A machine learning model predicted major adverse cardiovascular events with 85.5% AUC in prostate cancer patients on ARSIs, identifying 5 key risk factors.
Can a machine learning-based model accurately predict major adverse cardiovascular events in prostate cancer patients receiving abiraterone or enzalutamide?
A machine learning model using five routine clinical variables can accurately predict major adverse cardiovascular events in prostate cancer patients receiving androgen receptor signaling inhibitors, providing a tool for individualized risk assessment.
Absolute Event Rate: 0% vs 0%
Abstract Background Androgen receptor signaling inhibitors (ARSIs), including abiraterone acetate and enzalutamide, were approved for metastatic castration-resistant prostate cancer (mCRPC) based on landmark trials showing improved survival in both the pre- and post-chemotherapy settings. However, multiple real-world studies have demonstrated increased cardiovascular (CV) morbidity with these agents. Purpose To develop and validate a machine learning-based CV risk prediction model in prostate cancer (PCa) patients receiving ARSIs. Methods A nationwide cohort study was conducted utilizing data from the National Health Insurance Research Database containing the Taiwan Cancer Registry. The study population comprised 4,739 PCa patients who received abiraterone or enzalutamide between January 1, 2014, and February 28, 2022. The cohort was divided into a training set (70%, n=3,318) and a validation set (30%, n=1,421). The index date was defined as the initiation of either ARSI. A machine learning technique with random survival forest (RSF) model incorporating 16 variables was developed to predict major adverse cardiovascular events (MACEs), which were defined as a composite outcome encompassing heart failure (HF) hospitalization, myocardial infarction, ischemic stroke, and cardiovascular mortality. Results Over a mean follow-up period of 2.1 years, MACEs occurred in 10.9% and 11.3% of the training and validation cohorts, respectively. The RSF model utilized variable importance (VIMP) to rank the prognostic capability of 16 clinically relevant variables routinely available during ARSI treatment. These variables encompassed patient's age, ARSI type, androgen deprivation therapy (ADT) modality, eight comorbidities (not including cardiovascular disease because its components have been included), four prior cardiovascular events requiring hospitalization, and previous docetaxel use. The final RSF model identified five key predictive indicators: age 65 or ≥75 years, heart failure, stroke, hypertension, and myocardial infarction. The model exhibited robust performance, achieving an area under the curve (AUC) of 85.1% in the training set and demonstrating strong external validity with an AUC of 85.5% in the validation cohort. A positive correlation was observed between the number of risk factors and the incidence of MACEs. In the training cohort, stratification by risk factor count yielded the following 4-year MACE rates: 6.9% for patients with 0-1 risk factors, 15.3% for those with 2 risk factors, and 24.8% for individuals with ≥3 risk factors (P trend 0.001). This finding was consistent in the validation cohort. Conclusions This machine learning approach identified five predictors of MACEs in PCa patients receiving ARSIs. The risk stratification model provides clinicians with an evidence-based tool for individualized cardiovascular risk assessment prior to initiating ARSI therapy.Variable Importance Distribution Risk Stratification Based on Predictors
Chen et al. (Sat,) reported a other. A machine learning model predicted major adverse cardiovascular events with 85.5% AUC in prostate cancer patients on ARSIs, identifying 5 key risk factors.