Identification of undiagnosed valvular heart disease in the community using routine healthcare data: systematic review and meta-analysis

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Abstract
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 6 October 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 electrocardiogram-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.
Funding Information
  • NIHR Development and Skills Enhancement
  • Health Data Research UK
  • Barts Charity

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