Objective:To investigate phobia of acute myocardial infarction(AMI)patients after percutaneous coronary intervention(PCI), analyze its latent profile and explore the influencing factors in different categories.Methods:Three hundreds and thirty-five AMI patients who received PCI in Emergency Department ofthe First Affiliated Hospital of Naval Medical University from January 2021 to June 2022 were selected by convenient sampling method and prospective research as the survey objects. The basic situation questionnaire, cardiophobia scale (TSK-SV Heart), Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), Connor Davidson Psychoelasticity Scale (CD-RISC) and Posttraumatic Stress Disorder Checklist (PCL) were selected to investigate them, and the linear growth model was selected to analyze the latent profile of postoperative phobia in AMI patients.Results:The score of post-operative phobia in AMI patients was (44.47 ± 7.25) points, and the latent profile analysis showed that AMI patients were classified into psychological type (156 cases, 46.57%), physiological type (164 cases, 48.96%) and severe type (15 cases, 4.47%). The severe phobia type was selected as the reference group, and multiple logistic regression analysis showed that compared with the severe phobia type, age, resilience, posttraumatic stress disorder (PTSD) and no or mild anxiety were significant influencing factors for phobia after PCI in patients with psychophobia type AMI ( P<0.05), while age and resilience were significant influencing factors for phobia after PCI in patients with physiological phobia type AMI ( P<0.05). Conclusions:Through latent profile analysis, there are three types of phobia in AMI patients after PCI: psychophobia, physiological phobia and severe phobia. Postoperative phobia is affected by psychological resilience, PTSD, age, chronic disease and depression. Therefore, targeted intervention should be carried out for AMI patients based on different characteristics of phobia after PCI to enhance their enthusiasm for rehabilitation.

Objective:To construct the risk prediction model of unplanned readmission for elderly patients in emergency department within 30 days based on different machine learning algorithms, so as to help clinical staff identify high-risk patients early and formulate preventive interventions.Methods:A total of 1 207 elderly patients admitted to the emergency department of the First Affiliated Hospital of Naval Medical University from May 2022 to December 2023 were retrospectively selected as the study objects and were divided into the training set ( n = 842) and the test set ( n = 365) in a ratio of approximately 7∶3. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen the factors affecting the unplanned readmission of elderly patients within 30 days. Six prediction models, including extreme Gradient Boost (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost) Logistic regression, K-nearest neighbor (KNN) and Gauss Naive Bayes classification (GNB) were constructed respectively. The models were summarized, evaluated and validated, and the importance of key variables was analyzed using Shapley Additive Interpretation (SHAP). Results:Among 1 207 elderly patients, there were 842 in the training set, 430 males with a median age of 77 years, and 365 in the test set, 176 males with a median age of 78 years. Eight variable features were selected by LASSO regression. The GNB model performed the best among the 6 prediction models constructed based on XGBoost, LightGBM, AdaBoost, Logistic regression, KNN, GNB. The AUC of the test set was 0.818, and the sensitivity was 0.890, while the specificity was 0.660, and the train set and the verification set had strong fitting ability and high stability. The eight characteristics affecting the unplanned readmitted of elderly patients in the emergency department within 30 days were ranked in importance by age, chronic obstructive pulmonary disease, length of stay, Charson comorbidity index≥3, hypoproteinemia, abnormal vital signs≥2, stroke, anemia.Conclusions:The GNB model based on machine learning algorithms for unplanned readmission of elderly emergency patients within 30 days has good predictive performance, which helps medical staff to identify high-risk patients as early as possible before discharge, formulate targeted preventive measures, thereby reducing the short-term unplanned readmission rate of patients and improving their quality of life.