Identifying Modifiable Risk Factors for Relapse in Patients With Schizophrenia in China
BackgroundPreventing 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...
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Frontiers Media S.A.
2020-09-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fpsyt.2020.574763/full |
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| author | Wei-Feng Mi Xiao-Min Chen Teng-Teng Fan Serik Tabarak Jing-Bo Xiao Yong-Zhi Cao Xiao-Yu Li Yan-Ping Bao Ying Han Ying Han Ling-Zhi Li Ying Shi Li-Hua Guo Xiao-Zhi Wang Yong-Qiao Liu Zhan-Min Wang Jing-Xu Chen Feng-Chun Wu Wen-Bin Ma Hua-Fang Li Wei-Dong Xiao Fei-Hu Liu Wen Xie Hong-Yan Zhang Lin Lu Lin Lu Lin Lu Lin Lu |
| author_facet | Wei-Feng Mi Xiao-Min Chen Teng-Teng Fan Serik Tabarak Jing-Bo Xiao Yong-Zhi Cao Xiao-Yu Li Yan-Ping Bao Ying Han Ying Han Ling-Zhi Li Ying Shi Li-Hua Guo Xiao-Zhi Wang Yong-Qiao Liu Zhan-Min Wang Jing-Xu Chen Feng-Chun Wu Wen-Bin Ma Hua-Fang Li Wei-Dong Xiao Fei-Hu Liu Wen Xie Hong-Yan Zhang Lin Lu Lin Lu Lin Lu Lin Lu |
| author_sort | Wei-Feng Mi |
| collection | DOAJ |
| description | BackgroundPreventing 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.MethodsA 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.ResultsThe 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.ConclusionsFor 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. |
| first_indexed | 2024-12-11T03:24:41Z |
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| id | doaj.art-d3347f8154e142ef8d9bd64c1ff3e7d3 |
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| issn | 1664-0640 |
| language | English |
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| publishDate | 2020-09-01 |
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| series | Frontiers in Psychiatry |
| spelling | doaj.art-d3347f8154e142ef8d9bd64c1ff3e7d32022-12-22T01:22:32ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402020-09-011110.3389/fpsyt.2020.574763574763Identifying Modifiable Risk Factors for Relapse in Patients With Schizophrenia in ChinaWei-Feng Mi0Xiao-Min Chen1Teng-Teng Fan2Serik Tabarak3Jing-Bo Xiao4Yong-Zhi Cao5Xiao-Yu Li6Yan-Ping Bao7Ying Han8Ying Han9Ling-Zhi Li10Ying Shi11Li-Hua Guo12Xiao-Zhi Wang13Yong-Qiao Liu14Zhan-Min Wang15Jing-Xu Chen16Feng-Chun Wu17Wen-Bin Ma18Hua-Fang Li19Wei-Dong Xiao20Fei-Hu Liu21Wen Xie22Hong-Yan Zhang23Lin Lu24Lin Lu25Lin Lu26Lin Lu27Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaDepartment of Psychiatry, Affiliated Psychological Hospital of Anhui Medical University, Anhui Mental Health Center, Hefei Fourth People’s Hospital, Hefei, ChinaPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaPeking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, ChinaPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaKey Laboratory of High Confidence Software Technologies (MOE), Department of Computer Science and Technology, Peking University, Beijing, ChinaKey Laboratory of High Confidence Software Technologies (MOE), Department of Computer Science and Technology, Peking University, Beijing, ChinaNational Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, ChinaNational Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, ChinaDepartment of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, ChinaPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaDepartment of Psychiatry, The Fourth People’s Hospital of Dalian Jinzhou District, Dalian, ChinaDepartment of Psychiatry, The Sixth People’s Hospital of Hebei Province, Baoding, ChinaDepartment of Psychiatry, Rongjun Hospital of Hebei Province, Baoding, China0Department of Psychiatry, Beijing HuiLongGuan Hospital, Beijing, China1Department of Psychiatry, Guangzhou Psychiatric Hospital, Guangzhou, China2Department of Psychiatry, Jinzhou Kangning Hospital, Jinzhou, China3Department of Psychiatry, Shanghai Mental Health Center, Shanghai, China4Department of Psychiatry, The People’s Hospital of Hubei Province, Wuhan, China5Department of Psychiatry, The Mental Health Center of Xi’an, Xi’an, ChinaDepartment of Psychiatry, Affiliated Psychological Hospital of Anhui Medical University, Anhui Mental Health Center, Hefei Fourth People’s Hospital, Hefei, ChinaPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaPeking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, ChinaNational Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, ChinaDepartment of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, ChinaBackgroundPreventing 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.MethodsA 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.ResultsThe 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.ConclusionsFor 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.https://www.frontiersin.org/article/10.3389/fpsyt.2020.574763/fullschizophreniarelapserisk factorspredictorsdecision-tree model |
| spellingShingle | Wei-Feng Mi Xiao-Min Chen Teng-Teng Fan Serik Tabarak Jing-Bo Xiao Yong-Zhi Cao Xiao-Yu Li Yan-Ping Bao Ying Han Ying Han Ling-Zhi Li Ying Shi Li-Hua Guo Xiao-Zhi Wang Yong-Qiao Liu Zhan-Min Wang Jing-Xu Chen Feng-Chun Wu Wen-Bin Ma Hua-Fang Li Wei-Dong Xiao Fei-Hu Liu Wen Xie Hong-Yan Zhang Lin Lu Lin Lu Lin Lu Lin Lu Identifying Modifiable Risk Factors for Relapse in Patients With Schizophrenia in China Frontiers in Psychiatry schizophrenia relapse risk factors predictors decision-tree model |
| title | Identifying Modifiable Risk Factors for Relapse in Patients With Schizophrenia in China |
| title_full | Identifying Modifiable Risk Factors for Relapse in Patients With Schizophrenia in China |
| title_fullStr | Identifying Modifiable Risk Factors for Relapse in Patients With Schizophrenia in China |
| title_full_unstemmed | Identifying Modifiable Risk Factors for Relapse in Patients With Schizophrenia in China |
| title_short | Identifying Modifiable Risk Factors for Relapse in Patients With Schizophrenia in China |
| title_sort | identifying modifiable risk factors for relapse in patients with schizophrenia in china |
| topic | schizophrenia relapse risk factors predictors decision-tree model |
| url | https://www.frontiersin.org/article/10.3389/fpsyt.2020.574763/full |
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