Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia
Issued Date
2022-06-01
Resource Type
eISSN
20754426
Scopus ID
2-s2.0-85132553186
Journal Title
Journal of Personalized Medicine
Volume
12
Issue
6
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Personalized Medicine Vol.12 No.6 (2022)
Suggested Citation
Oh H.S., Lee B.J., Lee Y.S., Jang O.J., Nakagami Y., Inada T., Kato T.A., Kanba S., Chong M.Y., Lin S.K., Si T., Xiang Y.T., Avasthi A., Grover S., Kallivayalil R.A., Pariwatcharakul P., Chee K.Y., Tanra A.J., Rabbani G., Javed A., Kathiarachchi S., Myint W.A., Cuong T.V., Wang Y., Sim K., Sartorius N., Tan C.H., Shinfuku N., Park Y.C., Park S.C. Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia. Journal of Personalized Medicine Vol.12 No.6 (2022). doi:10.3390/jpm12060969 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/85809
Title
Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia
Author(s)
Oh H.S.
Lee B.J.
Lee Y.S.
Jang O.J.
Nakagami Y.
Inada T.
Kato T.A.
Kanba S.
Chong M.Y.
Lin S.K.
Si T.
Xiang Y.T.
Avasthi A.
Grover S.
Kallivayalil R.A.
Pariwatcharakul P.
Chee K.Y.
Tanra A.J.
Rabbani G.
Javed A.
Kathiarachchi S.
Myint W.A.
Cuong T.V.
Wang Y.
Sim K.
Sartorius N.
Tan C.H.
Shinfuku N.
Park Y.C.
Park S.C.
Lee B.J.
Lee Y.S.
Jang O.J.
Nakagami Y.
Inada T.
Kato T.A.
Kanba S.
Chong M.Y.
Lin S.K.
Si T.
Xiang Y.T.
Avasthi A.
Grover S.
Kallivayalil R.A.
Pariwatcharakul P.
Chee K.Y.
Tanra A.J.
Rabbani G.
Javed A.
Kathiarachchi S.
Myint W.A.
Cuong T.V.
Wang Y.
Sim K.
Sartorius N.
Tan C.H.
Shinfuku N.
Park Y.C.
Park S.C.
Author's Affiliation
Pushpagiri Institute of Medical Sciences and Research Centre
Chang Gung University School of Medicine
Faculty of Health Sciences
Siriraj Hospital
Graduate School of Medical Sciences
Nagoya University Graduate School of Medicine
Graduate School of Medicine
University of Sri Jayewardenepura
Hanyang University Guri Hospital
Chang Gung Memorial Hospital
National University Hospital
Hanyang University College of Medicine
Kuala Lumpur Hospital
Seinan Gakuin University
Inje University
Peking University
Singapore Institute of Mental Health
Konyang University, College of Medicine
Postgraduate Institute of Medical Education & Research, Chandigarh
Yong-In Mental Hospital
University of Medicine 1
Association for the Improvement of Mental Health Programmes
National Psychiatry Hospital
Bugok National Hospital
Wahidin Sudirohusodo Hospital
National Institute of Mental Health
Pakistan Medical Research Centre
Chang Gung University School of Medicine
Faculty of Health Sciences
Siriraj Hospital
Graduate School of Medical Sciences
Nagoya University Graduate School of Medicine
Graduate School of Medicine
University of Sri Jayewardenepura
Hanyang University Guri Hospital
Chang Gung Memorial Hospital
National University Hospital
Hanyang University College of Medicine
Kuala Lumpur Hospital
Seinan Gakuin University
Inje University
Peking University
Singapore Institute of Mental Health
Konyang University, College of Medicine
Postgraduate Institute of Medical Education & Research, Chandigarh
Yong-In Mental Hospital
University of Medicine 1
Association for the Improvement of Mental Health Programmes
National Psychiatry Hospital
Bugok National Hospital
Wahidin Sudirohusodo Hospital
National Institute of Mental Health
Pakistan Medical Research Centre
Other Contributor(s)
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.