Zhou Qianhui, He Rui, Li Hong, Gu Manping
Department of Nursing, The First Affiliated Hospital of Chongqing Medical, University, Chongqing, China.
Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing, Medical University, Chongqing, China.
Int J Med Inform. 2025 Jul;199:105884. doi: 10.1016/j.ijmedinf.2025.105884. Epub 2025 Mar 22.
Our study aims to develop and validate an effective in-hospital major adverse cardiovascular events(MACE) prediction model for patients with emergency Non-ST elevation acute coronary syndrome(NSTE-ACS).
We retrospectively collected NSTE-ACS patients in three tertiary hospitals in Chongqing. In-hospital MACE was the predicted outcome. Patients from one hospital were divided into training set and internal validation set according to the ratio of 7:3. Besides, 662 patients from two other tertiary hospitals were for external validation. Patient information including demographics, laboratory tests results and disease course records were for comprehensive analysis. Finally, LASSO were used to identify the predictors and develop the model. This model was subsequently visualized as a nomogram, followed by both internal and external validations.The receiver operating characteristic curve, calibration curve and clinical decision curve analysis were used to assess the model's discrimination, calibration and clinical applicability, respectively.
A total of 3,308 patients were included, 375 of whom developed in-hospital MACE. The LR model demonstrated that length of stay, neutrophils, myoglobin, NYHA, CCI, NT-proBNP, LVEF and respiratory failure were risk factors for in-hospital MACE in emergence NSTE-ACS patients. In the training set, the AUC was 0.860 (95%CI:0.831-0.889). In external validation,the AUC was 0.855(95%CI:0.808-0.902), and both the calibration curve and DCA in validation set also revealed stable predictive accuracy and clinical validity.Additionally,it is available to calculate the MACE risk online via the web page (https://cocozhou99.shinyapps.io/DynNomapp/).
The prediction model we constructed has good predictive performance and can help healthcare professionals accurately assess the risk of in-hospital MACE in emergence NSTE-ACS patients.
本研究旨在开发并验证一种针对急诊非ST段抬高型急性冠状动脉综合征（NSTE-ACS）患者的有效院内主要不良心血管事件（MACE）预测模型。
我们回顾性收集了重庆三家三级医院的NSTE-ACS患者。院内MACE为预测结局。将来自一家医院的患者按照7:3的比例分为训练集和内部验证集。此外，来自另外两家三级医院的662例患者用于外部验证。对包括人口统计学、实验室检查结果和病程记录在内的患者信息进行综合分析。最后，使用LASSO方法识别预测因素并构建模型。该模型随后被可视化成列线图，接着进行内部和外部验证。分别使用受试者工作特征曲线、校准曲线和临床决策曲线分析来评估模型的区分度、校准度和临床适用性。
共纳入3308例患者，其中375例发生院内MACE。Logistic回归（LR）模型显示，住院时间、中性粒细胞、肌红蛋白、纽约心脏协会（NYHA）心功能分级、Charlson合并症指数（CCI）、N末端脑钠肽前体（NT-proBNP）、左心室射血分数（LVEF）和呼吸衰竭是急诊NSTE-ACS患者院内MACE的危险因素。在训练集中，曲线下面积（AUC）为0.860（95%置信区间：0.831-0.889）。在外部验证中，AUC为0.855（95%置信区间：0.808-0.902），验证集的校准曲线和决策曲线分析也显示出稳定的预测准确性和临床有效性。此外，可通过网页（https://cocozhou99.shinyapps.io/DynNomapp/）在线计算MACE风险。
我们构建的预测模型具有良好的预测性能，可帮助医护人员准确评估急诊NSTE-ACS患者院内MACE的风险。
