Standard cardiovascular risk scores showed suboptimal discrimination for cardiovascular mortality in cancer survivors (AUCs 0.53-0.64), which improved with Youden-optimized cutoffs (AUC up to 0.71).
Observational (n=634)
Do traditional cardiovascular risk scores accurately predict cardiovascular mortality in cancer survivors, and does statistical optimization improve their performance?
Traditional cardiovascular risk scores have inadequate discrimination for predicting cardiovascular mortality in cancer survivors, highlighting the need for cardio-oncology-specific risk models.
Absolute Event Rate: 0.71% vs 0.64%
p-value: p=<0.001
Introduction: Cardiovascular disease (CVD) is a leading cause of non-cancer death among cancer survivors, attributable to cardiotoxic therapies and cardiovascular risk factors. General population risk prediction tools, including ASCVD (Atherosclerotic cardiovascular disease), Framingham’s Score, and PREVENT (Predicting Risk of Cardiovascular Disease EVENTS), lack cancer-specific variables. We evaluated whether these models, even after statistical optimization, could predict cardiovascular mortality in cancer survivors. Methods: Using the National Health and Nutrition Examination Survey (NHANES) 2001–2018, linked with National Death Index (NDI) mortality data, we conducted a retrospective analysis of 634 and 429 cancer survivors, respectively, across model-specific cohorts free of baseline cardiovascular disease. Discrimination was assessed for ASCVD, Framingham Score, and PREVENT using standardized thresholds of 7.5% and 20%, as well as Youden-optimized cutoffs. Area under the curve (AUC) comparisons were performed using the DeLong non-parametric method. Results: Standard thresholds showed suboptimal discrimination across all models (AUCs: ASCVD 0.56, Framingham 0.53, PREVENT 0.64). In contrast, Youden-optimized AUCs (ASCVD: 0.68; PREVENT: 0.71; all p < 0.001, DeLong test). Optimization increased the “low-risk” group’s mortality rate from 2.8% to 4.1% (RR = 1.47), suggesting improved statistical fit came at the cost of overestimating the risk. Optimized thresholds outperformed conventional cutoffs, underscoring the necessity for recalibrated, cohort-specific risk stratification in cancer survivors. Conclusions: Standard risk scores have inadequate discrimination for cardiovascular mortality prediction in cancer survivors. Threshold recalibration improves statistical metrics but does not resolve the structural failure of these models to account for cardiotoxic exposure. Development of cardio-oncology-specific risk models incorporating oncologic exposures is therefore warranted.
Patel et al. (Sun,) conducted a observational in Cancer survivors (n=634). Youden-optimized risk score thresholds vs. Standard risk score thresholds (7.5% and 20%) was evaluated on Discrimination for cardiovascular mortality (Area under the curve for PREVENT) (p=<0.001). Standard cardiovascular risk scores showed suboptimal discrimination for cardiovascular mortality in cancer survivors (AUCs 0.53-0.64), which improved with Youden-optimized cutoffs (AUC up to 0.71).