Ryan Catherine J, DeVon Holli A, Horne Rob, King Kathleen B, Milner Kerry, Moser Debra K, Quinn Jill R, Rosenfeld Anne, Hwang Seon Young, Zerwic Julie J
College of Nursing, University of Illinois at Chicago, Chicago, IL 60612, USA.
Nurs Res. 2007 Mar-Apr;56(2):72-81. doi: 10.1097/01.NNR.0000263968.01254.d6.
Early recognition of acute myocardial infarction (AMI) symptoms and reduced time to treatment may reduce morbidity and mortality. People having AMI experience a constellation of symptoms, but the common constellations or clusters of symptoms have yet to be identified.
To identify clusters of symptoms that represent AMI.
This was a secondary data analysis of nine descriptive, cross-sectional studies that included data from 1,073 people having AMI in the United States and England. Data were analyzed using latent class cluster analysis, an a theoretical method that uses only information contained in the data.
Five distinct clusters of symptoms were identified. Age, race, and sex were statistically significant in predicting cluster membership. None of the symptom clusters described in this analysis included all of the symptoms that are considered typical. In one cluster, subjects had only a moderate to low probability of experiencing any of the symptoms analyzed.
Symptoms of AMI occur in clusters, and these clusters vary among persons. None of the clusters identified in this study included all of the symptoms that are included typically as symptoms of AMI (chest discomfort, diaphoresis, shortness of breath, nausea, and lightheadedness). These AMI symptom clusters must be communicated clearly to the public in a way that will assist them in assessing their symptoms more efficiently and will guide their treatment-seeking behavior. Symptom clusters for AMI must also be communicated to the professional community in a way that will facilitate assessment and rapid intervention for AMI.
早期识别急性心肌梗死(AMI)症状并缩短治疗时间可降低发病率和死亡率。患有AMI的人会经历一系列症状,但常见的症状组合或集群尚未确定。
识别代表AMI的症状集群。
这是对9项描述性横断面研究的二次数据分析,这些研究纳入了美国和英国1073例AMI患者的数据。使用潜在类别聚类分析对数据进行分析,这是一种仅使用数据中包含信息的无理论方法。
识别出5个不同的症状集群。年龄、种族和性别在预测集群成员方面具有统计学意义。本分析中描述的症状集群均未包括所有被认为是典型的症状。在一个集群中,受试者出现所分析的任何症状的概率仅为中度至低度。
AMI症状以集群形式出现,且这些集群因人而异。本研究中识别出的集群均未包括通常作为AMI症状(胸痛、出汗、呼吸急促、恶心和头晕)的所有症状。必须以一种有助于公众更有效地评估其症状并指导其寻求治疗行为的方式,将这些AMI症状集群清晰地传达给公众。AMI症状集群还必须以一种便于对AMI进行评估和快速干预的方式传达给专业群体。