运用故障树分析心梗PCI术后一年内再入院的风险因素研究

Research on Risk Factors for Readmission within One Year after PCI for Myocardial Infarction Using Fault Tree Analysis

摘要: 目的：运用故障树分析法，系统识别和分析心梗患者PCI术后一年内再入院的风险因素，构建风险预测模型，为早期筛查高危人群、制定针对性护理干预措施、降低再入院率提供理论依据和实践指导。方法：采用病例对照研究设计，回顾性收集2018~2023年于陕西省人民医院心血管内科行PCI治疗的心梗患者资料，根据术后一年内是否再入院分为病例组和对照组。通过系统文献回顾和两轮专家咨询，确定风险因子清单。以“一年内再入院”为顶事件，自上而下构建故障树，进行定性与定量分析，计算最小割集及各基本事件的结构重要度。结果：共纳入病例组126例，对照组252例。故障树模型包含6个次级事件和35个基本事件，最小割集共47个。定性分析显示，或门占65%，提示系统风险较高；定量分析识别出5个关键风险因素：出院时左心室射血分数 < 40% (结构重要度0.213)、合并糖尿病(0.187)、服药依从性差(0.165)、缺乏家庭支持(0.142)、未参加心脏康复(0.128)。多因素logistic回归验证了上述因素与再入院的独立关联(P < 0.05)。结论：故障树分析法能直观揭示心梗PCI术后再入院的多因素层级关系，心脏功能、合并症、自我管理及社会支持是主要风险维度。针对关键节点实施分层干预，降低再入院率。

Abstract: Objective: To identify and analyze the risk factors for readmission within one year after PCI in patients with myocardial infarction using fault tree analysis, construct a risk prediction model, and provide theoretical basis and practical guidance for early screening of high-risk groups, formulating targeted nursing intervention measures, and reducing readmission rates. Methods: A case-control study design was adopted. Data of patients with myocardial infarction who underwent PCI in the Department of Cardiology of Shaanxi Provincial People’s Hospital from 2018 to 2023 were retrospectively collected. The patients were divided into the case group and the control group based on whether they were readmitted within one year after the operation. Through systematic literature review and two rounds of expert consultation, a list of risk factors was determined. With “readmission within one year” as the top event, a fault tree was constructed from top to bottom for qualitative and quantitative analysis, and the minimum cut sets and structural importance of each basic event were calculated. Results: A total of 126 cases were included in the case group and 252 cases in the control group. The fault tree model included 6 secondary events and 35 basic events, with a total of 47 minimum cut sets. Qualitative analysis showed that OR gates accounted for 65%, indicating a relatively high system risk. Quantitative analysis identified 5 key risk factors: left ventricular ejection fraction < 40% at discharge (structural importance 0.213), diabetes mellitus (0.187), poor medication compliance (0.165), lack of family support (0.142), and non-participation in cardiac rehabilitation (0.128). Multivariate logistic regression verified the independent association of these factors with readmission (P < 0.05). Conclusion: Fault tree analysis can visually reveal the multi-factor hierarchical relationship of readmission after PCI in myocardial infarction patients. Cardiac function, comorbidities, self-management, and social support are the main risk dimensions. Stratified intervention targeting key nodes can reduce readmission rates.