Abstract / Summary
Objective: To assess the accuracy of codes and algorithms used to identify selected connective tissue diseases (CTDs) in electronic health records (EHRs) and administrative databases. Methods: We searched MEDLINE, Embase, and CENTRAL databases for studies that validated case definitions in EHRs against a reference standard, including rheumatologist-confirmed diagnosis or clinical classification criteria. Title/abstract screening, full-text review, data extraction, and quality appraisal were independently performed in duplicate. Findings were synthesised narratively and through a bivariate random-effects meta-analysis of sensitivity and specificity. Results: A total of 38 studies were included. Systemic lupus erythematosus (SLE) was the most frequently studied disease, followed by systemic sclerosis (SSc). Across diseases, progressively restrictive algorithms generally improved positive predictive value (PPV), although the optimal strategy varied by disease. In SLE, multiple diagnostic codes provided the most favorable overall performance, whereas adding clinical data provided limited incremental benefit. In SSc, disease-specific clinical features, particularly Raynaud's phenomenon, improved case identification, while specialist or inpatient-based algorithms performed better for polymyositis/dermatomyositis, although evidence was limited. For SLE, meta-analysis yielded pooled sensitivity of 0.90 (95% Cis 0.70-0.97), specificity of 0.83 (95% CIs 0.54-0.95), and PPV of 0.75 (95% CIs 0.59-0.86). Evidence for Sjogren's Disease was limited to one study, and no studies evaluated MCTD/UCTD. Conclusions: Optimal EHR case-identification strategies vary by disease and should be tailored to disease-specific clinical and diagnostic characteristics rather than algorithm complexity alone. Future research should develop and externally validate robust, standardised algorithms in representative populations, using rigorous validation methods, and assess the added value of longitudinal EHR data, biomarkers, and computational phenotyping approaches.