Song Yaqin, Yang Kongzhi, Su Yingjie, Song Kun, Ding Ning
Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Department of Emergency Medicine, Clinical Research Center for Emergency and Critical Care in Hunan Province, Hunan Provincial Institute of Emergency Medicine, Hunan Provincial Key Laboratory of Emergency and Critical Care Metabonomics, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, People's Republic of China.
Risk Manag Healthc Policy. 2024 Dec 14;17:3171-3186. doi: 10.2147/RMHP.S485088. eCollection 2024.
There is lack of predictive models for the risk of severe complications during hospitalization in patients with acute myocardial infarction (AMI). In this study, we aimed to create a nomogram to forecast the likelihood of in-hospital severe complications in AMI.
From August 2020 to January 2023, 1024 patients with AMI including the modeling group (n=717) and the validation group (n=307) admitted in Changsha Central Hospital's emergency department. Conduct logistic regression analysis, both univariate and multivariate, on the pertinent patient data from the modeling cohort at admission, identify independent risk factors, create a nomogram to forecast the likelihood of severe complications in patients with AMI, and assess the accuracy of the graph's predictions in the validation cohort.
Age, heart rate, mean arterial pressure, diabetes, hypertension, triglycerides and white blood cells were seven independent risk factors for serious complications in AMI patients. Based on these seven variables, the nomogram model was constructed. The nomogram has high predictive accuracy (AUC=0.793 for the modeling group and AUC=0.732 for the validation group). The calibration curve demonstrates strong consistency between the anticipated and observed values of the nomogram in the modeling and validation cohorts. Moreover, the DCA curve results show that the model has a wide threshold range (0.01-0.73) and has good practicality in clinical practice.
This study developed and validated an intuitive nomogram to assist clinicians in evaluating the probability of severe complications in AMI patients using readily available clinical data and laboratory parameters.
急性心肌梗死（AMI）患者住院期间严重并发症风险的预测模型尚缺乏。在本研究中，我们旨在创建一个列线图来预测AMI患者住院期间发生严重并发症的可能性。
2020年8月至2023年1月，长沙中心医院急诊科收治的1024例AMI患者，分为建模组（n = 717）和验证组（n = 307）。对建模队列入院时的相关患者数据进行单因素和多因素逻辑回归分析，确定独立危险因素，创建列线图以预测AMI患者发生严重并发症的可能性，并评估该图在验证队列中的预测准确性。
年龄、心率、平均动脉压、糖尿病、高血压、甘油三酯和白细胞是AMI患者发生严重并发症的七个独立危险因素。基于这七个变量构建了列线图模型。该列线图具有较高的预测准确性（建模组AUC = 0.793，验证组AUC = 0.732）。校准曲线表明，列线图在建模和验证队列中的预测值与观察值之间具有很强的一致性。此外，决策曲线分析（DCA）结果表明，该模型具有较宽的阈值范围（0.01 - 0.73），在临床实践中具有良好的实用性。
本研究开发并验证了一种直观的列线图，以帮助临床医生利用现成的临床数据和实验室参数评估AMI患者发生严重并发症的概率。
