Development and Validation of a Prediction Model for Cardiac Events in Patients With Hepatocellular Carcinoma
Hye In Lee1, Jaeman Son2, Byungchul Cho1
1Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Purpose:
To develop and validate a prediction model for major adverse cardiac events (MACEs) in hepatocellular carcinoma patients treated with stereotactic body radiation therapy (SBRT).
Methods And Materials:
We retrospectively identified 1893 hepatocellular carcinoma patients who received SBRT at 2 institutions, with one serving as the development cohort (n = 1473) and the other as the validation cohort (n = 420). A 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 MACEs. 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 MACE occurrence rate was 5.8% in the development cohort and 6.7% in the validation cohort. Five parameters were selected for predicting MACEs 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 MACEs 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 MACEs 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 MACEs and 0.793 for coronary artery disease.
Conclusions:
The CEI model demonstrated robust performance in predicting MACEs, 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.
