Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Overview and Participants
2.2. Variable Profiles for the Substantial Prediction Model
2.3. Data Processing and Machine Learning
3. Results
3.1. General Characteristics of the Study Participants
3.2. Substantial Prediction Model Performance and Variable Importance: Random Forest Model
3.3. Substantial Prediction Model Performance and Variable Importance: LASSO Model
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Total (n = 3744) | Augmentation of Clozapine with ECT | Statistical Coefficient | p-Value | ||
---|---|---|---|---|---|
Yes (n = 47) | No (n = 3697) | ||||
Country/SAR | χ2 = 19.616 | <0.0001 | |||
Bangladesh, n (%) | 99 (2.6) | 0 (0.0) | 99 (2.7) | ||
China, n (%) | 160 (4.3) | 20 (42.6) | 140 (87.5) | ||
Hong Kong, n (%) | 31 (0.8) | 0 (0.0) | 31 (0.8) | ||
India, n (%) | 479 (12.8) | 2 (4.3) | 477 (12.9) | ||
Indonesia, n (%) | 581 (15.5) | 10 (21.3) | 571 (15.4) | ||
Japan, n (%) | 229 (6.1) | 2 (4.3) | 227 (6.1) | ||
Korea, n (%) | 131 (3.5) | 0 (0.0) | 131 (3.5) | ||
Malaysia, n (%) | 305 (8.1) | 4 (8.5) | 301 (8.1) | ||
Myanmar, n (%) | 164 (4.4) | 0 (0.0) | 164 (4.4) | ||
Pakistan, n (%) | 298 (8.0) | 0 (0.0) | 298 (8.0) | ||
Singapore, n (%) | 171 (4.6) | 2 (4.3) | 169 (4.6) | ||
Sri Lanka, n (%) | 97 (2.6) | 1 (2.1) | 96 (2.6) | ||
Thailand, n (%) | 322 (8.6) | 2 (4.3) | 320 (8.7) | ||
Taiwan, n (%) | 403 (10.8) | 2 (4.3) | 401 (10.8) | ||
Vietnam, n (%) | 274 (7.3) | 2 (4.3) | 272 (7.4) | ||
Age, mean (SD) years | 39.5 (13.2) | 39.3 (13.6) | 39.5 (13.1) | t = −0.109 | 0.913 |
Sex | χ2 = 5.142 | 0.023 | |||
Male, n (%) | 2199 (58.7) | 20 (42.6) | 2179 (58.9) | ||
Female, n (%) | 1545 (41.3) | 27 (57.4) | 1518 (41.1) | ||
BMI, mean (SD) kg/m2 | 23.9 (4.7) | 23.2 (3.4) | 23.9 (4.7) | t = −1.376 | 0.319 |
Hospitalization | χ2 = 39.942 | <0.0001 | |||
Outpatient, n (%) | 1793 (47.9) | 1 (2.1) | 1792 (48.5) | ||
Inpatient, n (%) | 1951 (52.1) | 46 (97.9) | 1905 (51.5) | ||
Duration of illness | χ2 = 19.253 | 0.004 | |||
<3 months, n (%) | 161 (4.3) | 6 (12.8) | 155 (4.2) | ||
3–6 months, n (%) | 125 (3.3) | 0 (0.0) | 125 (3.3) | ||
6–12 months, n (%) | 199 (5.3) | 1 (2.1) | 198 (5.4) | ||
1–5 years, n (%) | 794 (21.2) | 5 (0.6) | 789 (21.3) | ||
5–10 years, n (%) | 729 (19.5) | 8 (17.0) | 721 (19.5) | ||
10–20 years, n (%) | 971 (25.9) | 10 (21.3) | 961 (26.0) | ||
>20 years, n (%) | 765 (20.4) | 17 (36.2) | 748 (20.2) | ||
Clinical course for the past 1 year | |||||
Remission, n (%) | 1262 (33.7) | 18 (38.3) | 1244 (33.6) | χ2 = 0.449 | 0.503 |
Persistent symptoms, n (%) | 1917 (51.2) | 20 (42.6) | 1897 (51.3) | χ2 = 1.423 | 0.233 |
Current symptoms | |||||
Delusion, n (%) | 1599 (42.7) | 19 (40.4) | 1580 (42.7) | χ2 = 0.101 | 0.750 |
Hallucination, n (%) | 1752 (46.8) | 19 (40.4) | 1733 (46.9) | χ2 = 0.776 | 0.378 |
Disorganized speech, n (%) | 1110 (29.6) | 12 (25.5) | 1098 (29.7) | χ2 = 0.387 | 0.534 |
Grossly disorganized or catatonic behavior, n (%) | 666 (17.8) | 7 (14.9) | 659 (17.8) | χ2 = 0.273 | 0.601 |
Negative symptom, n (%) | 1313 (35.1) | 26 (55.3) | 1287 (34.8) | χ2 = 8.571 | 0.003 |
Social or occupational dysfunction, n (%) | 1693 (45.2) | 28 (59.6) | 1665 (45.0) | χ2 = 3.960 | 0.047 |
Verbal aggression, n (%) | 942 (25.2) | 9 (19.1) | 933 (25.2) | χ2 = 0.913 | 0.339 |
Physical aggression, n (%) | 780 (20.8) | 11 (23.4) | 769 (20.8) | χ2 = 0.191 | 0.662 |
Significant affective symptoms, n (%) | 425 (11.4) | 3 (6.4) | 422 (11.4) | χ2 = 1.168 | 0.280 |
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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. J. Pers. Med. 2022, 12, 969. https://doi.org/10.3390/jpm12060969
Oh HS, Lee BJ, Lee YS, Jang O-J, Nakagami Y, Inada T, Kato TA, Kanba S, Chong M-Y, Lin S-K, Si T, Xiang Y-T, Avasthi A, Grover S, Kallivayalil RA, Pariwatcharakul P, Chee KY, Tanra AJ, Rabbani G, Javed A, Kathiarachchi S, Myint WA, Cuong TV, Wang Y, Sim K, Sartorius N, Tan C-H, Shinfuku N, Park YC, 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. 2022; 12(6):969. https://doi.org/10.3390/jpm12060969
Chicago/Turabian StyleOh, Hong Seok, Bong Ju Lee, Yu Sang Lee, Ok-Jin Jang, Yukako Nakagami, Toshiya Inada, Takahiro A. Kato, Shigenobu Kanba, Mian-Yoon Chong, Sih-Ku Lin, Tianmei Si, Yu-Tao Xiang, Ajit Avasthi, Sandeep Grover, Roy Abraham Kallivayalil, Pornjira Pariwatcharakul, Kok Yoon Chee, Andi J. Tanra, Golam Rabbani, Afzal Javed, Samudra Kathiarachchi, Win Aung Myint, Tran Van Cuong, Yuxi Wang, Kang Sim, Norman Sartorius, Chay-Hoon Tan, Naotaka Shinfuku, Yong Chon Park, and Seon-Cheol Park. 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, no. 6: 969. https://doi.org/10.3390/jpm12060969