Identifying Modifiable Risk Factors for Relapse in Patients With Schizophrenia in China

Abstract

Background: Preventing relapse of schizophrenic patients is really a challenge. The present study sought to provide more explicit evidence and factors of different grades and weights by a series of step-by-step analysis through χ² test, logistic regression analysis and decision-tree model. The results of this study may contribute to controlling relapse of schizophrenic patients.

Methods: A total of 1,487 schizophrenia patients were included who were 18-65 years of age and discharged from 10 hospitals in China from January 2009 to August 2009 and from September 2011 to February 2012 with improvements or recovery of treatment effect. We used a questionnaire to collect information about relapse and correlative factors during one year after discharge by medical record collection and telephone interview. The χ² test and logistic regression analysis were used to identify risk factors and high-risk factors firstly, and then a decision-tree model was used to find predictive factors.

Results: The χ² test found nine risk factors which were associated with relapse. Logistic regression analysis also showed four high-risk factors further (medication adherence, occupational status, ability of daily living, payment method of medical costs). At last, a decision-tree model revealed four predictors of relapse; it showed that medication adherence was the first grade and the most powerful predictor of relapse (relapse rate for adherence vs. nonadherence: 22.9 vs. 55.7%, χ² = 116.36, p < 0.001). The second grade factor was occupational status (employment vs. unemployment: 19.7 vs. 42.7%, χ² = 17.72, p < 0.001); the third grade factors were ability of daily living (normal vs. difficult: 28.4 vs. 54.3%, χ² = 8.61, p = 0.010) and household income (household income ≥ 3000 RMB vs. <3000 RMB: 28.6 vs. 42.4%, χ² = 6.30, p = 0.036). The overall positive predictive value (PPV) of the logistic regression was 0.740, and the decision-tree model was 0.726. Both models were reliable.

Conclusions: For schizophrenic patients discharged from hospital, who had good medication adherence, more higher household income, be employed and normal ability of daily living, would be less likely to relapse. Decision tree provides a new path for doctors to find the schizophrenic inpatient's relapse risk and give them reasonable treatment suggestions after discharge.

Keywords: decision-tree model; predictors; relapse; risk factors; schizophrenia.

Figure 1

Study design.

Figure 2

Decision-tree model of predictors of relapse (training model, n = 1,020).