PDF(Pubmed)
Abstract：
BACKGROUND: ST-segment elevation myocardial infarction (STEMI) represents the most harmful clinical manifestation of coronary artery disease. Risk assessment plays a beneficial role in determining both the treatment approach and the appropriate time for discharge. Hierarchical agglomerative clustering (HAC), a machine learning algorithm, is an innovative approach employed for the categorization of patients with comparable clinical and laboratory features. The aim of the present study was to investigate the role of HAC in categorizing STEMI patients and to compare the results of these patients.
METHODS: A total of 3205 patients who were diagnosed with STEMI at the university hospital emergency clinic between 2015 and 2023 were included in the study. The patients were divided into 2 different phenotypic disease clusters using the HAC method, and their outcomes were compared.
RESULTS: In the present study, a total of 3205 STEMI patients were included; 2731 patients were in cluster 1, and 474 patients were in cluster 2. Mortality was observed in 147 (5.4%) patients in cluster 1 and 108 (23%) patients in cluster 2 (chi-square P value < 0.01). Survival analysis revealed that patients in cluster 2 had a significantly greater risk of death than patients in cluster 1 did (log-rank P < 0.001). After adjustment for age and sex in the Cox proportional hazards model, cluster 2 exhibited a notably greater risk of death than did cluster 1 (HR = 3.51, 95% CI = 2.71-4.54; P < 0.001).
CONCLUSIONS: Our study showed that the HAC method may be a potential tool for predicting one-month mortality in STEMI patients.
METHODS: A total of 3205 patients who were diagnosed with STEMI at the university hospital emergency clinic between 2015 and 2023 were included in the study. The patients were divided into 2 different phenotypic disease clusters using the HAC method, and their outcomes were compared.
RESULTS: In the present study, a total of 3205 STEMI patients were included; 2731 patients were in cluster 1, and 474 patients were in cluster 2. Mortality was observed in 147 (5.4%) patients in cluster 1 and 108 (23%) patients in cluster 2 (chi-square P value < 0.01). Survival analysis revealed that patients in cluster 2 had a significantly greater risk of death than patients in cluster 1 did (log-rank P < 0.001). After adjustment for age and sex in the Cox proportional hazards model, cluster 2 exhibited a notably greater risk of death than did cluster 1 (HR = 3.51, 95% CI = 2.71-4.54; P < 0.001).
CONCLUSIONS: Our study showed that the HAC method may be a potential tool for predicting one-month mortality in STEMI patients.
摘要：
背景：ST段抬高型心肌梗死（STEMI）是冠状动脉疾病最有害的临床表现。风险评估在确定治疗方法和适当的出院时间方面起着有益的作用。分层聚集聚类(HAC)，机器学习算法，是一种创新的方法，用于对具有可比临床和实验室特征的患者进行分类。本研究的目的是研究HAC在STEMI患者分类中的作用，并比较这些患者的结果。
方法：将2015年至2023年在大学医院急诊诊所诊断为STEMI的3205例患者纳入研究。使用HAC方法将患者分为2个不同的表型疾病簇，并对其结果进行了比较。
结果：在本研究中，共纳入3,205例STEMI患者;1组2731例患者和2组474例患者.在第1组147例（5.4％）患者和第2组108例（23％）患者中观察到死亡率（卡方P值<0.01）。生存分析显示，第2组患者的死亡风险明显高于第1组患者（log-rankP<0.001）。在Cox比例风险模型中调整了年龄和性别后，第2组的死亡风险显著高于第1组(HR=3.51,95%CI=2.71-4.54;P<0.001).
结论：我们的研究表明，HAC方法可能是预测STEMI患者一个月死亡率的潜在工具。
方法：将2015年至2023年在大学医院急诊诊所诊断为STEMI的3205例患者纳入研究。使用HAC方法将患者分为2个不同的表型疾病簇，并对其结果进行了比较。
结果：在本研究中，共纳入3,205例STEMI患者;1组2731例患者和2组474例患者.在第1组147例（5.4％）患者和第2组108例（23％）患者中观察到死亡率（卡方P值<0.01）。生存分析显示，第2组患者的死亡风险明显高于第1组患者（log-rankP<0.001）。在Cox比例风险模型中调整了年龄和性别后，第2组的死亡风险显著高于第1组(HR=3.51,95%CI=2.71-4.54;P<0.001).
结论：我们的研究表明，HAC方法可能是预测STEMI患者一个月死亡率的潜在工具。
