Mortality predictors in cancer patients undergoing transcatheter aortic valve implantation for aortic stenosis: a systematic review and meta-analysis
摘要
The coexistence of cancer and aortic stenosis is increasingly common in elderly patients, presenting unique challenges for management. Transcatheter aortic valve implantation (TAVI) offers a less invasive alternative to surgical valve replacement, but the impact of various clinical factors on post-TAVI mortality in cancer patients remains unclear.
ObjectiveTo systematically review and meta-analyze the predictors of mortality in cancer patients undergoing TAVI for aortic stenosis.
MethodsA comprehensive literature search was conducted in Scopus, PubMed, Web of Science, and Embase up to May 2025. Studies assessing mortality predictors in cancer patients post-TAVI were included. Data extraction and quality assessment were performed independently by two reviewers. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using random-effects models. Sensitivity analyses addressed within-study correlation assumptions, and heterogeneity was assessed using I² and τ² statistics.
ResultsFive high-quality case-control studies, encompassing over 7,000 patients, were included. Advanced NYHA class (III/IV), prior myocardial infarction, atrial fibrillation, COPD, diabetes mellitus, and higher STS scores were robustly associated with increased mortality. Active cancer conferred a significantly higher mortality risk (HR 1.64, 95% CI: 1.17–2.30), while preserved left ventricular ejection fraction and renal function were linked to improved survival. Most predictors remained stable across sensitivity analyses, although the impact of cancer status varied with correlation assumptions.
ConclusionBoth traditional cardiovascular and cancer-specific factors significantly influence mortality in cancer patients undergoing TAVI. These findings underscore the need for individualized, multidisciplinary risk assessment and highlight the importance of further research to refine patient selection and optimize outcomes in this growing patient population.
Graphical abstract