Accuracy of heart failure ascertainment using routinely collected healthcare data: a systematic review and meta-analysis.

BACKGROUND: Ascertainment of heart failure (HF) hospitalizations in cardiovascular trials is costly and complex, involving processes that could be streamlined by using routinely collected healthcare data (RCD). The utility of coded RCD for HF outcome ascertainment in randomized trials requires assessment. We systematically reviewed studies assessing RCD-based HF outcome ascertainment against "gold standard" (GS) methods to study the feasibility of using such methods in clinical trials. METHODS: Studies assessing International Classification of Disease (ICD) coded RCD-based HF outcome ascertainment against GS methods and reporting at least one agreement statistic were identified by searching MEDLINE and Embase from inception to May 2021. Data on study characteristics, details of RCD and GS data sources and definitions, and test statistics were reviewed. Summary sensitivities and specificities for studies ascertaining acute and prevalent HF were estimated using a bivariate random effects meta-analysis. Heterogeneity was evaluated using I2 statistics and hierarchical summary receiver operating characteristic (HSROC) curves. RESULTS: A total of 58 studies of 48,643 GS-adjudicated HF events were included in this review. Strategies used to improve case identification included the use of broader coding definitions, combining multiple data sources, and using machine learning algorithms to search free text data, but these methods were not always successful and at times reduced specificity in individual studies. Meta-analysis of 17 acute HF studies showed that RCD algorithms have high specificity (96.2%, 95% confidence interval [CI] 91.5-98.3), but lacked sensitivity (63.5%, 95% CI 51.3-74.1) with similar results for 21 prevalent HF studies. There was considerable heterogeneity between studies. CONCLUSIONS: RCD can correctly identify HF outcomes but may miss approximately one-third of events. Methods used to improve case identification should also focus on minimizing false positives.