Annals of palliative medicine · 2022
Development and validation of a risk prediction model for post-traumatic stress disorder symptoms in patients with acute myocardial infarction in China.
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Cui Kunxia, Sui Ping, Zang Xiaohui, Sun Yangyang, Liu Xiaohong
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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%.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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%.
CONCLUSIONS: 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.