[Development and progress of the National Autopsy Network (NATON)]

BACKGROUND

Autopsies have long been considered the gold standard for quality assurance in medicine, yet their significance in basic research has been relatively overlooked. The COVID-19 pandemic underscored the potential of autopsies in understanding pathophysiology, therapy, and disease management. In response, the German Registry for COVID-19 Autopsies (DeRegCOVID) was established in April 2020, followed by the DEFEAT PANDEMIcs consortium (2020-2021), which evolved into the National Autopsy Network (NATON).

DEREGCOVID

DeRegCOVID collected and analyzed autopsy data from COVID-19 deceased in Germany over three years, serving as the largest national multicenter autopsy study. Results identified crucial factors in severe/fatal cases, such as pulmonary vascular thromboemboli and the intricate virus-immune interplay. DeRegCOVID served as a central hub for data analysis, research inquiries, and public communication, playing a vital role in informing policy changes and responding to health authorities.

NATON

Initiated by the Network University Medicine (NUM), NATON emerged as a sustainable infrastructure for autopsy-based research. NATON aims to provide a data and method platform, fostering collaboration across pathology, neuropathology, and legal medicine. Its structure supports a swift feedback loop between research, patient care, and pandemic management.

CONCLUSION

DeRegCOVID has significantly contributed to understanding COVID-19 pathophysiology, leading to the establishment of NATON. The National Autopsy Registry (NAREG), as its successor, embodies a modular and adaptable approach, aiming to enhance autopsy-based research collaboration nationally and, potentially, internationally.

Establishing a Data Quality Baseline in the AKTIN Emergency Department Data Registry - A Secondary Use Perspective

Secondary use of clinical data is an increasing application that is affected by the data quality (DQ) of its source systems. Techniques such as audits and risk-based monitoring for controlling DQ often rely on source data verification (SDV). SDV requires access to data generating systems. We present an approach to a targeted SDV based on manual input and synthetic data that is applicable in low resource settings with restricted system access. We deployed the protocol in the DQ management of the AKTIN Emergency Department Data Registry. Our targeted approach has shown to be feasible to form a DQ baseline that can be used for different DQ monitoring processes such as the identification of different error sources.

A Federated and Distributed Data Management Infrastructure to Enable Public Health Surveillance from Intensive Care Unit Data

The Robert Koch Institute (RKI) monitors the actual number of COVID-19 patients requiring intensive care from aggregated data reported by hospitals in Germany. So far, there is no infrastructure to make use of individual patient-level data from intensive care units for public health surveillance. Adopting concepts and components of the already established AKTIN Emergency Department Data registry, we implemented the prototype of a federated and distributed research infrastructure giving the RKI access to patient-level intensive care data.