Yang Peng, Duan Jieying, Li Mingxuan, Tan Rui, Li Yuan, Zhang Zeqing, Wang Ying
Department of Geriatric Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Department of Cardiology, Henan Provincial Chest Hospital, Zhengzhou, Henan, People's Republic of China.
Clin Interv Aging. 2024 Nov 26;19:1965-1977. doi: 10.2147/CIA.S486839. eCollection 2024.
Current scoring systems used to predict major adverse cardiovascular events (MACE) in patients with acute myocardial infarction (AMI) lack some key components and their predictive ability needs improvement. This study aimed to develop a more effective scoring system for predicting 3-year MACE in patients with AMI.
Our statistical analyses included data for 461 patients with AMI. Eighty percent of patients (n=369) were randomly assigned to the training set and the remaining patients (n=92) to the validation set. Independent risk factors for MACE were identified in univariate and multifactorial logistic regression analyses. A nomogram was used to create the scoring system, the predictive ability of which was assessed using calibration curve, decision curve analysis, receiver-operating characteristic curve, and survival analysis.
The nomogram model included the following seven variables: age, diabetes, prior myocardial infarction, Killip class, chronic kidney disease, lipoprotein(a), and percutaneous coronary intervention during hospitalization. The predicted and observed values for the nomogram model were in good agreement based on the calibration curves. Decision curve analysis showed that the clinical nomogram model had good predictive ability. The area under the curve (AUC) for the scoring system was 0.775 (95% confidence interval [CI] 0.728-0.823) in the training set and 0.789 (95% CI 0.693-0.886) in the validation set. Risk stratification based on the scoring system found that the risk of MACE was 4.51-fold higher (95% CI 3.24-6.28) in the high-risk group than in the low-risk group. Notably, this scoring system demonstrated better predictive ability than the GRACE risk score (AUC 0.776 vs 0.731; =0.007).
The scoring system developed from the nomogram in this study showed favorable performance in prediction of MACE and risk stratification of patients with AMI.
目前用于预测急性心肌梗死（AMI）患者主要不良心血管事件（MACE）的评分系统缺乏一些关键要素，其预测能力有待提高。本研究旨在开发一种更有效的评分系统，用于预测AMI患者的3年MACE。
我们的统计分析纳入了461例AMI患者的数据。80%的患者（n = 369）被随机分配至训练集，其余患者（n = 92）被分配至验证集。通过单因素和多因素逻辑回归分析确定MACE的独立危险因素。使用列线图创建评分系统，并通过校准曲线、决策曲线分析、受试者工作特征曲线和生存分析评估其预测能力。
列线图模型包括以下七个变量：年龄、糖尿病、既往心肌梗死、Killip分级、慢性肾脏病、脂蛋白（a）和住院期间的经皮冠状动脉介入治疗。根据校准曲线，列线图模型的预测值与观察值吻合良好。决策曲线分析表明临床列线图模型具有良好的预测能力。训练集中评分系统的曲线下面积（AUC）为0.775（95%置信区间[CI] 0.728 - 0.823），验证集中为0.789（95% CI 0.693 - 0.886）。基于评分系统的风险分层发现，高风险组的MACE风险比低风险组高4.51倍（95% CI 3.24 - 6.28）。值得注意的是，该评分系统的预测能力优于GRACE风险评分（AUC 0.776对0.731；P = 0.007）。
本研究从列线图开发的评分系统在预测AMI患者的MACE和风险分层方面表现良好。
