AI Insight
Researchers analyzed electronic health records from approximately 1.5 million patients in UK primary care and Danish hospitals to identify real-world associations between medications and diagnoses. From 736,000 drug-diagnosis co-occurrences, they identified 7,763 statistically significant pairs with strong associations (odds ratios ≥3.5), which were then manually reviewed by six independent clinicians to determine whether drugs were directly treating the associated diagnoses. The study found that most statistically significant drug-diagnosis associations in healthcare records do not represent direct treatment relationships, highlighting the complexity of real-world prescription patterns and polypharmacy.
Why it matters
This curated resource addresses a critical gap in drug repositioning research by providing clinically validated drug-diagnosis relationships mapped to standard medical coding systems (ATC and ICD-10) actually used in healthcare settings. The findings emphasize that researchers cannot simply assume statistical associations in health records represent treatment relationships, which has important implications for analyzing prescription patterns, identifying drug repurposing opportunities, and understanding polypharmacy in clinical practice.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
As the use of electronic health records in drug repositioning research increases, so does the need for a well-curated resource describing real-world drug-diagnosis relationships. This need is particularly important in the context of polypharmacy. Although literature-based drug-disease maps exist, they are typically based on mechanistic disease ontologies, which are not widely used in clinical settings and do not align well with the ICD system, which is most often used in healthcare. Here, we used real-world primary and secondary healthcare data from approximately 1.5 million individuals to identify drug-diagnosis co-occurrences (~736,000 pairs), significant associations (7,763 pairs), and assess direct drug usage through medical expert curation. The final resource comprises 7,763 associations with odds ratios [≥]3.5, manually annotated by six independent clinicians (3 in the UK and 3 in Denmark). Finally, the clinician annotations were scored using an Expectation-Maximization-based framework providing a confidence score for each pair. Our resource shows that the vast majority of significantly associated drugs and diagnoses in healthcare records are not due to direct treatment of the diagnosis. Additionally, through the annotation results, we demonstrate the importance of accounting for systematic differences among annotators when working with real-world data.