Clinical Research Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Yidu Cloud Technology Inc., Beijing, China.
Clin Cardiol. 2023 Feb;46(2):184-194. doi: 10.1002/clc.23957. Epub 2022 Dec 7.
Cardiovascular diseases are a significant health burden with the prevalence increasing worldwide. Thus, a highly accurate assessment and prediction of death risk are crucial to meet the clinical demand. This study sought to develop and validate a model to predict in-hospital mortality among patients with the acute coronary syndrome (ACS) using nonlinear algorithms.
A total of 2414 ACS patients were enrolled in this study. All samples were divided into five groups for cross-validation. The logistic regression (LR) model and XGboost model were applied to predict in-hospital mortality. The results of two models were compared between the variable set by the global registry of acute coronary events (GRACE) score and the selected variable set.
The in-hospital mortality rate was 3.5% in the dataset. Model performance on the selected variable set was better than that on GRACE variables: a 3% increase in area under the receiver operating characteristic (ROC) curve (AUC) for LR and 1.3% for XGBoost. The AUC of XGBoost is 0.913 (95% confidence interval [CI]: 0.910-0.916), demonstrating a better discrimination ability than LR (AUC = 0.904, 95% CI: 0.902-0.905) on the selected variable set. Almost perfect calibration was found in XGBoost (slope of predicted to observed events, 1.08; intercept, -0.103; p < .001).
XGboost modeling, an advanced machine learning algorithm, identifies new variables and provides high accuracy for the prediction of in-hospital mortality in ACS patients.
心血管疾病是全球范围内一个重大的健康负担，其患病率正在不断增加。因此，对死亡风险进行高度准确的评估和预测对于满足临床需求至关重要。本研究旨在使用非线性算法开发和验证一种预测急性冠状动脉综合征（ACS）患者住院期间死亡率的模型。
本研究共纳入 2414 例 ACS 患者。所有样本被分为五组进行交叉验证。应用逻辑回归（LR）模型和 XGBoost 模型预测住院期间死亡率。比较了基于全球急性冠状动脉事件注册（GRACE）评分的变量集和选择变量集的两种模型的结果。
数据集的住院死亡率为 3.5%。在选择变量集上，模型性能优于 GRACE 变量：LR 的受试者工作特征（ROC）曲线下面积（AUC）增加 3%，XGBoost 增加 1.3%。XGBoost 的 AUC 为 0.913（95%置信区间[CI]：0.910-0.916），在选择变量集上，其鉴别能力优于 LR（AUC=0.904，95%CI：0.902-0.905）。XGBoost 发现几乎完美的校准（预测与观察事件的斜率，1.08；截距，-0.103；p<0.001）。
XGBoost 建模是一种先进的机器学习算法，可识别新变量，并为 ACS 患者住院期间死亡率的预测提供高度准确性。
