Development and validation of a prediction model for cardiac events in patients with hepatocellular carcinoma undergoing stereotactic body radiation therapy.

Abstract

Purpose: To develop and validate a prediction model for major adverse cardiac events (MACE) in hepatocellular carcinoma (HCC) patients treated with stereotactic body radiation therapy (SBRT).

Methods and materials: We retrospectively identified 1893 HCC patients who received SBRT at two institutions, with one serving as the development cohort (n=1473) and the other as the validation cohort (n=420). MACE was defined as any cardiac event classified as grade 3 or higher, according to the Common Terminology Criteria for Adverse Events, version 5.0. We evaluated 15 clinical and 88 dosimetric parameters using bootstrapped forward selection and area under the curve (AUC) to identify significant predictors for MACE. Based on these factors, we constructed the Cardiac Event Index (CEI) model, categorizing patients into distinct risk groups. Model performance was assessed for discrimination, efficiency, and calibration.

Results: The occurrence rate of MACE was 5.8% in the development cohort and 6.7% in the validation cohort. Five parameters were selected for predicting MACE and were incorporated into the CEI model using the following equation: CEI = age score + hypertension + current smoking + (2 × history of cardiac disease) + (0.05 × heart-V5 [%]), which yielded an AUC of 0.770 for MACE and 0.750 for coronary artery disease. The CEI model stratified patients into low-, intermediate-, and high-risk groups that had MACE incidence rates of 0.4%, 4.9%, and 22.8%, respectively. The impact of heart-V5 on MACE was minimal in low- and intermediate-risk groups but pronounced in the high-risk group. In the validation cohort, the CEI model yielded an AUC of 0.809 for MACE and 0.793 for coronary artery disease.

Conclusions: The CEI model demonstrated robust performance in predicting MACE, revealing the significant influence of clinical factors and the minimal impact of SBRT. This model can inform evidence-based decisions regarding cardiac dose optimization in SBRT planning.