Abreu Daisy, Salomé Cabral M, Ribeiro Fernando
Department of Cardiology, Hospital de Santa Maria, Universidade de Lisboa, Lisbon, Portugal.
Centro de Estatística e Aplicações, Departamento de Estatística e Investigação Operacional, Faculdade de Ciências, Universidade de Lisboa, Portugal.
Int J Cardiol Heart Vessel. 2014 Jul 10;4:97-101. doi: 10.1016/j.ijchv.2014.06.007. eCollection 2014 Sep.
BACKGROUND/OBJECTIVES: The goal of this paper is to identify the predictors of delay in total ischemia time that would be the focus of improvement efforts in patients with ST-segment elevation myocardial infarction.
Data was collected retrospectively through the patient's clinical records and by direct telephone interview.Total ischemic time was categorized in two classes according to the elapsed time since symptom presentation until restored flow, less than 6 h and 6 h or less. Logistic regression analysis was applied to evaluate the relationship between total ischemic time and a set of variables. Discrimination ability of the model was also assessed, as well as sensitivity and specificity, through ROC curves.
Data from 128 patients, 74.22% males and 25.78% females, were analyzed. The average age was approximately 62 years (± 13.6).Six variables associated with total ischemia were selected in the final model: the patient age, the level of pain intensity, the region of origin, the socioeconomic status, the activity that the patient was performing at the time of symptoms onset, and the fact that the patient has been transferred from another hospital.
The identification of variables associated with the total ischemia time allows the recognition of patients with possibility of worse prognosis, for which should be directed educational efforts and also the identification of variables that can be modified to optimize the therapy.
背景/目的:本文的目的是确定ST段抬高型心肌梗死患者总缺血时间延迟的预测因素,这将是改善措施的重点。
通过患者临床记录和直接电话访谈回顾性收集数据。根据症状出现至血流恢复的时间,将总缺血时间分为两类,即小于6小时和6小时及以上。应用逻辑回归分析评估总缺血时间与一组变量之间的关系。还通过ROC曲线评估了模型的辨别能力以及敏感性和特异性。
分析了128例患者的数据,其中男性占74.22%,女性占25.78%。平均年龄约为62岁(±13.6)。最终模型中选择了6个与总缺血相关的变量:患者年龄、疼痛强度水平、发病地区、社会经济状况、症状发作时患者正在进行的活动以及患者是否从另一家医院转诊。
识别与总缺血时间相关的变量有助于识别预后可能较差的患者,对此应开展教育工作,同时识别可修改的变量以优化治疗。