. Hamesch,
K. Bressem,
C. Haarburger,
J. Stegmaier,
C. Kuhl,
S. Nebelung and
D. Truhn,
"Medical transformer for multimodal survival prediction in intensive care: integration of imaging and non-imaging data.",
Scientific reports,
Jul.
2023.
Abstract:
Bei der Beurteilung der Prognose von Patienten in der Intensivpflege berücksichtigen sie bildgebende und nicht bildgebende Daten. Im Gegensatz dazu verlassen sich viele traditionelle maschinelle Lernmodelle nur auf eine dieser Modalitäten und begrenzen ihr Potenzial in medizinischen Anwendungen. Diese Arbeit schlägt vor und wertet ein transformatorbasiertes neuronales Netzwerk als neuartige AI-Architektur aus, die multimodale Patientendaten, d.h. Abbildungsdaten (chest radiographs) und nicht-imaging-Daten (klinische Daten) integriert. Wir bewerten die Leistung unseres Modells in einer retrospektiven Studie mit 6,125 Patienten in intensiver Pflege. Wir zeigen, dass das kombinierte Modell (Fläche unter der Empfänger-Betriebskennlinie [AUROC] von 0,863) dem Radiographen-only-Modell (AUROC = 0,811, p < 0,001) und dem klinischen Daten-only-Modell (AUROC = 0,785, p < 0,001) überlegen ist, wenn es mit der Vorhersage von In-hospital-Überleben pro Patient betraut wird. Darüber hinaus zeigen wir, dass unser vorgeschlagenes Modell robust ist, wenn nicht alle (klinischen) Datenpunkte zur Verfügung stehen.
| DOI: | 10.1038/s41598-023-37835-1 |
Abstract:
Bei der Beurteilung der Prognose von Patienten in der Intensivpflege berücksichtigen sie bildgebende und nicht bildgebende Daten. Im Gegensatz dazu verlassen sich viele traditionelle maschinelle Lernmodelle nur auf eine dieser Modalitäten und begrenzen ihr Potenzial in medizinischen Anwendungen. Diese Arbeit schlägt vor und wertet ein transformatorbasiertes neuronales Netzwerk als neuartige AI-Architektur aus, die multimodale Patientendaten, d.h. Abbildungsdaten (chest radiographs) und nicht-imaging-Daten (klinische Daten) integriert. Wir bewerten die Leistung unseres Modells in einer retrospektiven Studie mit 6,125 Patienten in intensiver Pflege. Wir zeigen, dass das kombinierte Modell (Fläche unter der Empfänger-Betriebskennlinie [AUROC] von 0,863) dem Radiographen-only-Modell (AUROC = 0,811, p < 0,001) und dem klinischen Daten-only-Modell (AUROC = 0,785, p < 0,001) überlegen ist, wenn es mit der Vorhersage von In-hospital-Überleben pro Patient betraut wird. Darüber hinaus zeigen wir, dass unser vorgeschlagenes Modell robust ist, wenn nicht alle (klinischen) Datenpunkte zur Verfügung stehen.
L. Schweizer,
T. Schaller,
M. Zwiebel,
Ö. Karayel,
J. B. Müller‐Reif,
W. Zeng,
S. Dintner,
T. M. Nordmann,
K. Hirschbühl,
B. Märkl,
R. Claus and
M. Mann,
"Quantitative multiorgan proteomics of fatal COVID‐19 uncovers tissue‐specific effects beyond inflammation",
EMBO Molecular Medicine,
vol. 15,
no. 9,
pp. e17459,
Jul.
2023.
Abstract:
SARS‐CoV‐2 may directly and indirectly damage lung tissue and other host organs, but there are few system‐wide, untargeted studies of these effects on the human body. Here, we developed a parallelized mass spectrometry (MS) proteomics workflow enabling the rapid, quantitative analysis of hundreds of virus‐infected FFPE tissues. The first layer of response to SARS‐CoV‐2 in all tissues was dominated by circulating inflammatory molecules. Beyond systemic inflammation, we differentiated between systemic and true tissue‐specific effects to reflect distinct COVID‐19‐associated damage patterns. Proteomic changes in the lungs resembled those of diffuse alveolar damage (DAD) in non‐COVID‐19 patients. Extensive organ‐specific changes were also evident in the kidneys, liver, and lymphatic and vascular systems. Secondary inflammatory effects in the brain were related to rearrangements in neurotransmitter receptors and myelin degradation. These MS‐proteomics‐derived results contribute substantially to our understanding of COVID‐19 pathomechanisms and suggest strategies for organ‐specific therapeutic interventions., This study reports a proteomic investigation of fatal COVID‐19 across organs using mass spectrometry, highlighting the central role of circulating inflammatory molecules and uncovering tissue‐specific alterations beyond inflammation.
| DOI: | 10.15252/emmm.202317459 |
| Datei: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10493576/ |
Abstract:
SARS‐CoV‐2 may directly and indirectly damage lung tissue and other host organs, but there are few system‐wide, untargeted studies of these effects on the human body. Here, we developed a parallelized mass spectrometry (MS) proteomics workflow enabling the rapid, quantitative analysis of hundreds of virus‐infected FFPE tissues. The first layer of response to SARS‐CoV‐2 in all tissues was dominated by circulating inflammatory molecules. Beyond systemic inflammation, we differentiated between systemic and true tissue‐specific effects to reflect distinct COVID‐19‐associated damage patterns. Proteomic changes in the lungs resembled those of diffuse alveolar damage (DAD) in non‐COVID‐19 patients. Extensive organ‐specific changes were also evident in the kidneys, liver, and lymphatic and vascular systems. Secondary inflammatory effects in the brain were related to rearrangements in neurotransmitter receptors and myelin degradation. These MS‐proteomics‐derived results contribute substantially to our understanding of COVID‐19 pathomechanisms and suggest strategies for organ‐specific therapeutic interventions., This study reports a proteomic investigation of fatal COVID‐19 across organs using mass spectrometry, highlighting the central role of circulating inflammatory molecules and uncovering tissue‐specific alterations beyond inflammation.
J. M. M. Rogasch,
L. Michaels,
G. L. Baumgärtner,
N. Frost,
J. Rückert,
J. Neudecker,
S. Ochsenreither,
M. Gerhold,
B. Schmidt,
P. Schneider,
H. Amthauer,
C. Furth and
T. Penzkofer,
"A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [18F]FDG-PET/CT parameters",
European journal of nuclear medicine and molecular imaging,
vol. 50,
no. 7,
pp. 2140-2151,
Jun.
2023.
Abstract:
In patients with non-small cell lung cancer (NSCLC), accuracy of [18F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image assessment. Monocentric retrospective analysis of pretherapeutic [18F]FDG-PET/CT in 491 consecutive patients with NSCLC using an analog PET/CT scanner (training + test cohort, n = 385) or digital scanner (validation, n = 106). Forty clinical variables, tumor characteristics, and image variables (e.g., primary tumor and LN SUVmax and size) were collected. Different combinations of machine learning methods for feature selection and classification of N0/1 vs. N2/3 disease were compared. Ten-fold nested cross-validation was used to derive the mean area under the ROC curve of the ten test folds ("test AUC") and AUC in the validation cohort. Reference standard was the final N stage from interdisciplinary consensus (histological results for N2/3 LNs in 96%). N2/3 disease was present in 190 patients (39%; training + test, 37%; validation, 46%; p = 0.09). A gradient boosting classifier (GBM) with 10 features was selected as the final model based on test AUC of 0.91 (95% confidence interval, 0.87-0.94). Validation AUC was 0.94 (0.89-0.98). At a target sensitivity of approx. 90%, test/validation accuracy of the GBM was 0.78/0.87. This was significantly higher than the accuracy based on "mediastinal LN uptake > mediastinum" (0.7/0.75; each p < 0.05) or combined PET/CT criteria (PET positive and/or LN short axis diameter > 10 mm; 0.68/0.75; each p < 0.001). Harmonization of PET images between the two scanners affected SUVmax and visual assessment of the LNs but did not diminish the AUC of the GBM. A machine learning model based on routinely available variables from [18F]FDG-PET/CT improved accuracy in mediastinal LN staging compared to established visual assessment criteria. A web application implementing this model was made available.
| DOI: | 10.1007/s00259-023-06145-z |
| Datei: | https://pubmed.ncbi.nlm.nih.gov/36820890/ |
Abstract:
In patients with non-small cell lung cancer (NSCLC), accuracy of [18F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image assessment. Monocentric retrospective analysis of pretherapeutic [18F]FDG-PET/CT in 491 consecutive patients with NSCLC using an analog PET/CT scanner (training + test cohort, n = 385) or digital scanner (validation, n = 106). Forty clinical variables, tumor characteristics, and image variables (e.g., primary tumor and LN SUVmax and size) were collected. Different combinations of machine learning methods for feature selection and classification of N0/1 vs. N2/3 disease were compared. Ten-fold nested cross-validation was used to derive the mean area under the ROC curve of the ten test folds ("test AUC") and AUC in the validation cohort. Reference standard was the final N stage from interdisciplinary consensus (histological results for N2/3 LNs in 96%). N2/3 disease was present in 190 patients (39%; training + test, 37%; validation, 46%; p = 0.09). A gradient boosting classifier (GBM) with 10 features was selected as the final model based on test AUC of 0.91 (95% confidence interval, 0.87-0.94). Validation AUC was 0.94 (0.89-0.98). At a target sensitivity of approx. 90%, test/validation accuracy of the GBM was 0.78/0.87. This was significantly higher than the accuracy based on "mediastinal LN uptake > mediastinum" (0.7/0.75; each p < 0.05) or combined PET/CT criteria (PET positive and/or LN short axis diameter > 10 mm; 0.68/0.75; each p < 0.001). Harmonization of PET images between the two scanners affected SUVmax and visual assessment of the LNs but did not diminish the AUC of the GBM. A machine learning model based on routinely available variables from [18F]FDG-PET/CT improved accuracy in mediastinal LN staging compared to established visual assessment criteria. A web application implementing this model was made available.
M. Krämer,
M. Ingwersen,
U. Teichgräber and
F. Güttler,
"Added value of chest CT in a machine learning-based prediction model to rule out COVID-19 before inpatient admission: A retrospective university network study",
European journal of radiology,
vol. 163,
pp. 110827,
Jun.
2023.
Abstract:
During the coronavirus disease 2019 (COVID-19) pandemic, hospitals still face the challenge of timely identification of infected individuals before inpatient admission. An artificial intelligence approach based on an established clinical network may improve prospective pandemic preparedness. Supervised machine learning was used to construct diagnostic models to predict COVID-19. A pooled database was retrospectively generated from 4437 participant data that were collected between January 2017 and October 2020 at 12 German centers that belong to the radiological cooperative network of the COVID-19 (RACOON) consortium. A total of 692 (15.6 %) participants were COVID-19 positive according to the reference of the reverse transcription-polymerase chain reaction test. The diagnostic models included chest CT features (model R), clinical examination and laboratory test features (model CL), or all three feature categories (model RCL). Performance outcomes included accuracy, sensitivity, specificity, negative and positive predictive value, and area under the receiver operating curve (AUC). Performance of predictive models improved significantly by adding chest CT features to clinical evaluation and laboratory test features. Without (model CL) and with inclusion of chest CT (model RCL), sensitivity was 0.82 and 0.89 (p < 0.0001), specificity was 0.84 and 0.89 (p < 0.0001), negative predictive value was 0.96 and 0.97 (p < 0.0001), AUC was 0.92 and 0.95 (p < 0.0001), and proportion of false negative classifications was 2.6 % and 1.7 % (p < 0.0001), respectively. Addition of chest CT features to machine learning-based predictive models improves the effectiveness in ruling out COVID-19 before inpatient admission to regular wards.
| DOI: | 10.1016/j.ejrad.2023.110827 |
| Datei: | https://pubmed.ncbi.nlm.nih.gov/37087928/ |
Abstract:
During the coronavirus disease 2019 (COVID-19) pandemic, hospitals still face the challenge of timely identification of infected individuals before inpatient admission. An artificial intelligence approach based on an established clinical network may improve prospective pandemic preparedness. Supervised machine learning was used to construct diagnostic models to predict COVID-19. A pooled database was retrospectively generated from 4437 participant data that were collected between January 2017 and October 2020 at 12 German centers that belong to the radiological cooperative network of the COVID-19 (RACOON) consortium. A total of 692 (15.6 %) participants were COVID-19 positive according to the reference of the reverse transcription-polymerase chain reaction test. The diagnostic models included chest CT features (model R), clinical examination and laboratory test features (model CL), or all three feature categories (model RCL). Performance outcomes included accuracy, sensitivity, specificity, negative and positive predictive value, and area under the receiver operating curve (AUC). Performance of predictive models improved significantly by adding chest CT features to clinical evaluation and laboratory test features. Without (model CL) and with inclusion of chest CT (model RCL), sensitivity was 0.82 and 0.89 (p < 0.0001), specificity was 0.84 and 0.89 (p < 0.0001), negative predictive value was 0.96 and 0.97 (p < 0.0001), AUC was 0.92 and 0.95 (p < 0.0001), and proportion of false negative classifications was 2.6 % and 1.7 % (p < 0.0001), respectively. Addition of chest CT features to machine learning-based predictive models improves the effectiveness in ruling out COVID-19 before inpatient admission to regular wards.
F. Meng,
J. Kottlors,
R. Shahzad,
H. Liu,
P. Fervers,
Y. Jin,
M. Rinneburger,
D. Le,
M. Weisthoff,
W. Liu,
M. Ni,
Y. Sun,
L. An,
X. Huai,
D. Móré,
A. Giannakis,
I. Kaltenborn,
A. Bucher,
D. Maintz,
L. Zhang,
F. Thiele,
M. Li,
M. Perkuhn,
H. Zhang and
T. Persigehl,
"AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.",
European radiology,
Jun.
2023.
Abstract:
Differenzierung zwischen COVID-19 und gemeindebenötigter Lungenentzündung (CAP) in der berechneten Tomographie (CT) ist eine Aufgabe, die von menschlichen Radiologen und künstlicher Intelligenz (KI) durchgeführt werden kann. Die vorliegende Studie zielt darauf ab, (1) einen AI-Algorithmus zur Differenzierung von COVID-19 von CAP zu entwickeln und (2) seine Leistung zu bewerten. (3) Nutzen Sie das AI-Ergebnis als Hilfe für die radiologische Diagnose und die Auswirkungen auf relevante Parameter wie Genauigkeit der Diagnose, Diagnosezeit und Vertrauen. Wir enthielten n = 1591 Multicenter, Multivendor-Brust-CT-Scans und teilten sie in AI-Trainings- und Validierungsdatensätze, um einen AI-Algorithmus (n = 991 CT-Scans; n = 462 COVID-19 und n = 529 CAP) aus drei Zentren in China zu entwickeln. Ein unabhängiger chinesischer und deutscher Testdatensatz von n = 600 CT-Scans aus sechs Zentren (COVID-19 / CAP; n = 300) wurde verwendet, um die Leistung von acht blinden Radiologen und dem AI-Algorithmus zu testen. Ein Subtest-Datensatz (180 CT-Scans; n = 90) wurde verwendet, um die Leistung der Radiologen ohne und mit AI-Hilfe zu bewerten, um Änderungen der Diagnosegenauigkeit, der Berichtszeit und des Diagnosevertrauens zu quantifizieren. Die diagnostische Genauigkeit des AI-Algorithmus im chinesisch-deutschen Testdatensatz betrug 76,5%. Ohne KI-Hilfe betrug die Diagnosegenauigkeit der acht Radiologen 79,1% und stieg mit KI-Hilfe auf 81,5% und ging mit deutlich kürzeren Entscheidungszeiten und höheren Vertrauenswerten zusammen. Diese große multizentrische Studie zeigt, dass AI-Hilfe in CT-basierte Differenzierung von COVID-19 und CAP die radiologische Leistung mit höherer Genauigkeit und Spezifität, schnellere Diagnosezeit und verbessertes Diagnosevertrauen erhöht. • KI kann Radiologen dabei helfen, eine höhere Diagnosegenauigkeit zu erreichen, schnellere Entscheidungen zu treffen und das Diagnosevertrauen zu verbessern. • Die China-deutsche Multicenter-Studie zeigt die Vorteile einer Mensch-Maschine-Interaktion mit AI in der klinischen Radiologie zur diagnostischen Differenzierung zwischen COVID-19 und CAP in CT-Scans.
| DOI: | 10.1007/s00330-022-09335-9 |
Abstract:
Differenzierung zwischen COVID-19 und gemeindebenötigter Lungenentzündung (CAP) in der berechneten Tomographie (CT) ist eine Aufgabe, die von menschlichen Radiologen und künstlicher Intelligenz (KI) durchgeführt werden kann. Die vorliegende Studie zielt darauf ab, (1) einen AI-Algorithmus zur Differenzierung von COVID-19 von CAP zu entwickeln und (2) seine Leistung zu bewerten. (3) Nutzen Sie das AI-Ergebnis als Hilfe für die radiologische Diagnose und die Auswirkungen auf relevante Parameter wie Genauigkeit der Diagnose, Diagnosezeit und Vertrauen. Wir enthielten n = 1591 Multicenter, Multivendor-Brust-CT-Scans und teilten sie in AI-Trainings- und Validierungsdatensätze, um einen AI-Algorithmus (n = 991 CT-Scans; n = 462 COVID-19 und n = 529 CAP) aus drei Zentren in China zu entwickeln. Ein unabhängiger chinesischer und deutscher Testdatensatz von n = 600 CT-Scans aus sechs Zentren (COVID-19 / CAP; n = 300) wurde verwendet, um die Leistung von acht blinden Radiologen und dem AI-Algorithmus zu testen. Ein Subtest-Datensatz (180 CT-Scans; n = 90) wurde verwendet, um die Leistung der Radiologen ohne und mit AI-Hilfe zu bewerten, um Änderungen der Diagnosegenauigkeit, der Berichtszeit und des Diagnosevertrauens zu quantifizieren. Die diagnostische Genauigkeit des AI-Algorithmus im chinesisch-deutschen Testdatensatz betrug 76,5%. Ohne KI-Hilfe betrug die Diagnosegenauigkeit der acht Radiologen 79,1% und stieg mit KI-Hilfe auf 81,5% und ging mit deutlich kürzeren Entscheidungszeiten und höheren Vertrauenswerten zusammen. Diese große multizentrische Studie zeigt, dass AI-Hilfe in CT-basierte Differenzierung von COVID-19 und CAP die radiologische Leistung mit höherer Genauigkeit und Spezifität, schnellere Diagnosezeit und verbessertes Diagnosevertrauen erhöht. • KI kann Radiologen dabei helfen, eine höhere Diagnosegenauigkeit zu erreichen, schnellere Entscheidungen zu treffen und das Diagnosevertrauen zu verbessern. • Die China-deutsche Multicenter-Studie zeigt die Vorteile einer Mensch-Maschine-Interaktion mit AI in der klinischen Radiologie zur diagnostischen Differenzierung zwischen COVID-19 und CAP in CT-Scans.
S. Meiler,
F. Poschenrieder,
A. Mohr,
Q. Strotzer,
G. Scharf,
J. Rennert,
C. Stroszczynski,
M. Pfeifer and
O. Hamer,
"CT findings in "Post-Covid": residua from acute pneumonia or "Post-Covid-ILD"?",
Sarcoidosis, vasculitis, and diffuse lung diseases : official journal of WASOG,
vol. 40,
no. 2,
pp. e2023024,
Jun.
2023.
Abstract:
The aim of this study was to evaluate if CT findings in patients with pulmonary Post Covid syndrome represent residua after acute pneumonia or if SARS-CoV 2 induces a true ILD. Consecutive patients with status post acute Covid-19 pneumonia and persisting pulmonary symptoms were enrolled. Inclusion criteria were availability of at least one chest CT performed in the acute phase and at least one chest CT performed at least 80 days after symptom onset. In both acute and chronic phase CTs 14 CT features as well as distribution and extent of opacifications were independently determined by two chest radiologists. Evolution of every single CT lesion over time was registered intraindividually for every patient. Moreover, lung abnormalities were automatically segmented using a pre-trained nnU-Net model and volume as well as density of parenchymal lesions were plotted over the entire course of disease including all available CTs. 29 patients (median age 59 years, IQR 8, 22 men) were enrolled. Follow-up period was 80-242 days (mean 134). 152/157 (97 %) lesions in the chronic phase CTs represented residua of lung pathology in the acute phase. Subjective and objective evaluation of serial CTs showed that CT abnormalities were stable in location and continuously decreasing in extent and density. The results of our study support the hypothesis that CT abnormalities in the chronic phase after Covid-19 pneumonia represent residua in terms of prolonged healing of acute infection. We did not find any evidence for a Post Covid ILD.
| DOI: | 10.36141/svdld.v40i2.13983 |
| Datei: | https://pubmed.ncbi.nlm.nih.gov/37382073/ |
Abstract:
The aim of this study was to evaluate if CT findings in patients with pulmonary Post Covid syndrome represent residua after acute pneumonia or if SARS-CoV 2 induces a true ILD. Consecutive patients with status post acute Covid-19 pneumonia and persisting pulmonary symptoms were enrolled. Inclusion criteria were availability of at least one chest CT performed in the acute phase and at least one chest CT performed at least 80 days after symptom onset. In both acute and chronic phase CTs 14 CT features as well as distribution and extent of opacifications were independently determined by two chest radiologists. Evolution of every single CT lesion over time was registered intraindividually for every patient. Moreover, lung abnormalities were automatically segmented using a pre-trained nnU-Net model and volume as well as density of parenchymal lesions were plotted over the entire course of disease including all available CTs. 29 patients (median age 59 years, IQR 8, 22 men) were enrolled. Follow-up period was 80-242 days (mean 134). 152/157 (97 %) lesions in the chronic phase CTs represented residua of lung pathology in the acute phase. Subjective and objective evaluation of serial CTs showed that CT abnormalities were stable in location and continuously decreasing in extent and density. The results of our study support the hypothesis that CT abnormalities in the chronic phase after Covid-19 pneumonia represent residua in terms of prolonged healing of acute infection. We did not find any evidence for a Post Covid ILD.
H. Meyer,
H. Kardas,
D. Schramm,
C. Bär,
A. Wienke,
J. Borggrefe and
A. Surov,
"CT-defined pectoralis muscle mass and muscle density are associated with mortality in acute pulmonary embolism. A multicenter analysis",
Clinical nutrition (Edinburgh, Scotland),
vol. 42,
no. 6,
pp. 1036-1040,
Jun.
2023.
Abstract:
Computed tomography (CT) defined muscle mass can be used as a surrogate parameter for sarcopenia. The present study used thoracic CT to assess pectoralis muscle area and density as an imaging biomarker for prognosis of 30-day mortality in patients with acute pulmonary embolism (PE) METHODS: The clinical database was retrospectively screened for patients with thoracic CT in 3 centers. Pectoralis musculature was measured on axial slices of the thoracic CT at the level of T4 of contrast enhanced pulmonary angiography CT. Skeletal muscle area (SMA), skeletal muscle index (SMI), muscle density and gauge were calculated. Overall, 981 patients (440 female, 44.9%) with a mean age of 63.5 ± 15.9 years were included into the study and 144 patients (14.6%) died within the 30-days period. Every pectoral muscle value was higher in survivors compared to non-survivors (exemplarily for SMI 9.9 ± 3.5 cm2/m2 versus 7.8 ± 2.6 cm2/m2, p < 0.001). Moreover, 91 patients were defined as hemodynamically instable (9.3%). Comparable, every pectoral muscle parameter was higher in patients with hemodynamically stable course compared to instable course. Different muscle variables are related to 30-day mortality: SMA, OR = 0.94 (95%CI= (0.92; 0.96), p < 0.001); SMI, OR = 0.78 (95%CI= (0.72; 0.84), p < 0.001); muscle density, OR = 0.96 (95%CI = (0.94; 0.97), p < 0.001); muscle gauge OR = 0.96 (95%CI = (0.94; 0.99), p < 0.001). SMI and muscle density were independently associated with 30-days mortality: SMI, OR = 0.81 (95%CI = (0.75; 0.88), p < 0.001); muscle density: OR = 0.96 (95%CI= (0.95; 0.98), p < 0.001). Parameters of the pectoralis musculature are associated with 30-day mortality in patients with acute PE. These findings should lead to an independent validation study and ultimately to the inclusion into clinical routine as a prognostic factor.
| DOI: | 10.1016/j.clnu.2023.04.022 |
| Datei: | https://pubmed.ncbi.nlm.nih.gov/37156143/ |
Abstract:
Computed tomography (CT) defined muscle mass can be used as a surrogate parameter for sarcopenia. The present study used thoracic CT to assess pectoralis muscle area and density as an imaging biomarker for prognosis of 30-day mortality in patients with acute pulmonary embolism (PE) METHODS: The clinical database was retrospectively screened for patients with thoracic CT in 3 centers. Pectoralis musculature was measured on axial slices of the thoracic CT at the level of T4 of contrast enhanced pulmonary angiography CT. Skeletal muscle area (SMA), skeletal muscle index (SMI), muscle density and gauge were calculated. Overall, 981 patients (440 female, 44.9%) with a mean age of 63.5 ± 15.9 years were included into the study and 144 patients (14.6%) died within the 30-days period. Every pectoral muscle value was higher in survivors compared to non-survivors (exemplarily for SMI 9.9 ± 3.5 cm2/m2 versus 7.8 ± 2.6 cm2/m2, p < 0.001). Moreover, 91 patients were defined as hemodynamically instable (9.3%). Comparable, every pectoral muscle parameter was higher in patients with hemodynamically stable course compared to instable course. Different muscle variables are related to 30-day mortality: SMA, OR = 0.94 (95%CI= (0.92; 0.96), p < 0.001); SMI, OR = 0.78 (95%CI= (0.72; 0.84), p < 0.001); muscle density, OR = 0.96 (95%CI = (0.94; 0.97), p < 0.001); muscle gauge OR = 0.96 (95%CI = (0.94; 0.99), p < 0.001). SMI and muscle density were independently associated with 30-days mortality: SMI, OR = 0.81 (95%CI = (0.75; 0.88), p < 0.001); muscle density: OR = 0.96 (95%CI= (0.95; 0.98), p < 0.001). Parameters of the pectoralis musculature are associated with 30-day mortality in patients with acute PE. These findings should lead to an independent validation study and ultimately to the inclusion into clinical routine as a prognostic factor.
H. Cao,
A. Morotti,
F. Mazzacane,
D. Desser,
F. Schlunk,
C. Güttler,
H. Kniep,
T. Penzkofer,
J. Fiehler,
U. Hanning,
A. Dell'Orco and
J. Nawabi,
"External Validation and Retraining of DeepBleed: The First Open-Source 3D Deep Learning Network for the Segmentation of Spontaneous Intracerebral and Intraventricular Hemorrhage.",
Journal of clinical medicine,
Jun.
2023.
Abstract:
Ziel dieser Studie war es, die Leistung der ersten öffentlich verfügbaren automatisierten 3D-Segmentation für spontane intrazerebrale Blutung (ICH) basierend auf einem 3D-Neuralnetzwerk vor und nach der Umschulung zu bewerten. Wir haben eine unabhängige Validierung dieses Modells mit einem multizentrischen retrospektiven Kohort durchgeführt. Performance Metriken wurden mit dem Würfel-Score (DSC), Sensitivität und positiven Vorhersagewerten (PPV) bewertet. Wir haben das Originalmodell (OM) neu trainiert und die Performance über ein externes Validierungsdesign bewertet. Ein multivariate lineares Regressionsmodell wurde verwendet, um unabhängige Variablen zu identifizieren, die mit der Leistung des Modells verbunden sind. Durch Pearsons Korrelationskoeffizienten (r) bzw. Intra-Klasse-Korrelationskoeffizienten (ICC) wurden Vereinbarungen in volumetrischen Messungen und Segmentierungen bewertet. Bei 1040 Patienten hatte das OM einen Median DSC, Sensitivität und PPV von 0,84, 0,79 und 0,93, verglichen mit deneno f 0,83, 0,80 und 0,91 im retrainierten Modell (RM). Der Median DSC für infratentoriale ICH war jedoch relativ gering und nach Umschulung bei <i>p</i> < 0,001 signifikant verbessert. Die Vereinbarung zwischen volumetrischen Messungen (r > 0,90, <i>p</i> > 0,05) und Segmentierungen (ICC ≥ 0,9, <i>p</i> < 0,001) war ausgezeichnet. Das Modell zeigte eine gute Verallgemeinerung in einer externen Validierungskohorte. Ortsspezifische Varianzen verbesserte sich nach dem Rücktraining deutlich. Externe Validierung und Umschulung sind wichtige Schritte, um zu berücksichtigen, bevor Sie tiefe Lernmodelle in neuen klinischen Einstellungen anwenden.
| DOI: | 10.3390/jcm12124005 |
Abstract:
Ziel dieser Studie war es, die Leistung der ersten öffentlich verfügbaren automatisierten 3D-Segmentation für spontane intrazerebrale Blutung (ICH) basierend auf einem 3D-Neuralnetzwerk vor und nach der Umschulung zu bewerten. Wir haben eine unabhängige Validierung dieses Modells mit einem multizentrischen retrospektiven Kohort durchgeführt. Performance Metriken wurden mit dem Würfel-Score (DSC), Sensitivität und positiven Vorhersagewerten (PPV) bewertet. Wir haben das Originalmodell (OM) neu trainiert und die Performance über ein externes Validierungsdesign bewertet. Ein multivariate lineares Regressionsmodell wurde verwendet, um unabhängige Variablen zu identifizieren, die mit der Leistung des Modells verbunden sind. Durch Pearsons Korrelationskoeffizienten (r) bzw. Intra-Klasse-Korrelationskoeffizienten (ICC) wurden Vereinbarungen in volumetrischen Messungen und Segmentierungen bewertet. Bei 1040 Patienten hatte das OM einen Median DSC, Sensitivität und PPV von 0,84, 0,79 und 0,93, verglichen mit deneno f 0,83, 0,80 und 0,91 im retrainierten Modell (RM). Der Median DSC für infratentoriale ICH war jedoch relativ gering und nach Umschulung bei <i>p</i> < 0,001 signifikant verbessert. Die Vereinbarung zwischen volumetrischen Messungen (r > 0,90, <i>p</i> > 0,05) und Segmentierungen (ICC ≥ 0,9, <i>p</i> < 0,001) war ausgezeichnet. Das Modell zeigte eine gute Verallgemeinerung in einer externen Validierungskohorte. Ortsspezifische Varianzen verbesserte sich nach dem Rücktraining deutlich. Externe Validierung und Umschulung sind wichtige Schritte, um zu berücksichtigen, bevor Sie tiefe Lernmodelle in neuen klinischen Einstellungen anwenden.
R. J. Gertz,
A. C. Bunck,
S. Lennartz,
T. Dratsch,
A. Iuga,
D. Maintz and
J. Kottlors,
"GPT-4 for Automated Determination of Radiological Study and Protocol based on Radiology Request Forms: A Feasibility Study",
Radiology,
vol. 307,
no. 5,
pp. e230877,
Jun.
2023.
Abstract:
Published under a CC BY 4.0 license.
| DOI: | 10.1148/radiol.230877 |
| Datei: | https://pubmed.ncbi.nlm.nih.gov/37310247/ |
Abstract:
Published under a CC BY 4.0 license.
[en]
Z. Liu,
C. Hollmann,
S. Kalanidhi,
A. Grothey,
S. Keating,
I. Mena-Palomo,
S. Lamer,
A. Schlosser,
A. Kaiping,
C. Scheller,
F. Sotzny,
A. Horn,
C. Nürnberger,
V. Cejka,
T. Bahmer,
S. Schreiber,
J. J. Vehreschild,
O. Miljukov,
C. Schäfer,
L. Kretzler,
T. Keil,
J. Reese,
F. A. Eichner,
L. Schmidbauer,
P. U. Heuschmann,
S. Störk,
C. Morbach,
G. Riemekasten,
N. Beyersdorf,
C. Scheibenbogen,
R. K. Naviaux,
M. Williams,
M. E. Ariza and
B. K. Prusty,
"Increased circulating fibronectin, depletion of natural IgM and
heightened EBV, HSV-1 reactivation in ME/CFS and long
COVID",
medRxiv,
vol. Online ahead of print,
Jun.
2023.
Abstract:
Myalgic Encephalomyelitis/ Chronic Fatigue syndrome (ME/CFS) is a complex, debilitating, long-term illness without a diagnostic biomarker. ME/CFS patients share overlapping symptoms with long COVID patients, an observation which has strengthened the infectious origin hypothesis of ME/CFS. However, the exact sequence of events leading to disease development is largely unknown for both clinical conditions. Here we show antibody response to herpesvirus dUTPases, particularly to that of Epstein-Barr virus (EBV) and HSV-1, increased circulating fibronectin (FN1) levels in serum and depletion of natural IgM against fibronectin ((n)IgM-FN1) are common factors for both severe ME/CFS and long COVID. We provide evidence for herpesvirus dUTPases-mediated alterations in host cell cytoskeleton, mitochondrial dysfunction and OXPHOS. Our data show altered active immune complexes, immunoglobulin-mediated mitochondrial fragmentation as well as adaptive IgM production in ME/CFS patients. Our findings provide mechanistic insight into both ME/CFS and long COVID development. Finding of increased circulating FN1 and depletion of (n)IgM-FN1 as a biomarker for the severity of both ME/CFS and long COVID has an immediate implication in diagnostics and development of treatment modalities.
| DOI: | 10.1101/2023.06.23.23291827 |
| Pubmed: | 37425897 |
Abstract:
Myalgic Encephalomyelitis/ Chronic Fatigue syndrome (ME/CFS) is a complex, debilitating, long-term illness without a diagnostic biomarker. ME/CFS patients share overlapping symptoms with long COVID patients, an observation which has strengthened the infectious origin hypothesis of ME/CFS. However, the exact sequence of events leading to disease development is largely unknown for both clinical conditions. Here we show antibody response to herpesvirus dUTPases, particularly to that of Epstein-Barr virus (EBV) and HSV-1, increased circulating fibronectin (FN1) levels in serum and depletion of natural IgM against fibronectin ((n)IgM-FN1) are common factors for both severe ME/CFS and long COVID. We provide evidence for herpesvirus dUTPases-mediated alterations in host cell cytoskeleton, mitochondrial dysfunction and OXPHOS. Our data show altered active immune complexes, immunoglobulin-mediated mitochondrial fragmentation as well as adaptive IgM production in ME/CFS patients. Our findings provide mechanistic insight into both ME/CFS and long COVID development. Finding of increased circulating FN1 and depletion of (n)IgM-FN1 as a biomarker for the severity of both ME/CFS and long COVID has an immediate implication in diagnostics and development of treatment modalities.
M. Hoffmann,
L. Wong,
P. Arora,
L. Zhang,
C. Rocha,
A. Odle,
I. Nehlmeier,
A. Kempf,
A. Richter,
N. Halwe,
J. Schön,
L. Ulrich,
D. Hoffmann,
M. Beer,
C. Drosten,
S. Perlman and
S. Pöhlmann,
"Omicron subvariant BA.5 efficiently infects lung cells",
Nat Commun,
vol. 14,
no. 1,
pp. 3500,
Jun.
2023.
| DOI: | 10.1038/s41467-023-39147-4 |
U. Ebong,
S. M. Büttner,
S. A. Schmidt,
F. Flack,
P. Korf,
L. Peters,
B. Grüner,
S. Stenger,
T. Stamminger,
H. Kestler,
M. Beer and
C. Kloth,
"Quantitative Evaluation of COVID-19 Pneumonia CT Using AI Analysis-Feasibility and Differentiation from Other Common Pneumonia Forms.",
Diagnostics (Basel, Switzerland),
Jun.
2023.
Abstract:
<b>PURPOSE: </b> Um die technische Durchführbarkeit eines KI-basierten Software-Prototyps zu implementieren, der für den Nachweis von COVID-19-Pneumonie in CT-Datensätzen der Lunge und die Differenzierung zwischen anderen Pneumonie-Etiologien optimiert ist. <b>METHODEN: </b> Diese einzentige retrospektive Fall-Kontroll-Studie ergab nacheinander 144 Patienten (58 weiblich, mittleres Alter 57,72 ± 18,25 y) mit CT-Datensätzen der Lunge. Untergruppen einschließlich bestätigter Bakterien (>i>n/i> = 24, 16,6%), virale (<i>n</i> = 52, 36,1%) oder Pilze (<i>n</i> = 25, 16,6%) Pneumonie und (<i>n>/i> = 43, 30.7%) Patienten ohne nachgewiesene Pneumonie (Compison group) wurden mit Hilfe der AI-basierten <i> <b>RESULTS: </b> Die Software erreichte eine optimale Empfindlichkeit von 80,8% mit einer Spezifität von 50% für den Nachweis von COVID-19; der menschliche Radiologe erreichte jedoch eine optimale Empfindlichkeit von 80,8% und eine Spezifität von 97,2%. Die mittlere Nachbearbeitungszeit betrug 7,61 ± 4,2 min. Die Verwendung eines Kontrastmittels hat die Ergebnisse der Software nicht beeinflusst (<i>p</i> = 0,81). Die mittlere bewertete COVID-19-Wahrscheinlichkeit liegt bei COVID-19-Patienten bei 0,80 ± 0,36 deutlich höher als bei Patienten mit pilzlicher Lungenentzündung (<i>p>/i>/i> < 0,001). Der mittlere Anteil an Opazität (PO) und Anteil an hoher Opazität (PHO ≥ -200 HU) lag bei COVID-19 Patienten deutlich höher als bei gesunden Patienten. Der Gesamtmittelwert HU bei COVID-19-Patienten betrug -679.57 ± 112.72, was deutlich höher ist als bei der gesunden Kontrollgruppe (<i>p</i> < 0,001). <b>CONCLUSION:</b> Die Detektion und Quantifizierung von Lungenentzündungen über die primär geschulten COVID-19-Datensätze ist möglich und zeigt vergleichbare Ergebnisse für COVID-19-Pneumonie zu einem erfahrenen Leser. Die Vorteile sind die schnelle, automatisierte Segmentierung und Quantifizierung der Lungenentzündung.
| DOI: | 10.3390/diagnostics13122129 |
Abstract:
<b>PURPOSE: </b> Um die technische Durchführbarkeit eines KI-basierten Software-Prototyps zu implementieren, der für den Nachweis von COVID-19-Pneumonie in CT-Datensätzen der Lunge und die Differenzierung zwischen anderen Pneumonie-Etiologien optimiert ist. <b>METHODEN: </b> Diese einzentige retrospektive Fall-Kontroll-Studie ergab nacheinander 144 Patienten (58 weiblich, mittleres Alter 57,72 ± 18,25 y) mit CT-Datensätzen der Lunge. Untergruppen einschließlich bestätigter Bakterien (>i>n/i> = 24, 16,6%), virale (<i>n</i> = 52, 36,1%) oder Pilze (<i>n</i> = 25, 16,6%) Pneumonie und (<i>n>/i> = 43, 30.7%) Patienten ohne nachgewiesene Pneumonie (Compison group) wurden mit Hilfe der AI-basierten <i> <b>RESULTS: </b> Die Software erreichte eine optimale Empfindlichkeit von 80,8% mit einer Spezifität von 50% für den Nachweis von COVID-19; der menschliche Radiologe erreichte jedoch eine optimale Empfindlichkeit von 80,8% und eine Spezifität von 97,2%. Die mittlere Nachbearbeitungszeit betrug 7,61 ± 4,2 min. Die Verwendung eines Kontrastmittels hat die Ergebnisse der Software nicht beeinflusst (<i>p</i> = 0,81). Die mittlere bewertete COVID-19-Wahrscheinlichkeit liegt bei COVID-19-Patienten bei 0,80 ± 0,36 deutlich höher als bei Patienten mit pilzlicher Lungenentzündung (<i>p>/i>/i> < 0,001). Der mittlere Anteil an Opazität (PO) und Anteil an hoher Opazität (PHO ≥ -200 HU) lag bei COVID-19 Patienten deutlich höher als bei gesunden Patienten. Der Gesamtmittelwert HU bei COVID-19-Patienten betrug -679.57 ± 112.72, was deutlich höher ist als bei der gesunden Kontrollgruppe (<i>p</i> < 0,001). <b>CONCLUSION:</b> Die Detektion und Quantifizierung von Lungenentzündungen über die primär geschulten COVID-19-Datensätze ist möglich und zeigt vergleichbare Ergebnisse für COVID-19-Pneumonie zu einem erfahrenen Leser. Die Vorteile sind die schnelle, automatisierte Segmentierung und Quantifizierung der Lungenentzündung.
Y. Shi,
R. Strobl,
C. Apfelbacher,
T. Bahmer,
R. Geisler,
P. Heuschmann,
A. Horn,
H. Hoven,
T. Keil,
M. Krawczak,
L. Krist,
C. Lemhöfer,
W. Lieb,
B. Lorenz-Depiereux,
R. Mikolajczyk,
F. Montellano,
J. Reese,
S. Schreiber,
N. Skoetz,
S. Störk,
J. J. Vehreschild,
M. Witzenrath,
E. Grill and
On Behalf Of The Napkon Study Group,
"Persistent symptoms and risk factors predicting prolonged time to symptom-free after SARS‑CoV‑2 infection: an analysis of the baseline examination of the German COVIDOM/NAPKON-POP cohort",
Mai
2023.
| DOI: | 10.1007/s15010-023-02043-6 |
D. Krefting,
G. Anton,
I. Chaplinskaya-Sobol,
S. Hanss,
W. Hoffmann,
S. Hopff,
M. Kraus,
R. Lorbeer,
B. Lorenz-Depiereux,
T. Illig,
C. Schäfer,
J. Schaller,
D. Stahl,
H. Valentin,
P. Heuschmann and
J. Vehreschild,
"The Importance of Being FAIR and FAST - The Clinical Epidemiology and Study Platform of the German Network University Medicine (NUKLEUS)",
Mai
2023.
| DOI: | 10.3233/SHTI230071 |
[eng]
K. Bräutigam,
S. Reinhard,
M. Wartenberg,
S. Forster,
K. Greif,
M. Granai,
H. Bösmüller,
K. Klingel and
C. M. Schürch,
"Comprehensive analysis of SARS-CoV-2 receptor proteins in human respiratory tissues identifies alveolar macrophages as potential virus entry site",
Histopathology,
vol. 82,
no. 6,
pp. 846—859,
Mai
2023.
Abstract:
AIMS: COVID-19 has had enormous consequences on global health-care and has resulted in millions of fatalities. The exact mechanism and site of SARS-CoV-2 entry into the body remains insufficiently understood. Recently, novel virus receptors were identified, and alveolar macrophages were suggested as a potential viral entry cell type and vector for intra-alveolar virus transmission. Here, we investigated the protein expression of 10 well-known and novel virus entry molecules along potential entry sites in humans using immunohistochemistry. METHODS AND RESULTS: Samples of different anatomical sites from up to 93 patients were incorporated into tissue microarrays. Protein expression of ACE2, TMPRSS2, furin, CD147, C-type lectin receptors (CD169, CD209, CD299), neuropilin-1, ASGR1 and KREMEN1 were analysed. In lung tissues, at least one of the three receptors ACE2, ASGR1 or KREMEN1 was expressed in the majority of cases. Moreover, all the investigated molecules were found to be expressed in alveolar macrophages, and co-localisation with SARS-CoV-2 N-protein was demonstrated using dual immunohistochemistry in lung tissue from a COVID-19 autopsy. While CD169 and CD209 showed consistent protein expression in sinonasal, conjunctival and bronchiolar tissues, neuropilin-1 and ASGR1 were mostly absent, suggesting a minor relevance of these two molecules at these specific sites. CONCLUSION: Our results extend recent discoveries indicating a role for these molecules in virus entry at different anatomical sites. Moreover, they support the notion of alveolar macrophages being a potential entry cell for SARS-CoV-2.
| DOI: | 10.1111/his.14871 |
Abstract:
AIMS: COVID-19 has had enormous consequences on global health-care and has resulted in millions of fatalities. The exact mechanism and site of SARS-CoV-2 entry into the body remains insufficiently understood. Recently, novel virus receptors were identified, and alveolar macrophages were suggested as a potential viral entry cell type and vector for intra-alveolar virus transmission. Here, we investigated the protein expression of 10 well-known and novel virus entry molecules along potential entry sites in humans using immunohistochemistry. METHODS AND RESULTS: Samples of different anatomical sites from up to 93 patients were incorporated into tissue microarrays. Protein expression of ACE2, TMPRSS2, furin, CD147, C-type lectin receptors (CD169, CD209, CD299), neuropilin-1, ASGR1 and KREMEN1 were analysed. In lung tissues, at least one of the three receptors ACE2, ASGR1 or KREMEN1 was expressed in the majority of cases. Moreover, all the investigated molecules were found to be expressed in alveolar macrophages, and co-localisation with SARS-CoV-2 N-protein was demonstrated using dual immunohistochemistry in lung tissue from a COVID-19 autopsy. While CD169 and CD209 showed consistent protein expression in sinonasal, conjunctival and bronchiolar tissues, neuropilin-1 and ASGR1 were mostly absent, suggesting a minor relevance of these two molecules at these specific sites. CONCLUSION: Our results extend recent discoveries indicating a role for these molecules in virus entry at different anatomical sites. Moreover, they support the notion of alveolar macrophages being a potential entry cell for SARS-CoV-2.
J. Leonhardi,
N. Bailis,
M. Lerche,
T. Denecke,
A. Surov and
H. Meyer,
"Computed Tomography Embolus Texture Analysis as a Prognostic Marker of Acute Pulmonary Embolism",
Angiology,
vol. 74,
no. 5,
pp. 461-471,
Mai
2023.
Abstract:
Texture analysis is a quantitative imaging analysis that provides novel biomarkers beyond conventional image reading. Our aim was to use texture analysis of pulmonary emboli derived from thoracic computed tomography for prediction of mortality and prognosis of acute pulmonary embolism (PE). Overall, 216 patients (116 female, 53.7%) were included in the analysis. Texture analysis was calculated on axial slices of the contrast enhanced pulmonary angiography of the proximal embolus. Clinical scores, serological parameters, need for intubation, intensive care unit (ICU) admission and mortality was assessed and correlated with the texture features. In the correlation analysis, there were several associations with mortality in days, the highest for the parameter S(0,5)SumVarnc (r = -0.43, P < 0.001). Another parameter, S(3,-3)AngScMom correlated with sepsis-related organ failure assessment score (SOFA)-score (r = 0.31, P < 0.001). Several texture features correlated with venous lactate and glucose levels. In discrimination analysis, there were significant differences in regard to texture features between survivors and non-survivors and between patients with and without the need for ICU admission (P = 0.02, respectively). These results highlight the potential clinical benefit of texture features in patients with acute PE as novel imaging biomarkers. Further studies are needed to validate these results.
| DOI: | 10.1177/00033197221111862 |
| Datei: | https://pubmed.ncbi.nlm.nih.gov/35973807/ |
Abstract:
Texture analysis is a quantitative imaging analysis that provides novel biomarkers beyond conventional image reading. Our aim was to use texture analysis of pulmonary emboli derived from thoracic computed tomography for prediction of mortality and prognosis of acute pulmonary embolism (PE). Overall, 216 patients (116 female, 53.7%) were included in the analysis. Texture analysis was calculated on axial slices of the contrast enhanced pulmonary angiography of the proximal embolus. Clinical scores, serological parameters, need for intubation, intensive care unit (ICU) admission and mortality was assessed and correlated with the texture features. In the correlation analysis, there were several associations with mortality in days, the highest for the parameter S(0,5)SumVarnc (r = -0.43, P < 0.001). Another parameter, S(3,-3)AngScMom correlated with sepsis-related organ failure assessment score (SOFA)-score (r = 0.31, P < 0.001). Several texture features correlated with venous lactate and glucose levels. In discrimination analysis, there were significant differences in regard to texture features between survivors and non-survivors and between patients with and without the need for ICU admission (P = 0.02, respectively). These results highlight the potential clinical benefit of texture features in patients with acute PE as novel imaging biomarkers. Further studies are needed to validate these results.
C. Dolch,
I. Bernemann,
B. Fösel,
S. Haag,
N. Klopp,
V. Kopfnagel,
A. Kühn-Steven,
S. Kunze,
B. Lorenz-Depiereux,
S. Mücke,
I. Ruhl,
M. Schieck,
G. Anton,
T. Illig and
M. Scheuner,
"Der NAPKON Bioprobenkern stellt sich vor",
Mai
2023.
S. Mücke,
S. Kunze,
V. Kopfnagel,
B. Fösel,
I. Bernemann,
P. Christ,
C. Dolch,
N. Klopp,
B. Lorenz-Depiereux,
T. Illig,
G. Anton and
N. Lindemann,
"Dezentralisiertes Biobanking in NAPKON – nach dem Sammeln ist vor der Herausgabe“.",
Mai
2023.
[en]
K. Schulze-Wundling,
P. F. Ottensmeyer,
K. M. Meyer-Schlinkmann,
M. Deckena,
S. Krüger,
S. Schlinkert,
A. Budde,
D. Münstermann,
N. Töpfner,
A. Petersmann,
M. Nauck,
A. Karch,
B. Lange,
S. Blaschke,
C. Tiemann and
H. Streeck,
"Immunity against SARS-CoV-2 in the German population",
Dtsch. Arztebl. Int.,
vol. 120,
no. 19,
pp. 337—344,
Mai
2023.
Abstract:
BACKGROUND: Early during the SARS-CoV-2 pandemic, national population-based seroprevalence surveys were conducted in some countries; however, this was not done in Germany. In particular, no seroprevalence surveys were planned for the summer of 2022. In the context of the IMMUNEBRIDGE project, the GUIDE study was carried out to estimate seroprevalence on the national and regional levels. METHODS: To obtain an overview of the population-wide immunity against SARS-CoV-2 among adults in Germany that would be as statistically robust as possible, serological tests were carried out using self-sampling dried blood spot cards in conjunction with surveys, one by telephone and one online. Blood samples were analyzed for the presence of antibodies to the S and N antigens of SARS-CoV-2. RESULTS: Among the 15 932 participants, antibodies to the S antigen were detected in 95.7%, and to the N antigen in 44.4%. In the higher-risk age groups of persons aged 65 and above and persons aged 80 and above, anti-S antibodies were found in 97,4% and 98.8%, respectively. Distinct regional differences in the distribution of anti-S and anti-N antibodies emerged. Immunity gaps were found both regionally and in particular subgroups of the population. High anti-N antibody levels were especially common in eastern German states, and high anti-S antibody levels in western German states. CONCLUSION: These findings indicate that a large percentage of the adult German population has formed antibodies against the SARS-CoV-2 virus. This will markedly lower the probability of an overburdening of the health care system by hospitalization and high occupancy of intensive care units due to future SARS-CoV-2 waves, depending on the viral characteristics of then prevailing variants.
| DOI: | https://doi.org/10.3238/arztebl.m2023.0072 |
Abstract:
BACKGROUND: Early during the SARS-CoV-2 pandemic, national population-based seroprevalence surveys were conducted in some countries; however, this was not done in Germany. In particular, no seroprevalence surveys were planned for the summer of 2022. In the context of the IMMUNEBRIDGE project, the GUIDE study was carried out to estimate seroprevalence on the national and regional levels. METHODS: To obtain an overview of the population-wide immunity against SARS-CoV-2 among adults in Germany that would be as statistically robust as possible, serological tests were carried out using self-sampling dried blood spot cards in conjunction with surveys, one by telephone and one online. Blood samples were analyzed for the presence of antibodies to the S and N antigens of SARS-CoV-2. RESULTS: Among the 15 932 participants, antibodies to the S antigen were detected in 95.7%, and to the N antigen in 44.4%. In the higher-risk age groups of persons aged 65 and above and persons aged 80 and above, anti-S antibodies were found in 97,4% and 98.8%, respectively. Distinct regional differences in the distribution of anti-S and anti-N antibodies emerged. Immunity gaps were found both regionally and in particular subgroups of the population. High anti-N antibody levels were especially common in eastern German states, and high anti-S antibody levels in western German states. CONCLUSION: These findings indicate that a large percentage of the adult German population has formed antibodies against the SARS-CoV-2 virus. This will markedly lower the probability of an overburdening of the health care system by hospitalization and high occupancy of intensive care units due to future SARS-CoV-2 waves, depending on the viral characteristics of then prevailing variants.
[en]
Y. Shi,
R. Strobl,
C. Apfelbacher,
T. Bahmer,
R. Geisler,
P. Heuschmann,
A. Horn,
H. Hoven,
T. Keil,
M. Krawczak,
L. Krist,
C. Lemhöfer,
W. Lieb,
B. Lorenz-Depiereux,
R. Mikolajczyk,
F. A. Montellano,
J. P. Reese,
S. Schreiber,
N. Skoetz,
S. Störk,
J. J. Vehreschild,
M. Witzenrath,
E. Grill and
NAPKON Study Group,
"Persistent symptoms and risk factors predicting prolonged time to symptom-free after SARS‑CoV‑2 infection: an analysis of the baseline examination of the German COVIDOM/NAPKON-POP cohort",
Infection,
vol. 51,
no. 6,
pp. 1679–1694,
Mai
2023.
Abstract:
PURPOSE: We aimed to assess symptoms in patients after SARS-CoV-2 infection and to identify factors predicting prolonged time to symptom-free. METHODS: COVIDOM/NAPKON-POP is a population-based prospective cohort of adults whose first on-site visits were scheduled $\geq$ 6 months after a positive SARS-CoV-2 PCR test. Retrospective data including self-reported symptoms and time to symptom-free were collected during the survey before a site visit. In the survival analyses, being symptom-free served as the event and time to be symptom-free as the time variable. Data were visualized with Kaplan-Meier curves, differences were tested with log-rank tests. A stratified Cox proportional hazard model was used to estimate adjusted hazard ratios (aHRs) of predictors, with aHR < 1 indicating a longer time to symptom-free. RESULTS: Of 1175 symptomatic participants included in the present analysis, 636 (54.1%) reported persistent symptoms after 280 days (SD 68) post infection. 25% of participants were free from symptoms after 18 days [quartiles: 14, 21]. Factors associated with prolonged time to symptom-free were age 49-59 years compared to < 49 years (aHR 0.70, 95% CI 0.56-0.87), female sex (aHR 0.78, 95% CI 0.65-0.93), lower educational level (aHR 0.77, 95% CI 0.64-0.93), living with a partner (aHR 0.81, 95% CI 0.66-0.99), low resilience (aHR 0.65, 95% CI 0.47-0.90), steroid treatment (aHR 0.22, 95% CI 0.05-0.90) and no medication (aHR 0.74, 95% CI 0.62-0.89) during acute infection. CONCLUSION: In the studied population, COVID-19 symptoms had resolved in one-quarter of participants within 18 days, and in 34.5% within 28 days. Over half of the participants reported COVID-19-related symptoms 9 months after infection. Symptom persistence was predominantly determined by participant's characteristics that are difficult to modify.
| DOI: | 10.1007/s15010-023-02043-6 |
| Pubmed: | 37231313 |
Abstract:
PURPOSE: We aimed to assess symptoms in patients after SARS-CoV-2 infection and to identify factors predicting prolonged time to symptom-free. METHODS: COVIDOM/NAPKON-POP is a population-based prospective cohort of adults whose first on-site visits were scheduled $\geq$ 6 months after a positive SARS-CoV-2 PCR test. Retrospective data including self-reported symptoms and time to symptom-free were collected during the survey before a site visit. In the survival analyses, being symptom-free served as the event and time to be symptom-free as the time variable. Data were visualized with Kaplan-Meier curves, differences were tested with log-rank tests. A stratified Cox proportional hazard model was used to estimate adjusted hazard ratios (aHRs) of predictors, with aHR < 1 indicating a longer time to symptom-free. RESULTS: Of 1175 symptomatic participants included in the present analysis, 636 (54.1%) reported persistent symptoms after 280 days (SD 68) post infection. 25% of participants were free from symptoms after 18 days [quartiles: 14, 21]. Factors associated with prolonged time to symptom-free were age 49-59 years compared to < 49 years (aHR 0.70, 95% CI 0.56-0.87), female sex (aHR 0.78, 95% CI 0.65-0.93), lower educational level (aHR 0.77, 95% CI 0.64-0.93), living with a partner (aHR 0.81, 95% CI 0.66-0.99), low resilience (aHR 0.65, 95% CI 0.47-0.90), steroid treatment (aHR 0.22, 95% CI 0.05-0.90) and no medication (aHR 0.74, 95% CI 0.62-0.89) during acute infection. CONCLUSION: In the studied population, COVID-19 symptoms had resolved in one-quarter of participants within 18 days, and in 34.5% within 28 days. Over half of the participants reported COVID-19-related symptoms 9 months after infection. Symptom persistence was predominantly determined by participant's characteristics that are difficult to modify.
G. Anton,
T. Bahmer,
S. Ciesek,
C. Dolch,
B. Fösel,
P. Heuschmann,
V. Kopfnagel,
S. Kunze,
B. Lorenz-Depiereux,
S. Mücke,
M. Nauck,
S. Pullamsetti,
M. Ralser,
J. Reese,
C. Schäfer,
M. Schattschneider,
S. Schreiber,
J. Vehreschild,
M. Witzenrath and
T. Illig,
"Systematische molekulare Analysen der NAPKON Kohorten – ein Überblick.",
Mai
2023.
R. Mieling,
M. Neidhardt,
S. Latus,
C. Stapper,
S. Gerlach,
I. Kniep,
A. Heinemann,
B. Ondruschka and
A. Schlaefer,
"Collaborative Robotic Biopsy with Trajectory Guidance and Needle Tip Force Feedback",
Mai
2023,
pp. 6893—6900.
| DOI: | 10.1109/ICRA48891.2023.10161377 |
B. Lorenz-Depiereux,
M. Scherer,
H. Valentin,
S. Berger,
M. Stecher,
A. Hermes,
M. Müller,
B. Balzuweit,
D. Stahl,
C. Schäfer,
S. Hanss,
J. Schaller,
M. Kraus and
E. Heim,
"Poster NAPKON.vention: Ethik-Koordination in NAPKON",
Mai
2023.
S. Tayebi Arasteh,
P. Isfort,
M. Saehn,
G. Mueller-Franzes,
F. Khader,
J. N. Kather,
C. Kuhl,
S. Nebelung and
D. Truhn,
"Collaborative training of medical artificial intelligence models with non-uniform labels.",
Scientific reports,
Apr.
2023.
Abstract:
Aufgrund der rasanten Fortschritte in den letzten Jahren wird die medizinische Bildanalyse weitgehend von Deep Learning (DL) dominiert. Der Aufbau von leistungsstarken und robusten DL-Modellen erfordert jedoch Schulungen mit großen Multi-Party-Datensätzen. Während mehrere Stakeholder öffentlich verfügbare Datensätze bereitgestellt haben, sind die Art und Weise, wie diese Daten markiert werden, sehr unterschiedlich. Eine Einrichtung könnte beispielsweise einen Datensatz von Brust-Funkgraphen mit Etiketten für das Vorhandensein von Lungenentzündung bereitstellen, während eine andere Einrichtung einen Schwerpunkt auf der Bestimmung des Vorhandenseins von Metastasen in der Lunge haben könnte. Die Ausbildung eines einzigen KI-Modells mit all diesen Daten ist mit dem herkömmlichen föderierten Lernen (FL) nicht machbar. Dies ruft uns dazu auf, eine Erweiterung des weitverbreiteten FL-Prozesses vorzuschlagen, nämlich flexibles föderiertes Lernen (FFL) zur kollaborativen Ausbildung solcher Daten. Mit 695.000 Brust-Radiographen von fünf Institutionen von überall auf der Welt mit unterschiedlichen Etiketten- wir zeigen, dass mit heterogen markierten Datensätzen, FFL-basiertes Training führt zu einer signifikanten Leistungssteigerung im Vergleich zu herkömmlichen FL-Training, wo nur die gleichmäßig markierten Bilder verwendet werden. Wir glauben, dass unser vorgeschlagener Algorithmus den Prozess beschleunigen könnte, kollaborative Trainingsmethoden aus der Forschungs- und Simulationsphase in die realen Anwendungen im Gesundheitswesen zu bringen.
| DOI: | 10.1038/s41598-023-33303-y |
Abstract:
Aufgrund der rasanten Fortschritte in den letzten Jahren wird die medizinische Bildanalyse weitgehend von Deep Learning (DL) dominiert. Der Aufbau von leistungsstarken und robusten DL-Modellen erfordert jedoch Schulungen mit großen Multi-Party-Datensätzen. Während mehrere Stakeholder öffentlich verfügbare Datensätze bereitgestellt haben, sind die Art und Weise, wie diese Daten markiert werden, sehr unterschiedlich. Eine Einrichtung könnte beispielsweise einen Datensatz von Brust-Funkgraphen mit Etiketten für das Vorhandensein von Lungenentzündung bereitstellen, während eine andere Einrichtung einen Schwerpunkt auf der Bestimmung des Vorhandenseins von Metastasen in der Lunge haben könnte. Die Ausbildung eines einzigen KI-Modells mit all diesen Daten ist mit dem herkömmlichen föderierten Lernen (FL) nicht machbar. Dies ruft uns dazu auf, eine Erweiterung des weitverbreiteten FL-Prozesses vorzuschlagen, nämlich flexibles föderiertes Lernen (FFL) zur kollaborativen Ausbildung solcher Daten. Mit 695.000 Brust-Radiographen von fünf Institutionen von überall auf der Welt mit unterschiedlichen Etiketten- wir zeigen, dass mit heterogen markierten Datensätzen, FFL-basiertes Training führt zu einer signifikanten Leistungssteigerung im Vergleich zu herkömmlichen FL-Training, wo nur die gleichmäßig markierten Bilder verwendet werden. Wir glauben, dass unser vorgeschlagener Algorithmus den Prozess beschleunigen könnte, kollaborative Trainingsmethoden aus der Forschungs- und Simulationsphase in die realen Anwendungen im Gesundheitswesen zu bringen.
M. Lebedin,
C. V. García,
L. Spatt,
C. Ratswohl,
C. Thibeault,
L. Ostendorf,
F. Paul,
L. E. Sander,
F. Kurth and
K. Rosa,
"Discriminating promiscuous from target-specific autoantibodies in COVID-19. Eur J Immunol",
Apr.
2023.
| DOI: | 10.1002/eji.202250210 |
[en]
C. Thibeault,
L. Bardtke,
K. Vanshylla,
V. Cristanziano,
K. A. Eberhardt,
P. Stubbemann,
D. Hillus,
P. Tober-Lau,
P. Mukherjee,
F. Münn,
L. J. Lippert,
E. T. Helbig,
T. Lingscheid,
F. Steinbeis,
M. Mittermaier,
M. Witzenrath,
T. Zoller,
P. Group,
F. Klein,
L. E. Sander and
F. Kurth,
"Short- and long-term T cell and antibody responses following
dexamethasone treatment in COVID-19",
JCI Insight,
vol. 8,
no. 8,
Apr.
2023.
Abstract:
BACKGROUNDAfter its introduction as standard-of-care for severe COVID-19, dexamethasone has been administered to a large number of patients globally. Detailed knowledge of its impact on the cellular and humoral immune response to SARS-CoV-2 remains scarce.METHODSWe included immunocompetent individuals with (a) mild COVID-19, (b) severe COVID-19 before introduction of dexamethasone treatment, and (c) severe COVID-19 infection treated with dexamethasone from prospective observational cohort studies at Charité-Universit"atsmedizin Berlin, Germany. We analyzed SARS-CoV-2 spike-reactive T cells, spike-specific IgG titers, and serum neutralizing activity against B.1.1.7 and B.1.617.2 in samples ranging from 2 weeks to 6 months after infection. We also analyzed BA.2 neutralization in sera after booster immunization.RESULTSPatients with severe COVID-19 and dexamethasone treatment had lower T cell and antibody responses to SARS-CoV-2 compared with patients without dexamethasone treatment in the early phase of disease, which converged in both groups before 6 months after infection and also after immunization. Patients with mild COVID-19 had comparatively lower T cell and antibody responses than patients with severe disease, including a lower response to booster immunization during convalescence.CONCLUSIONDexamethasone treatment was associated with a short-term reduction in T cell and antibody responses in severe COVID-19 when compared with the nontreated group, but this difference evened out 6 months after infection. We confirm higher cellular and humoral immune responses in patients after severe versus mild COVID-19 and the concept of improved hybrid immunity upon immunization.FUNDINGBerlin Institute of Health, German Federal Ministry of Education, and German Federal Institute for Drugs and Medical Devices.
| DOI: | 10.1172/jci.insight.166711 |
| Pubmed: | 36881474 |
Abstract:
BACKGROUNDAfter its introduction as standard-of-care for severe COVID-19, dexamethasone has been administered to a large number of patients globally. Detailed knowledge of its impact on the cellular and humoral immune response to SARS-CoV-2 remains scarce.METHODSWe included immunocompetent individuals with (a) mild COVID-19, (b) severe COVID-19 before introduction of dexamethasone treatment, and (c) severe COVID-19 infection treated with dexamethasone from prospective observational cohort studies at Charité-Universit"atsmedizin Berlin, Germany. We analyzed SARS-CoV-2 spike-reactive T cells, spike-specific IgG titers, and serum neutralizing activity against B.1.1.7 and B.1.617.2 in samples ranging from 2 weeks to 6 months after infection. We also analyzed BA.2 neutralization in sera after booster immunization.RESULTSPatients with severe COVID-19 and dexamethasone treatment had lower T cell and antibody responses to SARS-CoV-2 compared with patients without dexamethasone treatment in the early phase of disease, which converged in both groups before 6 months after infection and also after immunization. Patients with mild COVID-19 had comparatively lower T cell and antibody responses than patients with severe disease, including a lower response to booster immunization during convalescence.CONCLUSIONDexamethasone treatment was associated with a short-term reduction in T cell and antibody responses in severe COVID-19 when compared with the nontreated group, but this difference evened out 6 months after infection. We confirm higher cellular and humoral immune responses in patients after severe versus mild COVID-19 and the concept of improved hybrid immunity upon immunization.FUNDINGBerlin Institute of Health, German Federal Ministry of Education, and German Federal Institute for Drugs and Medical Devices.
[eng]
F. Heinrich,
T. Huter,
S. Mertens,
P. Lange,
J. Vering,
A. Heinemann,
D. S. Nörz,
A. Hoffmann,
B. Ondruschka,
S. Krasemann and
M. Lütgehetmann,
"New Postmortem Perspective on Emerging SARS-CoV-2 Variants of Concern, Germany",
Emerging Infectious Diseases,
vol. 29,
no. 3,
pp. 652—656,
Mä.
2023.
Abstract:
We performed autopsies on persons in Germany who died from COVID-19 and observed higher nasopharyngeal SARS-CoV-2 viral loads for variants of concern (VOC) compared with non-VOC lineages. Pulmonary inflammation and damage appeared higher in non-VOC than VOC lineages until adjusted for vaccination status, suggesting COVID-19 vaccination may mitigate pulmonary damage.
| DOI: | 10.3201/eid2903.221297 |
Abstract:
We performed autopsies on persons in Germany who died from COVID-19 and observed higher nasopharyngeal SARS-CoV-2 viral loads for variants of concern (VOC) compared with non-VOC lineages. Pulmonary inflammation and damage appeared higher in non-VOC than VOC lineages until adjusted for vaccination status, suggesting COVID-19 vaccination may mitigate pulmonary damage.
[eng]
J. Lücke,
M. Nawrocki,
J. Schnell,
N. Meins,
F. Heinrich,
T. Zhang,
F. Bertram,
M. Sabihi,
M. Böttcher,
T. Blankenburg,
M. Pfaff,
S. Notz,
J. Kempski,
M. Reeh,
S. Wolter,
O. Mann,
J. R. Izbicki,
M. Lütgehetmann,
A. Duprée,
A. D. Giannou,
B. Ondruschka and
S. Huber,
"TNFα aggravates detrimental effects of SARS-CoV-2 infection in the liver",
Frontiers in Immunology,
vol. 14,
pp. 1151937,
Mä.
2023.
Abstract:
Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus does not only lead to pulmonary infection but can also infect other organs such as the gut, the kidney, or the liver. Recent studies confirmed that severe cases of COVID-19 are often associated with liver damage and liver failure, as well as the systemic upregulation of pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNFα). However, the impact these immune mediators in the liver have on patient survival during SARS-CoV-2 infection is currently unknown. Here, by performing a post-mortem analysis of 45 patients that died from a SARS-CoV-2 infection, we find that an increased expression of TNFA in the liver is associated with elevated mortality. Using publicly available single-cell sequencing datasets, we determined that Kupffer cells and monocytes are the main sources of this TNFα production. Further analysis revealed that TNFα signaling led to the upregulation of pro-inflammatory genes that are associated with an unfavorable outcome. Moreover, high levels of TNFA in the liver were associated with lower levels of interferon alpha and interferon beta. Thus, TNFα signaling in the infected SARS-CoV-2 liver correlates with reduced interferon levels and overall survival time.
| DOI: | 10.3389/fimmu.2023.1151937 |
Abstract:
Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus does not only lead to pulmonary infection but can also infect other organs such as the gut, the kidney, or the liver. Recent studies confirmed that severe cases of COVID-19 are often associated with liver damage and liver failure, as well as the systemic upregulation of pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNFα). However, the impact these immune mediators in the liver have on patient survival during SARS-CoV-2 infection is currently unknown. Here, by performing a post-mortem analysis of 45 patients that died from a SARS-CoV-2 infection, we find that an increased expression of TNFA in the liver is associated with elevated mortality. Using publicly available single-cell sequencing datasets, we determined that Kupffer cells and monocytes are the main sources of this TNFα production. Further analysis revealed that TNFα signaling led to the upregulation of pro-inflammatory genes that are associated with an unfavorable outcome. Moreover, high levels of TNFA in the liver were associated with lower levels of interferon alpha and interferon beta. Thus, TNFα signaling in the infected SARS-CoV-2 liver correlates with reduced interferon levels and overall survival time.
A. Surov,
M. Thormann,
H. Kardas,
M. Hinnerichs,
J. Omari,
E. Cingöz,
M. Cingöz,
M. Dursun,
İ. Kormaz,
Ç. Orhan,
Ö. Yıldız,
E. Hocaoglu,
E. Inci,
H. Önder,
H. Erk,
O. Chousein,
H. Sasani,
K. A. Gönen,
M. Pech and
A. Wienke,
"Visceral to subcutaneous fat ratio predicts short-term mortality in patients with Covid 19. A multicenter study.",
The British journal of radiology,
Mä.
2023.
Abstract:
Um die Assoziierung von Körperzusammensetzungsparametern mit Ergebnissen in Covid-19 zu bewerten, wurden 173 Patienten, die für die Covid-19-Infektion in 6 europäischen Zentren stationiert waren, in diese retrospektive Studie aufgenommen. Die Messungen wurden auf L3-Ebene durchgeführt und umfassten Skelettmuskelindex (SMI), Muskeldichte (MD) und Adipose Gewebemessungen [visceral adipose Gewebe (VAT), subkutanes Fettgewebe (SAT), intramuskuläres Fettgewebe (IMAT), visceral-to-subcutane-adipose-tissue-area-ratio (VSR)]. Die Zuordnung zur Sterblichkeit, die Notwendigkeit der Intubation (MV) und die Notwendigkeit der Zulassung zur ICU innerhalb von 30 Tagen wurden bewertet. Höhere SAT-Dichte wurde mit einem höheren Risiko von MV (OR = 1.071, 95%CI=(1.034;1.110), <i>p</i> < 0,001) verbunden. Höhere MwSt.-Dichte wurde mit der Zulassung zu ICU (OR = 1.068, 95%CI=(1.029;1.109), <i>p</i> < 0.001) verbunden. Höhere MD war ein Schutzfaktor für MV und ICU-Zulassung (OR = 0.914, 95%CI=(0.870;0.960), <i>p</i> < 0.001; OR = 0.882, 95%CI=(0.832;0.934), <i>p>/i> = 0.028). Höhere VSR war mit der Sterblichkeit verbunden (OR = 2.147, 95%CI=(1.022;4.512), <i>p</i> = 0,044). Der männliche Sex zeigte den stärksten Einfluss auf das Risiko von ICU-Zulassungen und MV. SMI wurde weder mit einem Parameter verknüpft. Bei Patienten, die für die Covid-19-Infektion stationiert sind, scheint die höhere VSR ein starker prognostizierter Faktor der kurzfristigen Sterblichkeit zu sein. Schwache Assoziationen mit klinischem Kurs wurden für MD- und Fettgewebemessungen gefunden. Männlicher Sex war der stärkste prognostische Faktor des negativen klinischen Kurses. VSR ist ein prognostizierter Biomarker für 30-tägige Mortalität bei Patienten, die für die Kovid-19-Krankheit ins Krankenhaus gebracht werden.
| DOI: | 10.1259/bjr.20220869 |
Abstract:
Um die Assoziierung von Körperzusammensetzungsparametern mit Ergebnissen in Covid-19 zu bewerten, wurden 173 Patienten, die für die Covid-19-Infektion in 6 europäischen Zentren stationiert waren, in diese retrospektive Studie aufgenommen. Die Messungen wurden auf L3-Ebene durchgeführt und umfassten Skelettmuskelindex (SMI), Muskeldichte (MD) und Adipose Gewebemessungen [visceral adipose Gewebe (VAT), subkutanes Fettgewebe (SAT), intramuskuläres Fettgewebe (IMAT), visceral-to-subcutane-adipose-tissue-area-ratio (VSR)]. Die Zuordnung zur Sterblichkeit, die Notwendigkeit der Intubation (MV) und die Notwendigkeit der Zulassung zur ICU innerhalb von 30 Tagen wurden bewertet. Höhere SAT-Dichte wurde mit einem höheren Risiko von MV (OR = 1.071, 95%CI=(1.034;1.110), <i>p</i> < 0,001) verbunden. Höhere MwSt.-Dichte wurde mit der Zulassung zu ICU (OR = 1.068, 95%CI=(1.029;1.109), <i>p</i> < 0.001) verbunden. Höhere MD war ein Schutzfaktor für MV und ICU-Zulassung (OR = 0.914, 95%CI=(0.870;0.960), <i>p</i> < 0.001; OR = 0.882, 95%CI=(0.832;0.934), <i>p>/i> = 0.028). Höhere VSR war mit der Sterblichkeit verbunden (OR = 2.147, 95%CI=(1.022;4.512), <i>p</i> = 0,044). Der männliche Sex zeigte den stärksten Einfluss auf das Risiko von ICU-Zulassungen und MV. SMI wurde weder mit einem Parameter verknüpft. Bei Patienten, die für die Covid-19-Infektion stationiert sind, scheint die höhere VSR ein starker prognostizierter Faktor der kurzfristigen Sterblichkeit zu sein. Schwache Assoziationen mit klinischem Kurs wurden für MD- und Fettgewebemessungen gefunden. Männlicher Sex war der stärkste prognostische Faktor des negativen klinischen Kurses. VSR ist ein prognostizierter Biomarker für 30-tägige Mortalität bei Patienten, die für die Kovid-19-Krankheit ins Krankenhaus gebracht werden.
[en]
R. Mothes,
A. Pascual-Reguant,
R. Koehler,
J. Liebeskind,
A. Liebheit,
S. Bauherr,
L. Philipsen,
C. Dittmayer,
M. Laue,
R. Von Manitius,
S. Elezkurtaj,
P. Durek,
F. Heinrich,
G. A. Heinz,
G. M. Guerra,
B. Obermayer,
J. Meinhardt,
J. Ihlow,
J. Radke,
F. L. Heppner,
P. Enghard,
H. Stockmann,
T. Aschman,
J. Schneider,
V. Corman,
L. E. Sander,
M. Mashreghi,
T. Conrad,
A. Hocke,
R. A. Niesner,
H. Radbruch and
A. E. Hauser,
"Distinct tissue niches direct lung immunopathology via CCL18 and CCL21 in severe COVID-19",
Nature Communications,
vol. 14,
no. 1,
pp. 791,
Feb.
2023.
Abstract:
Abstract Prolonged lung pathology has been associated with COVID-19, yet the cellular and molecular mechanisms behind this chronic inflammatory disease are poorly understood. In this study, we combine advanced imaging and spatial transcriptomics to shed light on the local immune response in severe COVID-19. We show that activated adventitial niches are crucial microenvironments contributing to the orchestration of prolonged lung immunopathology. Up-regulation of the chemokines CCL21 and CCL18 associates to endothelial-to-mesenchymal transition and tissue fibrosis within these niches. CCL21 over-expression additionally links to the local accumulation of T cells expressing the cognate receptor CCR7. These T cells are imprinted with an exhausted phenotype and form lymphoid aggregates that can organize in ectopic lymphoid structures. Our work proposes immune-stromal interaction mechanisms promoting a self-sustained and non-resolving local immune response that extends beyond active viral infection and perpetuates tissue remodeling.
| DOI: | 10.1038/s41467-023-36333-2 |
| Datei: | https://www.nature.com/articles/s41467-023-36333-2 |
Abstract:
Abstract Prolonged lung pathology has been associated with COVID-19, yet the cellular and molecular mechanisms behind this chronic inflammatory disease are poorly understood. In this study, we combine advanced imaging and spatial transcriptomics to shed light on the local immune response in severe COVID-19. We show that activated adventitial niches are crucial microenvironments contributing to the orchestration of prolonged lung immunopathology. Up-regulation of the chemokines CCL21 and CCL18 associates to endothelial-to-mesenchymal transition and tissue fibrosis within these niches. CCL21 over-expression additionally links to the local accumulation of T cells expressing the cognate receptor CCR7. These T cells are imprinted with an exhausted phenotype and form lymphoid aggregates that can organize in ectopic lymphoid structures. Our work proposes immune-stromal interaction mechanisms promoting a self-sustained and non-resolving local immune response that extends beyond active viral infection and perpetuates tissue remodeling.
J. Kottlors,
P. Fervers,
S. Geißen,
R. J. Gertz,
J. Bremm,
M. Rinneburger,
M. Weisthoff,
R. Shahzad,
D. Maintz and
T. Persigehl,
"Morphological appearance of the B.1.1.7 mutation of the novel coronavirus 2 (SARS-CoV-2) in chest CT.",
Quantitative imaging in medicine and surgery,
Feb.
2023.
Abstract:
Die Diagnose einer Coronavirus-Krankheit 2019 (COVID-19)-Infektion mit hoher Spezifität in der Brust-Computertomographie (CT)-Bildgebung wird aufgrund von markanten Abbildungsmerkmalen der COVID-19-Pneumonie als möglich angesehen. Da andere virale nicht-COVID-Pneumonie meist ein anderes Verteilungsmuster zeigen, ist es sinnvoll, davon auszugehen, dass die durch das neuartige schwere akute Atemsyndrom Coronavirus 2 (SARS-CoV-2) beobachteten Muster eine Folge ihrer genetisch kodierten molekularen Eigenschaften bei der Interaktion mit dem Atemgewebe sind. Da im Laufe von 2021 mehr Mutationen des anfänglichen SARS-CoV-2 Wildtyps mit unterschiedlicher Aggressivität nachgewiesen wurden, wurde deutlich, dass sein Genom in einem Transformationszustand ist und somit eine mögliche Modifikation des spezifischen morphologischen Erscheinungsbildes in CT auftreten kann. Ziel dieser Studie war es, die morphologischen Unterschiede der SARS-CoV-2-B.1.1.7 Mutation und Wildtypvariante in CT-Scans des Thorax quantitativ zu analysieren. Wir analysierten einen Datensatz von 140 Patienten, der in Pneumonien unterteilt wurde, die durch n=40 Wildtypvarianten, n=40 B.1.1.7 Varianten, n=20 Bakterienpneumonien, n=20 virale (non-COVID) Pneumonien und eine Testgruppe von n=20 unremarkable CT-Untersuchungen des Thorax verursacht wurden. Zur Quantifizierung der Lungenwege wurde eine semiautomatisierte 3D-Segmentierung des Lungengewebes durchgeführt. Das Ausmaß, das Verhältnis und die spezifische Verteilung des entzündlich betroffenen Lungengewebes in jeder Gruppe wurden in einer multivariaten Gruppenanalyse verglichen. Die Lung-Segmentierung ergab einen signifikanten Unterschied zwischen dem Ausmaß der Bodenglasundurchlässigkeiten (GGO) oder dem Konsolidierungsvergleich von SARS-CoV-2 Wildtyp und B.1.1.7 Variante. Wildtyp und B.1.1.7 Variante zeigten sowohl ein symmetrisches Verteilungsmuster stufenabhängiger GGO als auch eine Konsolidierung in aufeinander abgestimmten COVID-19-Stufen. Virale nicht-COVID Pneumonien hatten deutlich weniger Konsolidierungen als die Bakterien, aber auch als die COVID-19 B.1.1.7 Variantengruppen. Die CT-basierte Segmentierung zeigte keinen signifikanten Unterschied zwischen dem morphologischen Erscheinungsbild der COVID-19 Wildtyp-Variante und der SARS-CoV-2 B.1.1.7 Mutation. Unser Ansatz erlaubte jedoch eine halbautomatische Quantifizierung von bakteriellen und viralen Lungenerkrankungen. Quantitative CT-Bildanalysen, wie die vorgestellte, scheinen eine wichtige Komponente der Pandemievorbereitung in Anbetracht eines Organismus mit anhaltender genetischer Veränderung zu sein, um eine potenziell auftretende Veränderung des CT-morphologischen Erscheinungsbildes möglicher neuer ankommender COVID-19-Varianten zu beschreiben.
| DOI: | 10.21037/qims-22-718 |
Abstract:
Die Diagnose einer Coronavirus-Krankheit 2019 (COVID-19)-Infektion mit hoher Spezifität in der Brust-Computertomographie (CT)-Bildgebung wird aufgrund von markanten Abbildungsmerkmalen der COVID-19-Pneumonie als möglich angesehen. Da andere virale nicht-COVID-Pneumonie meist ein anderes Verteilungsmuster zeigen, ist es sinnvoll, davon auszugehen, dass die durch das neuartige schwere akute Atemsyndrom Coronavirus 2 (SARS-CoV-2) beobachteten Muster eine Folge ihrer genetisch kodierten molekularen Eigenschaften bei der Interaktion mit dem Atemgewebe sind. Da im Laufe von 2021 mehr Mutationen des anfänglichen SARS-CoV-2 Wildtyps mit unterschiedlicher Aggressivität nachgewiesen wurden, wurde deutlich, dass sein Genom in einem Transformationszustand ist und somit eine mögliche Modifikation des spezifischen morphologischen Erscheinungsbildes in CT auftreten kann. Ziel dieser Studie war es, die morphologischen Unterschiede der SARS-CoV-2-B.1.1.7 Mutation und Wildtypvariante in CT-Scans des Thorax quantitativ zu analysieren. Wir analysierten einen Datensatz von 140 Patienten, der in Pneumonien unterteilt wurde, die durch n=40 Wildtypvarianten, n=40 B.1.1.7 Varianten, n=20 Bakterienpneumonien, n=20 virale (non-COVID) Pneumonien und eine Testgruppe von n=20 unremarkable CT-Untersuchungen des Thorax verursacht wurden. Zur Quantifizierung der Lungenwege wurde eine semiautomatisierte 3D-Segmentierung des Lungengewebes durchgeführt. Das Ausmaß, das Verhältnis und die spezifische Verteilung des entzündlich betroffenen Lungengewebes in jeder Gruppe wurden in einer multivariaten Gruppenanalyse verglichen. Die Lung-Segmentierung ergab einen signifikanten Unterschied zwischen dem Ausmaß der Bodenglasundurchlässigkeiten (GGO) oder dem Konsolidierungsvergleich von SARS-CoV-2 Wildtyp und B.1.1.7 Variante. Wildtyp und B.1.1.7 Variante zeigten sowohl ein symmetrisches Verteilungsmuster stufenabhängiger GGO als auch eine Konsolidierung in aufeinander abgestimmten COVID-19-Stufen. Virale nicht-COVID Pneumonien hatten deutlich weniger Konsolidierungen als die Bakterien, aber auch als die COVID-19 B.1.1.7 Variantengruppen. Die CT-basierte Segmentierung zeigte keinen signifikanten Unterschied zwischen dem morphologischen Erscheinungsbild der COVID-19 Wildtyp-Variante und der SARS-CoV-2 B.1.1.7 Mutation. Unser Ansatz erlaubte jedoch eine halbautomatische Quantifizierung von bakteriellen und viralen Lungenerkrankungen. Quantitative CT-Bildanalysen, wie die vorgestellte, scheinen eine wichtige Komponente der Pandemievorbereitung in Anbetracht eines Organismus mit anhaltender genetischer Veränderung zu sein, um eine potenziell auftretende Veränderung des CT-morphologischen Erscheinungsbildes möglicher neuer ankommender COVID-19-Varianten zu beschreiben.