Objective: To construct a recurrence prediction model for Elderly Schizophrenia Patients (ESCZP) and validate the model's spatial external applicability.
Methods: The modeling cohort consisted of 365 ESCZP cases from the Seventh People's Hospital of Dalian, admitted between May 2022 and April 2024. Variables were selected using Lasso-Logistic regression to construct the recurrence prediction model, with a nomogram plotted using the "RMS" package in R 4.3.3 software. Model validation was performed using 1,000 bootstrap resamples. Spatial external validation was conducted using 172 cases ESCZP from the Fourth Affiliated Hospital of Qiqihar Medical College during the same period. The model's discrimination, accuracy, and clinical utility were assessed using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA). The Hosmer-Lemeshow test was used to evaluate model fit.
Results: A total of 537 cases ESCZP were included based on inclusion and exclusion criteria, with 150 recurrences within two years and 387 non-recurrences. Lasso-Logistic regression analysis identified Medication status, Premorbid personality, Exercise frequency, Drug adverse reactions, Family care, Social support, and Life events as predictors of ESCZP recurrence. The AUC for the modeling cohort was 0.877 (95% CI: 0.837-0.917). For the external validation cohort, the AUC was 0.838 (95% CI: 0.776-0.899). Calibration curves indicated that the fit was close to the reference line, demonstrating high model stability. DCA results showed good net benefit at a threshold probability of 80%.
Conclusion: The nomogram prediction model developed based on Lasso-Logistic regression shows potential in identifying the risk of recurrence in ESCZP. However, further validation and refinement are needed before it can be applied in routine clinical practice.
1. Model Construction and Validation:A recurrence prediction model for ESCZP was constructed using Lasso-Logistic regression and validated for spatial external applicability. The model was based on 365 cases from the Seventh People’s Hospital of Dalian and validated with 172 cases from the Fourth Affiliated Hospital of Qiqihar Medical College, assessing discrimination, accuracy, and clinical utility through ROC curves, AUC, calibration curves, and DCA.2. Predictors and Performance:The identified predictors of ESCZP recurrence included Medication status, Premorbid personality, Exercise frequency, Drug adverse reactions, Family care, Social support, and Life events. The model demonstrated high performance, with an AUC of 0.877 for the modeling cohort and 0.838 for the external validation cohort, indicating good stability and fit as per the Hosmer-Lemeshow test.3. Clinical Utility and Generalizability:The nomogram prediction model effectively identifies high-risk populations for recurrence, showing good net benefit and generalizability. The study emphasizes the importance of non-invasive, easily measurable variables for constructing practical and economical prediction models, aiming to improve early identification and prevention of ESCZP relapse.
Keywords: Elderly; Prediction model; Recurrence; Schizophrenia.
© 2025. The Author(s).