First report from the German COVID-19 autopsy registry

BACKGROUND: Autopsies are an important tool in medicine, dissecting disease pathophysiology and causes of death. In COVID-19, autopsies revealed e.g., the effects on pulmonary (micro)vasculature or the nervous system, systemic viral spread, or the interplay with the immune system. To facilitate multicentre autopsy-based studies and provide a central hub supporting autopsy centres, researchers, and data analyses and reporting, in April 2020 the German COVID-19 Autopsy Registry (DeRegCOVID) was launched. METHODS: The electronic registry uses a web-based electronic case report form. Participation is voluntary and biomaterial remains at the respective site (decentralized biobanking). As of October 2021, the registry included N=1129 autopsy cases, with 69271 single data points including information on 18674 available biospecimens gathered from 29 German sites. FINDINGS: In the N=1095 eligible records, the male-to-female ratio was 1·8:1, with peaks at 65-69 and 80-84 years in males and >85 years in females. The analysis of the chain of events directly leading to death revealed COVID-19 as the underlying cause of death in 86% of the autopsy cases, whereas in 14% COVID-19 was a concomitant disease. The most common immediate cause of death was diffuse alveolar damage, followed by multi-organ failure. The registry supports several scientific projects, public outreach and provides reports to the federal health authorities, leading to legislative adaptation of the German Infection Protection Act, facilitating the performance of autopsies during pandemics. INTERPRETATION: A national autopsy registry can provide multicentre quantitative information on COVID-19 deaths on a national level, supporting medical research, political decision-making and public discussion. FUNDING: German Federal Ministries of Education and Research and Health. Hintergrund: Obduktionen sind ein wichtiges Instrument in der Medizin, um die Pathophysiologie von Krankheiten und Todesursachen zu untersuchen. Im Rahmen von COVID-19 wurden durch Obduktionen z.B. die Auswirkungen auf die pulmonale Mikrovaskulatur, das Nervensystem, die systemische Virusausbreitung, und das Zusammenspiel mit dem Immunsystem untersucht. Um multizentrische, auf Obduktionen basierende Studien zu erleichtern und eine zentrale Anlaufstelle zu schaffen, die Obduktionszentren, Forscher sowie Datenanalysen und -berichte unterstützt, wurde im April 2020 das deutsche COVID-19-Autopsieregister (DeRegCOVID) ins Leben gerufen. Methoden: Das elektronische Register verwendet ein webbasiertes elektronisches Fallberichtsformular. Die Teilnahme ist freiwillig und das Biomaterial verbleibt am jeweiligen Standort (dezentrales Biobanking). Im Oktober 2021 umfasste das Register N=1129 Obduktionsfälle mit 69271 einzelnen Datenpunkten, die Informationen über 18674 verfügbare Bioproben enthielten, die von 29 deutschen Standorten gesammelt wurden. Ergebnisse: In den N=1095 ausgewerteten Datensätzen betrug das Verhältnis von Männern zu Frauen 1,8:1 mit Spitzenwerten bei 65-69 und 80-84 Jahren bei Männern und >85 Jahren bei Frauen. Die Analyse der Sequenz der unmittelbar zum Tod führenden Ereignisse ergab, dass in 86 % der Obduktionsfälle COVID-19 die zugrunde liegende Todesursache war, während in 14 % der Fälle COVID-19 eine Begleiterkrankung war. Die häufigste unmittelbare Todesursache war der diffuse Alveolarschaden, gefolgt von Multiorganversagen. Das Register unterstützt mehrere wissenschaftliche Projekte, die Öffentlichkeitsarbeit und liefert Berichte an die Bundesgesundheitsbehörden, was zu einer Anpassung des deutschen Infektionsschutzgesetzes führte und die Durchführung von Obduktionen in Pandemien erleichtert. Interpretation: Ein nationales Obduktionsregister kann multizentrische quantitative Informationen über COVID-19-Todesfälle auf nationaler Ebene liefern und damit die medizinische Forschung, die politische Entscheidungsfindung und die öffentliche Diskussion unterstützen. Finanzierung: Bundesministerien für Bildung und Forschung und für Gesundheit

Intracranial hemorrhage in COVID-19 patients during extracorporeal membrane oxygenation for acute respiratory failure: a nationwide register study report

BACKGROUND: In severe cases, SARS-CoV-2 infection leads to acute respiratory distress syndrome (ARDS), often treated by extracorporeal membrane oxygenation (ECMO). During ECMO therapy, anticoagulation is crucial to prevent device-associated thrombosis and device failure, however, it is associated with bleeding complications. In COVID-19, additional pathologies, such as endotheliitis, may further increase the risk of bleeding complications. To assess the frequency of bleeding events, we analyzed data from the German COVID-19 autopsy registry (DeRegCOVID). METHODS: The electronic registry uses a web-based electronic case report form. In November 2021, the registry included N = 1129 confirmed COVID-19 autopsy cases, with data on 63 ECMO autopsy cases and 1066 non-ECMO autopsy cases, contributed from 29 German sites. FINDINGS: The registry data showed that ECMO was used in younger male patients and bleeding events occurred much more frequently in ECMO cases compared to non-ECMO cases (56% and 9%, respectively). Similarly, intracranial bleeding (ICB) was documented in 21% of ECMO cases and 3% of non-ECMO cases and was classified as the immediate or underlying cause of death in 78% of ECMO cases and 37% of non-ECMO cases. In ECMO cases, the three most common immediate causes of death were multi-organ failure, ARDS and ICB, and in non-ECMO cases ARDS, multi-organ failure and pulmonary bacterial ± fungal superinfection, ordered by descending frequency. INTERPRETATION: Our study suggests the potential value of autopsies and a joint interdisciplinary multicenter (national) approach in addressing fatal complications in COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-022-03945-x

Identifikation und Charakterisierung renaler Keratine in Nierenerkrankungen

Acute and chronic kidney injury have become global healthcare burdens with high morbidity and mortality rates. Early diagnosis and preventive treatment halting progress of renal insufficiency are thus needed. Keratins (K) are useful and widely used epithelial cell-specific disease biomarkers, e.g. in diagnostic pathology as tissue markers for cancer or as circulating biomarkers of neoplastic and non-neoplastic diseases. In the kidney K7, K8, K18 and K19 were previously described. We could show that these keratins are significantly and progressively upregulated in renal disease with tubular cell injury, independently of the underlying pathomechanism. We showed, that these keratins can be used as markers of tubular epithelial cell stress on tissue and in urine. The described keratins were not upregulated in an expected equimolar ratio, which hinted to possible other keratins being expressed in kidney diseases. Reanalysis of publicly available microarray data revealed K10, K14 and K17 as potentially expressed in the kidney. Expression of those three keratins was validated using RT-PCR and Western Blot.K10 was downregulated in a model of renal fibrosis, representing the first keratin in the kidney to be downregulated in response to renal injury. K14 showed a low expression in healthy kidneys and was progressively upregulated during disease similarly to the other keratins.K17 was de novo expressed during disease, independently of the underlying injury: with only single K17-positive cells in the beginning, continuously expanding to entire K17-positive tubular crosssections in advanced disease stages. In contrast to the main keratins, K17 did not colocalize with NGAL, a well-recognized marker of tubular epithelial cell injury. Thus, K17 seems to be associated with renal injury, but does not mark injured tubular epithelial cells. Further studies are needed to evaluate the nature of K17-positive cells, before it can be evaluated as a possible biomarker

Identifikation und Charakterisierung renaler Keratine in Nierenerkrankungen

Acute and chronic kidney injury have become global healthcare burdens with high morbidity and mortality rates. Early diagnosis and preventive treatment halting progress of renal insufficiency are thus needed. Keratins (K) are useful and widely used epithelial cell-specific disease biomarkers, e.g. in diagnostic pathology as tissue markers for cancer or as circulating biomarkers of neoplastic and non-neoplastic diseases. In the kidney K7, K8, K18 and K19 were previously described. We could show that these keratins are significantly and progressively upregulated in renal disease with tubular cell injury, independently of the underlying pathomechanism. We showed, that these keratins can be used as markers of tubular epithelial cell stress on tissue and in urine. The described keratins were not upregulated in an expected equimolar ratio, which hinted to possible other keratins being expressed in kidney diseases. Reanalysis of publicly available microarray data revealed K10, K14 and K17 as potentially expressed in the kidney. Expression of those three keratins was validated using RT-PCR and Western Blot.K10 was downregulated in a model of renal fibrosis, representing the first keratin in the kidney to be downregulated in response to renal injury. K14 showed a low expression in healthy kidneys and was progressively upregulated during disease similarly to the other keratins.K17 was de novo expressed during disease, independently of the underlying injury: with only single K17-positive cells in the beginning, continuously expanding to entire K17-positive tubular crosssections in advanced disease stages. In contrast to the main keratins, K17 did not colocalize with NGAL, a well-recognized marker of tubular epithelial cell injury. Thus, K17 seems to be associated with renal injury, but does not mark injured tubular epithelial cells. Further studies are needed to evaluate the nature of K17-positive cells, before it can be evaluated as a possible biomarker

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney's glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN's pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex 'big data,' requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach

Autopsy registry can facilitate COVID‐19 research

The WHO declared the global outbreak of SARS‐CoV‐2 a pandemic on March 11, 2020, and “call(ed) on all countries to exchange country experiences and practices in a transparent and timely way” (http://www.euro.who.int/en/health-topics/health-emergencies/pages/news/news/2020/03/who-announces-covid-19-outbreak-a-pandemic). To date, many medical societies have announced their intention to collect and analyze data from COVID‐19 patients and some large‐scale prospective data collections are already running, such as the LEOSS registry (Lean European Open Survey on SARS‐CoV‐2 Infected Patients) or the CAPACITYCOVID registry (registry of patients with COVID‐19 including cardiovascular risk and complications). The necessity to mobilize and harmonize basic and applied research worldwide is of utmost importance (Sansonetti, 2020)

Tackling stain variability using CycleGAN-based stain augmentation

Background: Considerable inter- and intra-laboratory stain variability exists in pathology, representing a challenge in development and application of deep learning (DL) approaches. Since tackling all sources of stain variability with manual annotation is not feasible, we here investigated and compared unsupervised DL approaches to reduce the consequences of stain variability in kidney pathology. Methods: We aimed to improve the applicability of a pretrained DL segmentation model to 3 external multi-centric cohorts with large stain variability. In contrast to the traditional approach of training generative adversarial networks (GAN) for stain normalization, we here propose to tackle stain variability by data augmentation. We augment the training data of the pretrained model by the stain variability using CycleGANs and then retrain the model on the stain-augmented dataset. We compared the performance of i/ the unmodified pretrained segmentation model with ii/ CycleGAN-based stain normalization, iii/ a feature-preserving modification to ii/ for improved normalization, and iv/ the proposed stain-augmented model. Results: The proposed stain-augmented model showed highest mean segmentation accuracy in all external cohorts and maintained comparable performance on the training cohort. However, the increase in performance was only marginal compared to the pretrained model. CycleGAN-based stain normalization suffered from encoded imperceptible information into the normalizations that confused the pretrained model and thus resulted in slightly worse performance. Conclusions: Our findings suggest that stain variability can be tackled more effectively by augmenting data by it than by following the commonly used approach of normalizing the stain. However, the applicability of this approach providing only a rather slight performance increase has to be weighted against an additional carbon footprint

tRigon: an R package and Shiny App for integrative (path-)omics data analysis

Abstract Background Pathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation we developed tRigon (Toolbox foR InteGrative (path-)Omics data aNalysis), a Shiny application for fast, comprehensive and reproducible pathomics analysis. Results tRigon is available via the CRAN repository ( https://cran.r-project.org/web/packages/tRigon ) with its source code available on GitLab ( https://git-ce.rwth-aachen.de/labooratory-ai/trigon ). The tRigon package can be installed locally and its application can be executed from the R console via the command ‘tRigon::run_tRigon()’. Alternatively, the application is hosted online and can be accessed at https://labooratory.shinyapps.io/tRigon . We show fast computation of small, medium and large datasets in a low- and high-performance hardware setting, indicating broad applicability of tRigon. Conclusions tRigon allows researchers without coding abilities to perform exploratory feature analyses of pathomics and non-pathomics datasets on their own using a variety of hardware