Weifang Medical University, Weifang, China.
Nursing Department, Weifang Medical University Affiliated Hospital, Weifang, China.
Ann Palliat Med. 2022 Sep;11(9):2897-2905. doi: 10.21037/apm-22-881.
At present, there are many influencing factors of post-traumatic stress disorder (PTSD) symptoms in patients with acute myocardial infarction (AMI), but based on this, there are few studies on the risk prediction model of PTSD symptoms. The aim of this study was to investigate the risk factors of PTSD symptoms in patients with AMI and to construct a risk prediction model.
From April 2021 to March 2022, 287 patients were enrolled from a hospital in Shandong Province, China. According to the PTSD Checklist (PCL-C) scores 30 days after discharge, the participants were divided into a PTSD symptoms group (92 cases) and a non-PTSD symptoms group (195 cases). The demographic data, disease factors, treatment factors, and laboratory examination indicators were compared between the 2 groups; independent risk factors were screened out, and a risk prediction model was constructed by logistic regression. Area under the curve (AUC) was used as the internal verification of the model prediction. From April 2022 to June 2022, 72 patients with AMI in a hospital in Shandong Province were selected. PCL-C data were collected 30 days after discharge, and finally external validation of the model was performed.
Five factors, including gender [odds ratio (OR) =3.325], diabetes history (OR =2.292), creatine kinase isozyme (OR =1.046), insomnia score (OR =2.045), and fear of disease progression score (OR =1.126) were included to construct the risk prediction model. According to the Hosmer-Lemeshow test, P=0.785. The AUC was 0.910, the maximum value of Youden index was 0.751, the sensitivity was 0.870, the specificity was 0.881, and the accuracy rate of practical application was 67.64%.
The risk prediction model of PTSD symptoms in patients with AMI established in this study is consistent and effective. It can provide a reference for clinical assessment of PTSD symptoms risk in patients with AMI.
目前，急性心肌梗死（AMI）患者的创伤后应激障碍（PTSD）症状存在诸多影响因素，但基于此，针对 PTSD 症状的风险预测模型研究较少。本研究旨在探讨 AMI 患者 PTSD 症状的危险因素，并构建风险预测模型。
2021 年 4 月至 2022 年 3 月，连续纳入山东省某医院 287 例患者。根据出院后 30 天 PTSD 检查表（PCL-C）评分，将患者分为 PTSD 症状组（92 例）和非 PTSD 症状组（195 例）。比较两组患者的人口学资料、疾病因素、治疗因素和实验室检查指标；筛选独立危险因素，采用 logistic 回归构建风险预测模型。采用曲线下面积（AUC）对模型预测进行内部验证。2022 年 4 月至 6 月，连续纳入山东省某医院 AMI 患者 72 例。收集患者出院后 30 天的 PCL-C 数据，最终对模型进行外部验证。
构建的风险预测模型包含性别（OR=3.325）、糖尿病史（OR=2.292）、肌酸激酶同工酶（OR=1.046）、失眠评分（OR=2.045）、疾病进展恐惧评分（OR=1.126）共 5 个因素。经 Hosmer-Lemeshow 检验，P=0.785。AUC 为 0.910，最大约登指数为 0.751，灵敏度为 0.870，特异度为 0.881，实际应用准确率为 67.64%。
本研究构建的 AMI 患者 PTSD 症状风险预测模型具有较好的一致性和有效性，可为临床评估 AMI 患者 PTSD 症状风险提供参考。
