Multiparametric Integrative Model for Predicting the Risk of Developing Cardiovascular Vasculotoxicity in Antitumor...

Aim        To develop and validate a simple and clinically convenient multiparametric model combining clinical, instrumental and laboratory predictors to prognose the risk of developing cardiovascular toxicity (CVT) in patients receiving antitumor therapy with subsequent personalized stratification of prevention and treatment.Material and methods          This multicenter prospective study included 252 patients (mean age 64.8±10.4 years; 64.5% women) with large tumors and lymphoproliferative diseases who received various antitumor treatments. Demographic data, risk factors, echocardiography results, left ventricular ejection fraction, left ventricular global longitudinal strain (GLS), laboratory data (high-sensitivity troponin I/T, terminal pro-brain natriuretic peptide (NT-proBNP), C-reactive protein, lipid profile) were collected. The endpoint of the study was the development of CVT within 12 months according to the criteria of the European Society of Cardiology (2022).Results    Following multivariate analysis using the Cox model, the most sensitive independent predictors of cardiotoxicity were selected: age >60 years, GLS <-18%, troponin elevation >99th percentile, and NT-proBNP concentration >300 pg/ml. A prognostic model was developed based on the identified parameters. Model validation demonstrated its high validity.Conclusion            Integration of multicomponent data into a single compact prognostic model ensures high accuracy of assessing the risk for the development of CVT and can become a simple tool for personalized monitoring and prevention in cancer patients.