A Random Forest Model for Predicting Social Functional Improvement in Chinese Patients with Schizophrenia After 3 Months of Atypical Antipsychotic Monopharmacy: A Cohort Study

Yange Li,1 Lei Zhang,1 Yan Zhang,1 Hui Wen,1 Jingjing Huang,1,2 Yifeng Shen,1– 3 Huafang Li1– 3 1Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Shanghai Key Laboratory of Psychotic Disorders, Mental Health...

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Autores principales:	Li Y, Zhang L, Zhang Y, Wen H, Huang J, Shen Y, Li H
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	schizophrenia atypical antipsychotics social functional improvement personal and social performance (psp) Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/f9c04ab2d77f4a1c8421cf784bb07a18
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spelling	oai:doaj.org-article:f9c04ab2d77f4a1c8421cf784bb07a182021-12-02T16:31:09ZA Random Forest Model for Predicting Social Functional Improvement in Chinese Patients with Schizophrenia After 3 Months of Atypical Antipsychotic Monopharmacy: A Cohort Study1178-2021https://doaj.org/article/f9c04ab2d77f4a1c8421cf784bb07a182021-03-01T00:00:00Zhttps://www.dovepress.com/a-random-forest-model-for-predicting-social-functional-improvement-in--peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Yange Li,1 Lei Zhang,1 Yan Zhang,1 Hui Wen,1 Jingjing Huang,1,2 Yifeng Shen,1– 3 Huafang Li1– 3 1Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Shanghai Key Laboratory of Psychotic Disorders, Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 3Shanghai Clinical Research Center for Mental Health, Shanghai, People’s Republic of ChinaCorrespondence: Huafang LiShanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600, Wan Ping Nan Lu, Shanghai, 200030, People’s Republic of ChinaTel +86 21 34773122Fax +86-21-54240982Email lhlh_5@163.comPurpose: Impaired social functions contribute to the burden of schizophrenia patients and their families, but predictive tools of social functioning prognosis and specific factors are undefined in Chinese clinical practice. This article explores a machine learning tool to identify whether patients will achieve significant social functional improvement after 3 months of atypical antipsychotic monopharmacy and finds the defined risk factors using a multicenter clinical study.Patients and Methods: A multicenter study on atypical antipsychotic (AAP) treatment in Chinese patients with schizophrenia (SALT-C) was conducted from July 2011 to August 2018. Data from 550 patients with AAP monopharmacy from their baseline to their 3-month follow-up were used to establish machine learning tools after screening. The positive outcome was an increase in the Personal and Social Performance (PSP) scale score by ≥ 10 points. The predictors were a range of investigator-rated assessments on symptoms, functioning, the safety of AAPs and illness history. The Least Absolute Shrinkage and Selection Operator (LASSO) was used for the feature screening and ranking of the predicted variables. The random forest algorithm and five-fold cross-validation for optimizing the model were selected to ensure the generalizability and precision.Results: There were 137 patients (mean [SD] age, 41.1 [16.8] years; 77 [58.8%] female) who had a good social functional prognosis. A lower PSP score, taking a mood stabilizer, a high total Positive and Negative Symptom Scale (PANSS) and PANSS general subscale score, unemployment, a hepatic injury with medication, comorbid cardiovascular disease and being male predicted poor PSP outcomes. The generalizability of the PSP predictive tool was estimated with the precision–recall curve (accuracy of 79.5%, negative predictive value of 92.6% and positive predictive value of 57.1%) and receiver operating characteristic curve (ROC) (specificity of 81.8% and sensitivity of 78.7%).Conclusion: The machine learning tool established using our current real-world data could assist in predicting PSP outcome by several clinical factors.Keywords: schizophrenia, atypical antipsychotics, social functional improvement, Personal and Social Performance, PSPLi YZhang LZhang YWen HHuang JShen YLi HDove Medical Pressarticleschizophreniaatypical antipsychoticssocial functional improvementpersonal and social performance (psp)Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 17, Pp 847-857 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	schizophrenia atypical antipsychotics social functional improvement personal and social performance (psp) Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	schizophrenia atypical antipsychotics social functional improvement personal and social performance (psp) Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Li Y Zhang L Zhang Y Wen H Huang J Shen Y Li H A Random Forest Model for Predicting Social Functional Improvement in Chinese Patients with Schizophrenia After 3 Months of Atypical Antipsychotic Monopharmacy: A Cohort Study
description	Yange Li,1 Lei Zhang,1 Yan Zhang,1 Hui Wen,1 Jingjing Huang,1,2 Yifeng Shen,1– 3 Huafang Li1– 3 1Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Shanghai Key Laboratory of Psychotic Disorders, Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 3Shanghai Clinical Research Center for Mental Health, Shanghai, People’s Republic of ChinaCorrespondence: Huafang LiShanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600, Wan Ping Nan Lu, Shanghai, 200030, People’s Republic of ChinaTel +86 21 34773122Fax +86-21-54240982Email lhlh_5@163.comPurpose: Impaired social functions contribute to the burden of schizophrenia patients and their families, but predictive tools of social functioning prognosis and specific factors are undefined in Chinese clinical practice. This article explores a machine learning tool to identify whether patients will achieve significant social functional improvement after 3 months of atypical antipsychotic monopharmacy and finds the defined risk factors using a multicenter clinical study.Patients and Methods: A multicenter study on atypical antipsychotic (AAP) treatment in Chinese patients with schizophrenia (SALT-C) was conducted from July 2011 to August 2018. Data from 550 patients with AAP monopharmacy from their baseline to their 3-month follow-up were used to establish machine learning tools after screening. The positive outcome was an increase in the Personal and Social Performance (PSP) scale score by ≥ 10 points. The predictors were a range of investigator-rated assessments on symptoms, functioning, the safety of AAPs and illness history. The Least Absolute Shrinkage and Selection Operator (LASSO) was used for the feature screening and ranking of the predicted variables. The random forest algorithm and five-fold cross-validation for optimizing the model were selected to ensure the generalizability and precision.Results: There were 137 patients (mean [SD] age, 41.1 [16.8] years; 77 [58.8%] female) who had a good social functional prognosis. A lower PSP score, taking a mood stabilizer, a high total Positive and Negative Symptom Scale (PANSS) and PANSS general subscale score, unemployment, a hepatic injury with medication, comorbid cardiovascular disease and being male predicted poor PSP outcomes. The generalizability of the PSP predictive tool was estimated with the precision–recall curve (accuracy of 79.5%, negative predictive value of 92.6% and positive predictive value of 57.1%) and receiver operating characteristic curve (ROC) (specificity of 81.8% and sensitivity of 78.7%).Conclusion: The machine learning tool established using our current real-world data could assist in predicting PSP outcome by several clinical factors.Keywords: schizophrenia, atypical antipsychotics, social functional improvement, Personal and Social Performance, PSP
format	article
author	Li Y Zhang L Zhang Y Wen H Huang J Shen Y Li H
author_facet	Li Y Zhang L Zhang Y Wen H Huang J Shen Y Li H
author_sort	Li Y
title	A Random Forest Model for Predicting Social Functional Improvement in Chinese Patients with Schizophrenia After 3 Months of Atypical Antipsychotic Monopharmacy: A Cohort Study
title_short	A Random Forest Model for Predicting Social Functional Improvement in Chinese Patients with Schizophrenia After 3 Months of Atypical Antipsychotic Monopharmacy: A Cohort Study
title_full	A Random Forest Model for Predicting Social Functional Improvement in Chinese Patients with Schizophrenia After 3 Months of Atypical Antipsychotic Monopharmacy: A Cohort Study
title_fullStr	A Random Forest Model for Predicting Social Functional Improvement in Chinese Patients with Schizophrenia After 3 Months of Atypical Antipsychotic Monopharmacy: A Cohort Study
title_full_unstemmed	A Random Forest Model for Predicting Social Functional Improvement in Chinese Patients with Schizophrenia After 3 Months of Atypical Antipsychotic Monopharmacy: A Cohort Study
title_sort	random forest model for predicting social functional improvement in chinese patients with schizophrenia after 3 months of atypical antipsychotic monopharmacy: a cohort study
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/f9c04ab2d77f4a1c8421cf784bb07a18
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A Random Forest Model for Predicting Social Functional Improvement in Chinese Patients with Schizophrenia After 3 Months of Atypical Antipsychotic Monopharmacy: A Cohort Study

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