Analysis of status and influencing factors of mental health in patients with systemic lupus erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disorder with varied clinical courses and prognoses, not only did the patients suffer from physical impairment, but also various physical and psychiatric comorbidities. Growing evidence have suggested that mental disorders in SLE patients, can lead to various adverse consequences. AIM: To explored the features and influencing factors of mental health in patients with SLE and clarifying the correlations between mental health and personality characteristics and perceived social support. The results would provide a basis for psychological intervention in patients with SLE. METHODS: The clinical data of 168 patients with SLE admitted at the First Affiliated Hospital of Hainan Medical University between June 2020 and June 2022 were collected. Psychological assessment and correlation analysis were conducted using the Symptom Checklist-90 (SCL-90) and Perceived Social Support Scale, and the collected data were compared with the national norms in China. The relevant factors influencing mental health were identified by statistical analysis. A general information questionnaire, the Revised Life Orientation Test, and Short-Form 36-Item Health Survey were employed to assess optimism level and quality of life (QoL), respectively. RESULTS: Patients with SLE obtained higher scores for the somatization, depression, anxiety, and phobic anxiety subscales than national norms (P < 0.05). A correlation was identified between total social support and total SCL-90 score or each subscale (P < 0.05). The factors significantly affecting patients' mental health were hormone dosage and disease activity index (DAI) (P < 0.05). The average optimism score of patients with SLE was 14.36 ± 4.42, and 30 cases were in the middle and lower levels. A positive correlation was found between optimism level and QoL scores. CONCLUSION: Patients with SLE develop psychological disorders at varying degrees, which are significantly influenced by hormone dosage and DAI. Patients' mental health should be closely monitored during clinical diagnosis and treatment and provided adequate support in establishing positive, healthy thinking and behavior patterns and improving their optimism level and QoL.

Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study.

BACKGROUND: Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology. METHODS: A retrospective study of patients' data was conducted using the Emergency Rescue Database of Chinese People's Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination (RFE) method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity and the areas under receiver operator characteristic curves (ROC-AUC). RESULTS: The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the Logistic regression were cardiac troponin T (CTnT) (coefficient=0.346, OR = 1.413), temperature (T) (coefficient=0.235, OR = 1.265), respiratory rate (RR) (coefficient= -0.206,OR = 0.814), serum kalium (K) (coefficient=0.137, OR = 1.146), pulse oxygen saturation (SPO2) (coefficient= -0.101, OR = 0.904), and albumin (ALB) (coefficient= -0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heartrate, and systolic blood pressure. CONCLUSIONS: The main clinical indicators of concern included CTnT, RR, SPO2, T, ALB and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.