Development of a risk prediction model for relapse in patients with schizophrenia based on a systematic review and...

The high relapse rate in patients with schizophrenia imposes a significant burden on both families and society, hindering patients' recovery. Predictive modeling of relapse risk factors aids in early identification of high-risk patients for timely intervention.

A comprehensive search of multiple databases was conducted to collect both domestic and international publications on factors influencing relapse in patients with schizophrenia, up to July 1, 2024. After literature screening, data extraction, and quality assessment by two researchers, meta-analysis was performed using RevMan 5.4 software to calculate combined odds ratios (OR) and 95% confidence intervals (CIs). A risk prediction model was constructed based on the natural logarithmic transformation of the composite hazard values. Inpatient medical records of patients with schizophrenia from Wuhu Fourth People's Hospital, collected between January 2022 and July 2024, were screened for analysis. The model's effectiveness in predicting relapse risk was validated through multiple curves and decision analysis.

A total of 35 papers (27 cohort studies and 8 case-control studies), involving 159,973 participants and 5924 relapses, were included in the analysis. The meta-analysis identified 11 relapse risk factors. The corresponding logistic regression risk prediction model is: Logit(P) = α + 1.477X₁ + 1.495X₂ + 0.604X₃ + 0.668X₄ + 1.637X₅ + 1.351X₆ + 1.141X₇ + 1.413X₈ + 0.888X₉ + 0.582X₁₀ + 1.281X₁₁. The model was validated using an external dataset of 452 medical records, demonstrating good diagnostic performance. The Hosmer-Lemeshow test, calibration curves, and decision analysis further confirmed the model's accuracy and high clinical applicability.

An evidence-based predictive model for relapse risk in patients with schizophrenia was developed, demonstrating moderate predictive ability. This model allows for early identification of high-risk patients and facilitates targeted interventions to improve outcomes.