Development and External Validation of a Machine Learning–Based Risk Classification Score for Prostate Cancer–Specific Cardiovascular Risk

PURPOSE: Patients with prostate cancer (PC) face elevated cardiovascular (CV) risk. Conventional CV risk scores are often poorly calibrated for PC and require laboratory tests that are not routinely available in PC clinics, limiting oncology use. METHODS: The development cohort (DC) included men with PC (age ≥18 years) diagnosed in 2010-2014. Validation cohort 1 (V1) included men age 45 years and older within 1 year of PC diagnosis or near initiation of androgen deprivation therapy. Validation cohort 2 (V2) included men with PC diagnosed in 2006-2019. Outcomes were cardiovascular disease (CVD), atherosclerotic CVD (ASCVD), and heart failure (HF). Predictors were selected with XGBoost and LASSO, modeled with Fine-Gray and penalized Cox regression, and scaled to additive point scores (low/intermediate/high risk). Performance was assessed using time-dependent AUC (TDAUC), which quantifies the model's ability to correctly rank patients by risk at each time point, and compared with conventional scores. RESULTS: The DC included 1,815 patients, and V1 and V2 included 4,022 and 1,729 patients, respectively (median follow-up 10.3 years). At 10 years, cumulative incidence was 15.0% for composite CVD, 9.4% for ASCVD, and 10.1% for HF. The composite CVD score (age, race, smoking history, high-risk Gleason score, and household members) achieved a 10-year TDAUC of 0.71 (95% CI, 0.64 to 0.78) in the DC and 0.59 (95% CI, 0.52 to 0.65) in V2, with a 2-year TDAUC of 0.66 (95% CI, 0.56 to 0.75) in V1. In the DC, ASCVD and HF scores achieved 10-year TDAUCs of 0.66 (95% CI, 0.58 to 0.75) and 0.70 (95% CI, 0.62 to 0.79), respectively, and retained discrimination in validation. CONCLUSION: To our knowledge, these are the first PC-specific CV risk scores derived from oncology-available variables, supporting pragmatic CV risk stratification in PC clinics and motivating further validation and implementation studies.

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Publication Details

Journal
JCO Oncology Practice
Published
2026-05-29
DOI
https://doi.org/10.1200/op-26-00131
Primary Topic
Chemotherapy-induced cardiotoxicity and mitigation
Type
article
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article

Development and External Validation of a Machine Learning–Based Risk Classification Score for Prostate Cancer–Specific Cardiovascular Risk

Nickolas Stabellini, Umang Swami, Isla P. Garraway, Viraj Shah et al.
JCO Oncology Practice
Chemotherapy-induced cardiotoxicity and mitigation
article

Development and External Validation of a Machine Learning–Based Risk Classification Score for Prostate Cancer–Specific Cardiovascular Risk

Nickolas Stabellini, Umang Swami, Isla P. Garraway, Viraj Shah, Nathanael Fillmore, Omar M. Makram, Tarek Nahle, Avirup Guha, Harikrishnan H. Kunhiraman, Neeraj Agarwal, Jehonathan Pinthus, John Shanahan, Darryl Leong, Jennifer Cullen, Morteza Mahmoudi, Sai S. Kollapaneni, Alberto J. Montero, Neal L. Weintraub
article en

Abstract

PURPOSE: Patients with prostate cancer (PC) face elevated cardiovascular (CV) risk. Conventional CV risk scores are often poorly calibrated for PC and require laboratory tests that are not routinely available in PC clinics, limiting oncology use. METHODS: The development cohort (DC) included men with PC (age ≥18 years) diagnosed in 2010-2014. Validation cohort 1 (V1) included men age 45 years and older within 1 year of PC diagnosis or near initiation of androgen deprivation therapy. Validation cohort 2 (V2) included men with PC diagnosed in 2006-2019. Outcomes were cardiovascular disease (CVD), atherosclerotic CVD (ASCVD), and heart failure (HF). Predictors were selected with XGBoost and LASSO, modeled with Fine-Gray and penalized Cox regression, and scaled to additive point scores (low/intermediate/high risk). Performance was assessed using time-dependent AUC (TDAUC), which quantifies the model's ability to correctly rank patients by risk at each time point, and compared with conventional scores. RESULTS: The DC included 1,815 patients, and V1 and V2 included 4,022 and 1,729 patients, respectively (median follow-up 10.3 years). At 10 years, cumulative incidence was 15.0% for composite CVD, 9.4% for ASCVD, and 10.1% for HF. The composite CVD score (age, race, smoking history, high-risk Gleason score, and household members) achieved a 10-year TDAUC of 0.71 (95% CI, 0.64 to 0.78) in the DC and 0.59 (95% CI, 0.52 to 0.65) in V2, with a 2-year TDAUC of 0.66 (95% CI, 0.56 to 0.75) in V1. In the DC, ASCVD and HF scores achieved 10-year TDAUCs of 0.66 (95% CI, 0.58 to 0.75) and 0.70 (95% CI, 0.62 to 0.79), respectively, and retained discrimination in validation. CONCLUSION: To our knowledge, these are the first PC-specific CV risk scores derived from oncology-available variables, supporting pragmatic CV risk stratification in PC clinics and motivating further validation and implementation studies.

JCO Oncology Practice
Harvard University (US), University of California, Los Angeles (US), University of Utah (US), Augusta University (US), University Hospitals of Cleveland (US), Huntsman Cancer Institute (US), Population Health Research Institute (CA), University Hospitals Seidman Cancer Center (US), Children's Cancer Center (US), VA Greater Los Angeles Healthcare System (US), Case Western Reserve University (US), Hamilton Health Sciences (CA), Michigan State University (US), McMaster University (CA)
Openalex Percentile: Top 6%
Chemotherapy-induced cardiotoxicity and mitigation
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