Key result
Optimized cutoffs improve the suboptimal discrimination of standard CV risk scores for cancer survivor mortality.
Why the study?
General population cardiovascular risk prediction tools lack cancer-specific variables, leaving it unclear whether they can accurately predict cardiovascular mortality in cancer survivors even after statistical optimization.
Do traditional cardiovascular risk scores accurately predict cardiovascular mortality in cancer survivors, and does statistical optimization improve their performance?
Observational (n=634)
Do traditional cardiovascular risk scores accurately predict cardiovascular mortality in cancer survivors, and does statistical optimization improve their performance?
Absolute Event Rate: 0.71% vs 0.64%
p-value: p=<0.001
Traditional cardiovascular risk scores have inadequate discrimination for predicting cardiovascular mortality in cancer survivors, highlighting the need for cardio-oncology-specific risk models.
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Optimized general CVD risk models warrant caution in cancer survivors; leaves open need for cancer-specific prediction tools.
Patel et al. (2026) conducted an 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).
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