Real-World Cardiovascular Research Using the German IQVIA Disease Analyzer Database: Methods, Evidence, and Limitations (2000-2025).

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide. This increases the demand for real-world evidence to complement findings from randomized controlled trials. The German IQVIA Disease Analyzer (DA) database, which is populated with anonymized electronic medical records from general practitioners and specialists, has become an increasingly valuable source for cardiovascular research. Over the past two decades, and especially between 2020 and 2025, numerous epidemiological studies have used this database to explore associations between cardiovascular risk factors, comorbidities, therapeutic patterns, and cardiovascular outcomes in large, broadly representative outpatient populations. This review synthesizes evidence from 13 selected DA-based studies examining atrial fibrillation, heart failure, cardiometabolic disease, lipid management, non-alcoholic fatty liver disease (NAFLD)-related cardiovascular risks, cerebrovascular complications, COVID-19-associated vascular events, and modifiable behavioral and anthropometric factors. These studies were selected based on predefined criteria including cardiovascular relevance, methodological rigor, large sample size, and representativeness of key disease domains across the 2000-2025 period. Eligible studies were identified through targeted searches of peer-reviewed literature using the German IQVIA Disease Analyzer database and were selected to reflect major cardiovascular disease domains, risk factors, and therapeutic areas. Across disease domains, the reviewed studies consistently demonstrate the DA database's capacity to identify reproducible associations between cardiometabolic risk factors, comorbidities, and cardiovascular outcomes in routine outpatient care. While causal inference is not possible, the database enables the identification of clinically meaningful associations that inform hypothesis generation, help quantify disease burden, and highlight gaps in prevention or treatment. The database's strengths include large sample sizes (often exceeding 100,000 patients), long follow-up periods, and high external validity, while limitations relate to coding accuracy, residual confounding, and the absence of detailed clinical measures. Collectively, the evidence underscores the importance of the DA database as a crucial platform for real-world cardiovascular research.