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Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia
Oh, Hong Seok1; Lee, Bong Ju2; Lee, Yu Sang3; Jang, Ok Jin4; Nakagami, Yukako5; Inada, Toshiya6; Kato, Takahiro A.7; Kanba, Shigenobu7; Chong, Mian Yoon8; Lin, Sih Ku9; Si, Tianmei10; Xiang, Yu Tao11; Avasthi, Ajit12; Grover, Sandeep12; Kallivayalil, Roy Abraham13; Pariwatcharakul, Pornjira14; Chee, Kok Yoon15; Tanra, Andi J.16; Rabbani, Golam17; Javed, Afzal18; Kathiarachchi, Samudra19; Myint, Win Aung20; Cuong, Tran Van21; Wang, Yuxi22; Sim, Kang22,23; Sartorius, Norman24; Tan, Chay Hoon25; Shinfuku, Naotaka26; Park, Yong Chon27; Park, Seon Cheol27,28
2022-06-01
Source PublicationJournal of Personalized Medicine
ISSN2075-4426
Volume12Issue:6Pages:969
Abstract

The augmentation of clozapine with electroconvulsive therapy (ECT) has been an optimal treatment option for patients with treatment-or clozapine-resistant schizophrenia. Using data from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics survey, which was the largest international psychiatry research collaboration in Asia, our study aimed to develop a machine learning algorithm-based substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in terms of precision medicine. A random forest model and least absolute shrinkage and selection operator (LASSO) model were used to develop a substantial prediction model for the augmented use of clozapine with ECT. Among the 3744 Asian patients with schizophrenia, those treated with a combination of clozapine and ECT were characterized by significantly greater proportions of females and inpatients, a longer duration of illness, and a greater prevalence of negative symptoms and social or occupational dysfunction than those not treated. In the random forest model, the area under the curve (AUC), which was the most preferred indicator of the prediction model, was 0.774. The overall accuracy was 0.817 (95% confidence interval, 0.793–0.839). Inpatient status was the most important variable in the substantial prediction model, followed by BMI, age, social or occupational dysfunction, persistent symptoms, illness duration > 20 years, and others. Furthermore, the AUC and overall accuracy of the LASSO model were 0.831 and 0.644 (95% CI, 0.615–0.672), respectively. Despite the subtle differences in both AUC and overall accuracy of the random forest model and LASSO model, the important variables were commonly shared by the two models. Using the machine learning algorithm, our findings allow the development of a substantial prediction model for the augmented use of clozapine with ECT in Asian patients with schizophrenia. This substantial prediction model can support further studies to develop a substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in a strict epidemiological context.

KeywordAugmentation Clozapine ElEctroconvulsive Therapy (Ect) Machine Learning Precision Medicine Schizophrenia
DOI10.3390/jpm12060969
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaHealth Care Sciences & Services ; General & Internal Medicine
WOS SubjectHealth Care Sciences & Services ; Medicine, General & Internal
WOS IDWOS:000816187600001
Scopus ID2-s2.0-85132553186
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionFaculty of Health Sciences
Institute of Translational Medicine
DEPARTMENT OF PUBLIC HEALTH AND MEDICINAL ADMINISTRATION
Corresponding AuthorPark, Seon Cheol
Affiliation1.Department of Psychiatry, Konyang University Hospital, Daejeon, 35356, South Korea
2.Department of Psychiatry, Inje University Haeundae Paik Hospital, Busan, 48108, South Korea
3.Department of Psychiatry, Yong-In Mental Hospital, Yongin, 17089, South Korea
4.Department of Psychiatry, Bugok National Hospital, Changyeong, 50365, South Korea
5.Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
6.Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
7.Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan
8.Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung & Chang Gung University School of Medicine, Taoyuan, 83301, Taiwan
9.Department of Psychiatry, Linkou Chang Gung Memorial Hospital, Taoyuan, 33305, Taiwan
10.Peking Institute of Mental Health (PIMH), Peking University, Beijing, 100083, China
11.Unit of Psychiatry, Department of Public Health and Medicinal Administration & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao
12.Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India
13.Department of Psychiatry, Pushpagiri Institute of Medical Sciences, Tiruvalla, 689101, India
14.Department of Psychiatry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10400, Thailand
15.Tunku Abdul Rahman Institute of Neuroscience, Kuala Lumpur Hospital, Kuala Lumpur, 502586, Malaysia
16.Wahidin Sudirohusodo University, Makassar, Sulawesi Selatan, 90245, Indonesia
17.National Institute of Mental Health, Dhaka, 1207, Bangladesh
18.Pakistan Psychiatric Research Centre, Fountain House, Lahore, 39020, Pakistan
19.Department of Psychiatry, University of Sri Jayewardenepura, Nugegoda, 10250, Sri Lanka
20.Department of Mental Health, University of Medicine (1), Yangon, 15032, Myanmar
21.National Psychiatry Hospital, Hanoi, 10000, Viet Nam
22.West Region, Institute of Mental Health, Singapore, 119228, Singapore
23.Research Division, Institute of Mental Health, Singapore, 119228, Singapore
24.Association of the Improvement of Mental Health Programs (AMH), Geneva, 1209, Switzerland
25.Department of Pharmacology, National University Hospital, Singapore, 119228, Singapore
26.Department of Social Welfare, School of Human Sciences, Seinan Gakuin University, Fukuoka, 814-8511, Japan
27.Department of Psychiatry, Hanyang University College of Medicine, Seoul, 04763, South Korea
28.Department of Psychiatry, Hanyang University Guri Hospital, Guri, 11923, South Korea
Recommended Citation
GB/T 7714
Oh, Hong Seok,Lee, Bong Ju,Lee, Yu Sang,et al. Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia[J]. Journal of Personalized Medicine, 2022, 12(6), 969.
APA Oh, Hong Seok., Lee, Bong Ju., Lee, Yu Sang., Jang, Ok Jin., Nakagami, Yukako., Inada, Toshiya., Kato, Takahiro A.., Kanba, Shigenobu., Chong, Mian Yoon., Lin, Sih Ku., Si, Tianmei., Xiang, Yu Tao., Avasthi, Ajit., Grover, Sandeep., Kallivayalil, Roy Abraham., Pariwatcharakul, Pornjira., Chee, Kok Yoon., Tanra, Andi J.., Rabbani, Golam., ...& Park, Seon Cheol (2022). Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia. Journal of Personalized Medicine, 12(6), 969.
MLA Oh, Hong Seok,et al."Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia".Journal of Personalized Medicine 12.6(2022):969.
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