医学
精神分裂症(面向对象编程)
过度拟合
自举(财务)
比例危险模型
住院治疗
精神病院
队列
精神科
急诊医学
内科学
医疗保健
机器学习
人工神经网络
计算机科学
金融经济学
经济
经济增长
作者
Akira Satô,Toshihiro Moriyama,Norio Watanabe,Kazushi Maruo,Toshi A Furukawa
标识
DOI:10.3389/fpsyt.2023.1242918
摘要
Objective Relapses and rehospitalization prevent the recovery of individuals with schizophrenia or related psychoses. We aimed to build a model to predict the risk of rehospitalization among people with schizophrenia or related psychoses, including those with multiple episodes. Methods This retrospective cohort study included individuals aged 18 years or older, with schizophrenia or related psychoses, and discharged between January 2014 and December 2018 from one of three Japanese psychiatric hospital acute inpatient care ward. We collected nine predictors at the time of recruitment, followed up with the participants for 12 months, and observed whether psychotic relapse had occurred. Next, we applied the Cox regression model and used an elastic net to avoid overfitting. Then, we examined discrimination using bootstrapping, Steyerberg’s method, and “leave-one-hospital-out” cross-validation. We also constructed a bias-corrected calibration plot. Results Data from a total of 805 individuals were analyzed. The significant predictors were the number of previous hospitalizations (HR 1.42, 95% CI 1.22–1.64) and the current length of stay in days (HR 1.31, 95% CI 1.04–1.64). In model development for relapse, Harrell’s c-index was 0.59 (95% CI 0.55–0.63). The internal and internal-external validation for rehospitalization showed Harrell’s c-index to be 0.64 (95% CI 0.59–0.69) and 0.66 (95% CI 0.57–0.74), respectively. The calibration plot was found to be adequate. Conclusion The model showed moderate discrimination of readmission after discharge. Carefully defining a research question by seeking needs among the population with chronic schizophrenia with multiple episodes may be key to building a useful model.
科研通智能强力驱动
Strongly Powered by AbleSci AI