Background: With the growing impact of observational research studies, there is also a growing focus on data quality (DQ). As opposed to experimental study designs, observational research studies are performed using data mostly collected in a non-research context (secondary use). Depending on the number of data elements to be analyzed, DQ reports of data stored within research networks can grow very large. They might be cumbersome to read and important information could be overseen quickly. To address this issue, a DQ assessment (DQA) tool with a graphical user interface (GUI) was developed and provided as a web application.Methods: The aim was to provide an easy-to-use interface for users without prior programming knowledge to carry out DQ checks and to present the results in a clearly structured way. This interface serves as a starting point for a more detailed investigation of possible DQ irregularities. A user-centered development process ensured the practical feasibility of the interactive GUI. The interface was implemented in the R programming language and aligned to Kahn et al.’s DQ categories conformance, completeness and plausibility.Results: With DQAgui, an R package with a web-app frontend for DQ assessment was developed. The GUI allows users to perform DQ analyses of tabular data sets and to systematically evaluate the results. During the development of the GUI, additional features were implemented, such as analyzing a subset of the data by defining time periods and restricting the analyses to certain data elements.Conclusions: As part of the MIRACUM project, DQAgui is now being used at ten German university hospitals for DQ assessment and to provide a central overview of the availability of important data elements in a datamap over 2 years. Future development efforts should focus on design optimization and include a usability evaluation.

Background: The objective of this IRB-approved retrospective monocentric study was to identify risk factors for mortality after surgery for congenital heart defects (CHDs) in pediatric patients using machine learning (ML). CHD belongs to the most common congenital malformations, and remains the leading mortality cause from birth defects. Methods: The most recent available hospital encounter for each patient with an age <18 years hospitalized for CHD-related cardiac surgery between the years 2011 and 2020 was included in this study. The cohort consisted of 1302 eligible patients (mean age [SD]: 402.92 [±562.31] days), who were categorized into four disease groups. A random survival forest (RSF) and the ‘eXtreme Gradient Boosting’ algorithm (XGB) were applied to model mortality (incidence: 5.6% [n = 73 events]). All models were then applied to predict the outcome in an independent holdout test dataset (40% of the cohort). Results: RSF and XGB achieved average C-indices of 0.85 (±0.01) and 0.79 (±0.03), respectively. Feature importance was assessed with ‘SHapley Additive exPlanations’ (SHAP) and ‘Time-dependent explanations of machine learning survival models’ (SurvSHAP(t)), both of which revealed high importance of the maximum values of serum creatinine observed within 72 h post-surgery for both ML methods. Conclusions: ML methods, along with model explainability tools, can reveal interesting insights into mortality risk after surgery for CHD. The proposed analytical workflow can serve as a blueprint for translating the analysis into a federated setting that builds upon the infrastructure of the German Medical Informatics Initiative.

The use of electronic health records for clinical research offers access to large-scale real-world data, but it requires the accurate transformation of data across clinical data repositories. In this study, we evaluate the data quality and completeness in three repositories (DWH, FHIR, and TriNetX) at Erlangen University Hospital. Key data elements (diagnosis, procedure, and laboratory codes) were analyzed, alongside a specific research question. Our results show good overall consistency, but discrepancies arise due to differences in code systems, data filtering, and the mapping process. These findings highlight the importance of critically assessing data provenance and the transformation processes when conducting multicenter research. Understanding the strengths and limitations of each repository is essential for ensuring high-quality research outcomes.

Reproducibility imposes some special requirements at different stages of each project, including reproducible workflows for the analysis including to follow best practices regarding code style and to make the creation of the manuscript reproducible as well. Available tools therefore include version control systems such as Git and document creation tools such as Quarto or R Markdown. However, a re-usable project template mapping the entire process from performing the data analysis to finally writing the manuscript in a reproducible manner is yet lacking. This work aims to fill this gap by presenting an open source template for conducting reproducible research projects utilizing a containerized framework for both developing and conducting the analysis and summarizing the results in a manuscript. This template can be used instantly without any customization.