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Background <p>The high relapse rate in schizophrenia leads to ongoing morbidity for patients and their families and places a substantial burden on both family systems and society. This study aimed to identify predictors associated with relapse and to develop and validate a nomogram model for estimating relapse risk in individuals with first-episode schizophrenia.</p> Methods <p>A retrospective review of medical records was conducted on 426 patients with schizophrenia who were admitted for the first time between January 1, 2022, and December 31, 2023. A predictive model was developed based on the relapse status of patients within 1 year. Temporal validation was performed using an independent cohort of 182 patients with schizophrenia admitted from January to July 2024, serving as external validation data. Risk factors for 1-year relapse among individuals with first-episode schizophrenia were identified through Least Absolute Shrinkage and Selection Operator (LASSO) regression followed by multivariate logistic regression analyses. A nomogram model was then constructed to facilitate individualized prediction of relapse risk. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA).</p> Results <p>A nomogram model was developed by incorporating eight risk factors: onset form, co-residence, family type, place of residence, suicide risk, lack of interest/socialization, personal hygiene, and behavioral slowness. The model achieved an area under the curve (AUC) of 0.754 [95% confidence interval (CI) (0.707–0.802)] in the training set and 0.742 [95% CI (0.669–0.815)] in the external validation set. Calibration curves indicated good calibration, and DCA supported the clinical utility of the model.</p> Conclusion <p>The model demonstrates moderate discriminatory power in predicting relapse within 1 year post-discharge for patients with first-episode schizophrenia. Given the accessibility of the predictive variables, clinicians can utilize this tool to predict and manage relapse in psychiatric patients.</p> Clinical trial number <p>Not applicable.</p>

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Development and validation of a model for predicting relapse in patients with first-episode schizophrenia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Wenlong Tang,
  • Zhidan Wei,
  • Chunlan Guan,
  • Jiali Yuan,
  • Lu Sun,
  • Xinmiao Zhang,
  • Huixia Cui

摘要

Background

The high relapse rate in schizophrenia leads to ongoing morbidity for patients and their families and places a substantial burden on both family systems and society. This study aimed to identify predictors associated with relapse and to develop and validate a nomogram model for estimating relapse risk in individuals with first-episode schizophrenia.

Methods

A retrospective review of medical records was conducted on 426 patients with schizophrenia who were admitted for the first time between January 1, 2022, and December 31, 2023. A predictive model was developed based on the relapse status of patients within 1 year. Temporal validation was performed using an independent cohort of 182 patients with schizophrenia admitted from January to July 2024, serving as external validation data. Risk factors for 1-year relapse among individuals with first-episode schizophrenia were identified through Least Absolute Shrinkage and Selection Operator (LASSO) regression followed by multivariate logistic regression analyses. A nomogram model was then constructed to facilitate individualized prediction of relapse risk. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA).

Results

A nomogram model was developed by incorporating eight risk factors: onset form, co-residence, family type, place of residence, suicide risk, lack of interest/socialization, personal hygiene, and behavioral slowness. The model achieved an area under the curve (AUC) of 0.754 [95% confidence interval (CI) (0.707–0.802)] in the training set and 0.742 [95% CI (0.669–0.815)] in the external validation set. Calibration curves indicated good calibration, and DCA supported the clinical utility of the model.

Conclusion

The model demonstrates moderate discriminatory power in predicting relapse within 1 year post-discharge for patients with first-episode schizophrenia. Given the accessibility of the predictive variables, clinicians can utilize this tool to predict and manage relapse in psychiatric patients.

Clinical trial number

Not applicable.