Performance of Traditional Cardiovascular Risk Scores and Objective Optimization in Cancer Survivors. - Patel, Harsh A.; Syed, Saifullah; Tella, Pranathi; Thyagaturu, Harshith; Patel, Brijesh (Current Oncology, 2026)
Simple Summary: Cancer survivors are living longer but face a higher risk of heart disease compared to the general population, partly due to cancer treatments such as chemotherapy. Doctors rely on standard heart risk calculators to predict problems and determine who may benefit from preventive therapies like statins. However, it remains unclear if these tools accurately predict risk for people with a history of cancer. Using national health data, we evaluated three ASCVD risk scores (PCE, Framingham, and PREVENT) and found they all performed poorly in cancer survivors. When we recalibrated the tools to perform better statistically, the improvement came with a serious drawback: more high-risk survivors were incorrectly labeled as low risk. Modifying existing heart risk calculators is insufficient. We need new risk models designed specifically for cancer survivors that incorporate their unique treatment history and cancer-related variables. This will help guide better prevention strategies and improve long-term cardiovascular care. 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.