Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is no practical method to predict or monitor the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) complicated by hyperuricemia. We aimed to evaluate the performance of different machine learning models for the prediction of 1-year mortality in STEMI patients with hyperuricemia. We compared five machine learning models (logistic regression, -nearest neighbor, CatBoost, random forest, and XGBoost) with the traditional global (GRACE) risk score for acute coronary event registrations. We registered patients aged >18 years diagnosed with STEMI and hyperuricemia at the Affiliated Hospital of Zunyi Medical University between January 2016 and January 2020. Overall, 656 patients were enrolled (average age, ; 83.6%, male). All patients underwent emergency percutaneous coronary intervention. We evaluated the performance of five machine learning classifiers and the GRACE risk model in predicting 1-year mortality. The area under the curve (AUC) of the six models, including the GRACE risk model, ranged from 0.75 to 0.88. Among all the models, CatBoost had the highest predictive accuracy (0.89), AUC (0.87), precision (0.84), and F1 value (0.44). After hybrid sampling technique optimization, CatBoost had the highest accuracy (0.96), AUC (0.99), precision (0.95), and F1 value (0.97). Machine learning algorithms, especially the CatBoost model, can accurately predict the mortality associated with STEMI complicated by hyperuricemia after a 1-year follow-up.

中文翻译：

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基于临床特征的机器学习模型对高尿酸血症患者 ST 段抬高心肌梗死的 1 年死亡率风险预测：一项回顾性研究

在临床实践中，对高危患者进行准确的风险评估至关重要。然而，对于ST段抬高型心肌梗死（STEMI）并发高尿酸血症患者的预后，尚无实用的方法来预测或监测。我们旨在评估不同机器学习模型在预测 STEMI 高尿酸血症患者 1 年死亡率方面的性能。我们比较了五种机器学习模型（逻辑回归，-最近邻、CatBoost、随机森林和 XGBoost）与急性冠状动脉事件登记的传统全局 (GRACE) 风险评分。我们登记了 2016 年 1 月至 2020 年 1 月在遵义医科大学附属医院诊断为 STEMI 和高尿酸血症的年龄 >18 岁的患者。总共招募了 656 名患者（平均年龄，; 83.6%，男性）。所有患者均接受紧急经皮冠状动脉介入治疗。我们评估了五个机器学习分类器和 GRACE 风险模型在预测 1 年死亡率方面的性能。包括 GRACE 风险模型在内的 6 个模型的曲线下面积（AUC）在 0.75 到 0.88 之间。在所有模型中，CatBoost 具有最高的预测准确度（0.89）、AUC（0.87）、精度（0.84）和 F1 值（0.44）。在混合采样技术优化后，CatBoost 的准确度（0.96）、AUC（0.99）、精度（0.95）和 F1 值（0.97）最高。机器学习算法，尤其是 CatBoost 模型，可以准确预测 1 年随访后 STEMI 并发高尿酸血症的死亡率。