Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics

• Published in: Schizophrenia research 2025-01, Vol.275, p.146-155
• Main Authors: Kim, Eun Young, Kim, Jayoun, Jeong, Jae Hoon, Jang, Jinhyeok, Kang, Nuree, Seo, Jieun, Park, Young Eun, Park, Jiae, Jeong, Hyunsu, Ahn, Yong Min, Kim, Yong Sik, Lee, Donghwan, Kim, Se Hyun
• Format: Article
• Language: English
• Subjects: Adult; Antipsychotic Agents - therapeutic use; Body Mass Index; Female; Humans; Machine Learning; Male; Middle Aged; Outcome Assessment, Health Care; Prediction score; Schizophrenia; Schizophrenia - drug therapy; Schizophrenia - metabolism; Schizophrenic Psychology; Treatment Outcome; Treatment response
• ISSN: 0920-9964; 1573-2509; 1573-2509
• DOI: 10.1016/j.schres.2024.12.018

Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from 299 patients with schizophrenia from three multicenter, open-label, non-comparative clinical trials. For prediction of treatment response at weeks 4, 8, and 24, psychopathology (both objective and subjective symptoms), sociodemographic and clinical factors, functional outcomes, attitude toward medication, and metabolic characteristics were evaluated. Various ML techniques were applied. The highest area under the curve (AUC) at weeks 4, 8 and 24 was 0.711, 0.664 and 0.678 with extreme gradient boosting, respectively. Notably, our findings indicate that BMI and attitude toward medication play a pivotal role in predicting treatment responses at all-time points. Other salient features for weeks 4 and 8 included psychosocial functioning, negative symptoms, subjective symptoms like psychoticism and hostility, and the level of prolactin. For week 24, positive symptoms, depression, education level and duration of illness were also important. This study introduced a precise clinical model for predicting schizophrenia treatment outcomes using multiple readily accessible predictors. The findings underscore the significance of metabolic parameters and subjective traits. •A precise machine learning model was developed for predicting treatment response in schizophrenia.•Body mass index and attitudes toward medication were significant predictors of treatment response.•Self-reported assessments of psychoticism, hostility, and depression emerged as important predictive factors.•Metabolic parameters and subjective traits are significant predictors of the treatment response in schizophrenia.