Construction and validation of a nomogram prediction model for myocardial injury in breast cancer patients after treatment

Abstract

Improved prognostic models are needed to guide personalized prevention and treatment decisions for breast cancer patients to reduce the risk of myocardial injury after anti-cancer therapy. This study aimed to develop and validate a prognostic nomogram to predict early myocardial injury after treatment. Breast cancer patients with at least three hypersensitive troponin T (hs-cTnT) measurements between January 2014 and December 2021 were included. Patients with hs-cTnT >14 pg/mL during 6-month follow-up were considered to have early myocardial injury. Participants were randomly divided into a training cohort (n = 366) and a validation cohort (n = 185). Univariate Cox regression and LASSO analysis were used to identify candidate predictors, followed by multivariable Cox regression to construct the nomogram. A total of 551 patients were included. Independent predictors included hs-cTnT level, HDL-C level, thoracic aortic calcification score, and anti-HER2 therapy. The nomogram demonstrated good discrimination (AUC 0.901, 95% CI 0.832–0.971) and calibration (C-index 0.908, 95% CI 0.883–0.933) in the validation cohort. Decision curve analysis indicated clinical usefulness, and patients were effectively stratified into high- and low-risk groups (p < 0.001). This nomogram accurately predicts the 6-month risk of myocardial injury after anti-cancer treatment and may assist in risk stratification and clinical decision-making for breast cancer patients.Abstract_Figure1.jpegAbstract_Figure2.jpg