aceMachine Learning for Mortality Prediction in Infective Endocarditis: A Systematic Review and Meta-Analysis
Infective endocarditis (IE) continues to be an often fatal condition despite improvements in cardiac surgical procedures and antibiotic therapy, and conventional scoring tools show poor generalizability. Machine learning (ML) addresses these limitations by capturing complex, nonlinear clinical relationships, outperforming conventional scores in predictive accuracy, though prior ML work in IE has focused on diagnosis. A PRISMA-compliant systematic review and meta-analysis of PubMed (Supplemental Digital Content,…
Supervised ML models, especially ensemble methods, predicted all-cause mortality in adult infective endocarditis with pooled AUC 0.85 for both in-hospital/early and 6-month mortality, outperforming conventional scores.
Half of included studies had identified risk of bias and clinical adoption remains limited, requiring multicenter prospective validation and interpretable frameworks before bedside use.
Evidence base is limited by retrospective design and bias, with 7 of 8 studies retrospective and risk of bias in half, requiring prospective multicenter validation before bedside use.
Evidence
- Peer-reviewedCardiology in Review2026-09-09
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Truvace Impact Record TRV-2026-1049, v1: “Machine Learning for Mortality Prediction in Infective Endocarditis: A Systematic Review and Meta-Analysis.” Truvace, 2026-09-10. /record/TRV-2026-1049 (accessed at citation time). sha256 b0dc2586649f11d3…
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