CARDIOVASCULAR RISK PROFILE IN PATIENTS WITH ONCOLOGICAL AND CHRONIC DISEASES

IMPORTANCE Despite the high cardiovascular risk in multiple myeloma (MM) patients eligible for carfilzomib (K) therapy, the true incidence and spectrum of adverse events (CVAEs), the baseline predictors and validated baseline risk scores to identify the highest-risk patients are unknown due to lacking of real-world data. OBJECTIVES This study aims to prospectively evaluate a cohort of MM patients treated with K (1) determine the true incidence of CVAEs and of subclinical organ injury (2) examine baseline predictors, including advanced cardiac and vascular parameters; and (3) assess the performance of guideline-based risk score and to develop an internal tailored risk model. METHODS Consecutive patients with MM candidate to K therapy were enrolled since January 2015 and are currently being followed. They underwent a baseline and follow-up clinical and instrumental evaluations during K, detecting CVAEs. CONCLUSIONS MM patients have high cardiovascular (CV) risk with a substantial incidence of CVAEs during K therapy. The HFA-ICOS risk score showed limited discrimination for predicting events in our cohort. Conversely, a CVAEs risk score built from the strongest baseline predictors successfully identified higher-risk individuals, pending external validation. MAIN RESULTS MM patients have high baseline cardiovascular (CV) risk, with substantial subclinical organ damage (about 20% cardiac and 25% vascular subclinical damage). During follow-up, up to 45% of patients experienced a CVAEs, including 15% with cardiac and up to 40% with hypertensive events, particularly in regimens containing higher K doses (59% vs 40%) and with more rapid onset. Baseline parameters—office systolic (SBP), daytime SBP and BP variability, LV mass, LV global longitudinal strain (GLS), left atrial strain (LAS), and pulse wave velocity (cf-PWV) —emerged as strong predictors of CVAEs. The HFA-ICOS score showed limited discrimination between risk categories (low, intermediate, high) and reduced ability to predict events in our cohort. Conversely, a CVAEs risk score built from the strongest predictors in our population—SBP, BPV, LVH, GLS and cf-PWV—successfully identified higher-risk individuals