Identification of Undiagnosed Valvular Heart Disease in the Community Using Routine Healthcare Data: Systematic Review and Meta-Analysis

Background and Aims Valvular heart disease (VHD) conveys a high burden of morbidity and mortality and, given the increasing armamentarium of therapeutic interventions, earlier diagnosis is important.

This meta-analysis aimed to evaluate the potential of tools in routine health data to identify individuals with undiagnosed VHD in community or population-based settings.

Methods MEDLINE and Embase were searched from inception to October 6, 2025, for studies describing tools derived, validated or augmented to identify VHD in community populations. Potential measures (C-statistics) were pooled using Bayesian meta-analysis stratified by valve class and data modality with potential data from ≥3 cohorts, with heterogeneity assessed using 95% prediction intervals. Risk of bias was evaluated using the Prediction model Risk Of Bias Assessment Tool (PROBAST).

Results Nineteen studies including 33 prediction tools were included; 18% had a high risk of bias. Imaging- and ECG-based models demonstrated excellent potential (C-statistic 0.861 [95% CI 0.827-0.889; I2 = 96%] and 0.830 [0.802-0.855; I2 = 100%]), while multivariable clinical models displayed good potential and biomarker-based models adequate potential (0.786 [0.663-0.872; I2 = 99%] and 0.699 [0.629-0.760; I2 = 0%]). Models targeting right-sided and mitral valve disease showed excellent potential (0.885 [0.837-0.920; I2 = 98%], 0.851 [0.819-0.879; I2 = 99%]). Models targeting aortic valve disease displayed good potential (0.797 [0.765-0.826; I2 = 100%]). External validation was limited, only one study assessed clinical utility, and no study prospectively validated a model.

Conclusions Models using routine healthcare data to identify risk of undiagnosed VHD show potential but comprehensive external and prospective validation is required before consideration of implementation.