Development and Validation of a Mortality Prediction Model for Left Ventricular Thrombus

Left ventricular thrombus (LVT) is a severe complication associated with increased risks of systemic embolism and mortality. Despite advancements in anticoagulant therapy, optimal management strategies and risk factors for all-cause mortality remain unclear. This study aims to develop a predictive model to assess mortality risk in LVT patients and guide clinical decision-making.

This retrospective cohort study included LVT patients diagnosed at West China Hospital (June 2018-June 2023). Patients were classified into survival and mortality groups based on all-cause mortality during follow-up. A total of 459 patients were included, randomly divided into training (n = 322) and validation (n = 137) sets. Logistic regression analysis identified seven independent predictors of mortality, which were used to construct a nomogram-based risk prediction model. The model demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.846 in the training set and 0.791 in the validation set. Key mortality predictors included elevated B-type natriuretic peptide (BNP) (OR 3.359, 95% CI 1.827-6.176, p = 0.0001), lower albumin levels (OR 0.930, 95% CI 0.882-0.981, p = 0.0077), absence of antithrombotic therapy (OR 0.468, 95% CI 0.303-0.723, p = 0.0006), and presence of malignant tumors (OR 6.199, 95% CI 1.593-24.129, p = 0.0085).

A novel mortality prediction model for LVT patients was developed, offering a valuable tool for risk assessment and treatment optimization. This model provides a valuable tool for risk assessment and treatment optimization in Asian populations, particularly in China. Further validation is required to confirm its clinical utility.