Cao Jiao-Yu, Zhang Li-Xiang, Zhou Xiao-Juan
Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China.
World J Cardiol. 2024 Feb 26;16(2):80-91. doi: 10.4330/wjc.v16.i2.80.
Acute myocardial infarction (AMI) is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium. Timely medical contact is critical for successful AMI treatment, and delays increase the risk of death for patients. Pre-hospital delay time (PDT) is a significant challenge for reducing treatment times, as identifying high-risk patients with AMI remains difficult. This study aims to construct a risk prediction model to identify high-risk patients and develop targeted strategies for effective and prompt care, ultimately reducing PDT and improving treatment outcomes.
To construct a nomogram model for forecasting pre-hospital delay (PHD) likelihood in patients with AMI and to assess the precision of the nomogram model in predicting PHD risk.
A retrospective cohort design was employed to investigate predictive factors for PHD in patients with AMI diagnosed between January 2022 and September 2022. The study included 252 patients, with 180 randomly assigned to the development group and the remaining 72 to the validation group in a 7:3 ratio. Independent risk factors influencing PHD were identified in the development group, leading to the establishment of a nomogram model for predicting PHD in patients with AMI. The model's predictive performance was evaluated using the receiver operating characteristic curve in both the development and validation groups.
Independent risk factors for PHD in patients with AMI included living alone, hyperlipidemia, age, diabetes mellitus, and digestive system diseases ( < 0.05). A nomogram model incorporating these five predictors accurately predicted PHD occurrence. The receiver operating characteristic curve analysis indicated area under the receiver operating characteristic curve values of 0.787 (95% confidence interval: 0.716-0.858) and 0.770 (95% confidence interval: 0.660-0.879) in the development and validation groups, respectively, demonstrating the model's good discriminatory ability. The Hosmer-Lemeshow goodness-of-fit test revealed no statistically significant disparity between the anticipated and observed incidence of PHD in both development and validation cohorts ( > 0.05), indicating satisfactory model calibration.
The nomogram model, developed with independent risk factors, accurately forecasts PHD likelihood in AMI individuals, enabling efficient identification of PHD risk in these patients.
急性心肌梗死（AMI）是一种严重的心血管疾病，由冠状动脉阻塞导致心肌缺血坏死引起。及时就医对于成功治疗AMI至关重要，而延误则会增加患者死亡风险。院前延误时间（PDT）是缩短治疗时间的一项重大挑战，因为识别AMI高危患者仍然困难。本研究旨在构建一个风险预测模型，以识别高危患者并制定有针对性的策略，实现有效且及时的护理，最终缩短PDT并改善治疗结果。
构建一个列线图模型，用于预测AMI患者的院前延误（PHD）可能性，并评估该列线图模型预测PHD风险的准确性。
采用回顾性队列设计，调查2022年1月至2022年9月期间确诊的AMI患者中PHD的预测因素。该研究纳入252例患者，其中180例按7:3的比例随机分配至开发组，其余72例分配至验证组。在开发组中确定影响PHD的独立危险因素，从而建立一个预测AMI患者PHD的列线图模型。使用受试者工作特征曲线在开发组和验证组中评估该模型的预测性能。
AMI患者中PHD的独立危险因素包括独居、高脂血症、年龄、糖尿病和消化系统疾病（<0.05）。包含这五个预测因素的列线图模型准确预测了PHD的发生情况。受试者工作特征曲线分析表明，开发组和验证组的受试者工作特征曲线下面积值分别为0.787（95%置信区间：0.716-0.858）和0.770（95%置信区间：0.660-0.879），表明该模型具有良好的区分能力。Hosmer-Lemeshow拟合优度检验显示，开发组和验证组中PHD的预期发病率与观察发病率之间无统计学显著差异（>0.05），表明模型校准良好。
利用独立危险因素开发的列线图模型能够准确预测AMI患者的PHD可能性，有助于有效识别这些患者的PHD风险。
