23 research outputs found
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 unterstĂŒtzt, 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 ObduktionsfĂ€lle mit 69271 einzelnen Datenpunkten, die Informationen ĂŒber 18674 verfĂŒgbare Bioproben enthielten, die von 29 deutschen Standorten gesammelt wurden. Ergebnisse: In den N=1095 ausgewerteten DatensĂ€tzen betrug das VerhĂ€ltnis von MĂ€nnern zu Frauen 1,8:1 mit Spitzenwerten bei 65-69 und 80-84 Jahren bei MĂ€nnern und >85 Jahren bei Frauen. Die Analyse der Sequenz der unmittelbar zum Tod fĂŒhrenden Ereignisse ergab, dass in 86 % der ObduktionsfĂ€lle COVID-19 die zugrunde liegende Todesursache war, wĂ€hrend in 14 % der FĂ€lle COVID-19 eine Begleiterkrankung war. Die hĂ€ufigste unmittelbare Todesursache war der diffuse Alveolarschaden, gefolgt von Multiorganversagen. Das Register unterstĂŒtzt mehrere wissenschaftliche Projekte, die Ăffentlichkeitsarbeit und liefert Berichte an die Bundesgesundheitsbehörden, was zu einer Anpassung des deutschen Infektionsschutzgesetzes fĂŒhrte und die DurchfĂŒhrung von Obduktionen in Pandemien erleichtert. Interpretation: Ein nationales Obduktionsregister kann multizentrische quantitative Informationen ĂŒber COVID-19-TodesfĂ€lle auf nationaler Ebene liefern und damit die medizinische Forschung, die politische Entscheidungsfindung und die öffentliche Diskussion unterstĂŒtzen. Finanzierung: Bundesministerien fĂŒr Bildung und Forschung und fĂŒr 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 Nâ=â1129 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 bacterialâ±âfungal 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
Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)
Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.</p
Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)
Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.</p
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 COVIDâ19 research
The WHO declared the global outbreak of SARSâCoVâ2 a pandemic on March 11, 2020, and âcall(ed) on all countries to exchange country experiences and practices in a transparent and timely wayâ (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 COVIDâ19 patients and some largeâscale prospective data collections are already running, such as the LEOSS registry (Lean European Open Survey on SARSâCoVâ2 Infected Patients) or the CAPACITYCOVID registry (registry of patients with COVIDâ19 including cardiovascular risk and complications). The necessity to mobilize and harmonize basic and applied research worldwide is of utmost importance (Sansonetti, 2020)