Shi Tao, Yang Jianping, Zhou Yanji, Yang Sirui, Yang Fazhi, Ma Xinuo, Peng Yujuan, Pu Jinfang, Wei Hong, Chen Lixing
Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
College of Big Data, Yunnan Agricultural University, Kunming, China.
Front Med (Lausanne). 2025 Jul 18;12:1594273. doi: 10.3389/fmed.2025.1594273. eCollection 2025.
Acute ST-segment elevation myocardial infarction (STEMI) is a cardiovascular emergency that is associated with a high risk of death. In this study, we developed explainable machine learning models to predict the overall survival (OS) of STEMI patients to help improve prognosis and increase survival.
After applying the inclusion and exclusion criteria, we selected 893 patients who underwent emergency coronary angiography and percutaneous coronary intervention (PCI) for STEMI at the First Affiliated Hospital of Kunming Medical University. The best predictor variables were screened by least absolute shrinkage and selection operator (LASSO) regression. These variables were used to construct Cox proportional hazards regression (coxph) and random survival forest (rfsrc) models. Three criteria (C-index, Brier score, and C/D AUC) were utilised to compare the performance of the two models. Then, by applying the time-dependent variable importance and the partial dependence survival profile, a global explanation of the entire cohort was conducted. Finally, local explanations for individual patients were performed with the SurvSHAP(t) and SurvLIME plots and the ceteris paribus survival profile.
Combining the results of the comparison of the three criteria, the performance of the rfsrc model was shown to be superior to that of the coxph model. LASSO regression was used to screen 11 predictor variables, such as diastolic blood pressure (DBP), Killip class, hyperlipidaemia, global registry of acute coronary events (GRACE) Score, creatine kinase isoenzyme-MB, myoglobin, white blood cells, monocytes, thrombin time, globulin (GLB), and conjugated bilirubin. The global explanation of the whole cohort revealed that DBP, GRACE Score, myoglobin, and monocytes had a significant effect on the OS of STEMI patients in the coxph model and that DBP, GRACE Score, and GLB were the variables that significantly affected the OS of STEMI patients in the rfsrc model. Incorporating a single patient into the model can yield a local explanation of each patient, thus guiding clinicians in developing precision treatments.
The rfsrc model outperformed the coxph model in terms of predictive performance. Clinicians can use these predictive models to understand the major risk factors for each STEMI patient and thus develop more individualised and precise treatment strategies.
急性ST段抬高型心肌梗死（STEMI）是一种心血管急症，与高死亡风险相关。在本研究中，我们开发了可解释的机器学习模型来预测STEMI患者的总生存期（OS），以帮助改善预后并提高生存率。
应用纳入和排除标准后，我们选择了893例在昆明医科大学第一附属医院接受急诊冠状动脉造影和经皮冠状动脉介入治疗（PCI）的STEMI患者。通过最小绝对收缩和选择算子（LASSO）回归筛选最佳预测变量。这些变量用于构建Cox比例风险回归（coxph）模型和随机生存森林（rfsrc）模型。使用三个标准（C指数、Brier评分和C/D AUC）比较这两个模型的性能。然后，通过应用时间依赖变量重要性和部分依赖生存曲线，对整个队列进行全局解释。最后，使用SurvSHAP(t)和SurvLIME图以及条件生存曲线对个体患者进行局部解释。
综合三个标准的比较结果，rfsrc模型的性能优于coxph模型。LASSO回归用于筛选11个预测变量，如舒张压（DBP）、Killip分级、高脂血症、急性冠状动脉事件全球注册（GRACE）评分、肌酸激酶同工酶-MB、肌红蛋白、白细胞、单核细胞、凝血酶时间、球蛋白（GLB）和结合胆红素。对整个队列的全局解释显示，在coxph模型中，DBP、GRACE评分、肌红蛋白和单核细胞对STEMI患者的OS有显著影响，而在rfsrc模型中，DBP、GRACE评分和GLB是显著影响STEMI患者OS的变量。将单个患者纳入模型可以对每个患者进行局部解释，从而指导临床医生制定精准治疗方案。
rfsrc模型在预测性能方面优于coxph模型。临床医生可以使用这些预测模型了解每个STEMI患者的主要危险因素，从而制定更个体化、精准的治疗策略。
