論文

国際誌
2022年6月14日

Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia.

Journal of personalized medicine
  • Hong Seok Oh
  • 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
  • Seon-Cheol Park
  • 全て表示

12
6
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.3390/jpm12060969

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.

リンク情報
DOI
https://doi.org/10.3390/jpm12060969
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/35743753
ID情報
  • DOI : 10.3390/jpm12060969
  • PubMed ID : 35743753

エクスポート
BibTeX RIS