Construction of readmission risk prediction model for male inpatients with schizophrenia in a hospital in Zhoushan
10.3969/j.issn.1673-9701.2024.30.002
- VernacularTitle:舟山市某医院男性精神分裂症患者再住院风险预测模型构建
- Author:
Kai NIU
1
;
Yuefeng WANG
;
Yongfa WU
;
Juner LE
Author Information
1. 舟山市第二人民医院精神科,浙江舟山 316000
- Keywords:
Schizophrenia;
Male;
Readmission;
Risk prediction model;
Nomogram
- From:
China Modern Doctor
2024;62(30):7-12
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the readmission of male schizophrenia patients in a hospital of Zhoushan and its influencing factors,and to build a nomogram model.Methods A total of 183 male schizophrenics patients admitted to the Second People's Hospital of Zhoushan from January to December 2022 were analyzed retrospectively.Patients were divided into readmission group (hospitalization frequency ≥ 2 times) and non-readmission group (hospitalization was once) based on the number of hospitalization.Perform univariate analysis on the data of two groups of patients using t test or x2 test.Multivariate Logistic regression model was used to investigate the influencing factors of readmission in schizophrenia patients within one year,and a nomogram prediction model was established.Results The rate of readmission was 49.18％ in 183 schizophrenic patients.Course of disease,duration of first hospitalization,and history of tobacco and alcohol consumption were independent risk factors for readmission (P<0.05).The area under the curve (AUC) of the receiver operating characteristic curve in the column chart model is 76.53.Conclusion The readmission rate of male schizophrenia patients in the Second People's Hospital of Zhoushan is high,and the course of disease,duration of first hospitalization,and history of tobacco and alcohol consumption are independent risk factors for the readmission of schizophrenia patients within one year.The establishment of the nomogram prediction model is expected to evaluate the high risk factors for the readmission of schizophrenia patients and implement targeted interventions to improve the quality of life of patients.
