Validity of Heart Failure Diagnoses in Administrative Databases: A Systematic Review and Meta-Analysis

Objective: Heart failure (HF) is an important covariate and outcome in studies of elderly populations and cardiovascular disease cohorts, among others. Administrative data is increasingly being used for long-term clinical research in these populations. We aimed to conduct the first systematic review and meta-analysis of studies reporting on the validity of diagnostic codes for identifying HF in administrative data. Methods: MEDLINE and EMBASE were searched (inception to November 2010) for studies: (a) Using administrative data to identify HF; or (b) Evaluating the validity of HF codes in administrative data; and (c) Reporting validation statistics (sensitivity, specificity, positive predictive value [PPV], negative predictive value, or Kappa scores) for HF, or data sufficient for their calculation. Additional articles were located by hand search (up to February 2011) of original papers. Data were extracted by two independent reviewers; article quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. Using a random-effects model, pooled sensitivity and specificity values were produced, along with estimates of the positive (LR+) and negative (LR-) likelihood ratios, and diagnostic odds ratios (DOR = LR+/LR-) of HF codes. Results: Nineteen studies published from 1999-2009 were included in the qualitative review. Specificity was >= 95% in all studies and PPV was >= 87% in the majority, but sensitivity was lower (>= 69% in >= 50% of studies). In a meta-analysis of the 11 studies reporting sensitivity and specificity values, the pooled sensitivity was 75.3% (95% CI: 74.7-75.9) and specificity was 96.8% (95% CI: 96.8-96.9). The pooled LR+ was 51.9 (20.5-131.6), the LR- was 0.27 (0.20-0.37), and the DOR was 186.5 (96.8-359.2). Conclusions: While most HF diagnoses in administrative databases do correspond to true HF cases, about one-quarter of HF cases are not captured. The use of broader search parameters, along with laboratory and prescription medication data, may help identify more cases.