Liao S, Tang JW, Li Y. Gradient boosting machine model predicts psychiatric complications after deep brain stimulation in Parkinson’s disease. World J Psychiatry 2026; 16(2): 113124 [DOI: 10.5498/wjp.v16.i2.113124]
Corresponding Author of This Article
Yong Li, MD, Chief Physician, Department of Anesthesiology, The Second People’s Hospital of Hunan Province (Brain Hospital of Hunan Province), Section 3, No. 427 Furong Middle Road, Changsha 410000, Hunan Province, China. ly13975136864@163.com
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Psychiatry
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Retrospective Study
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Feb 19, 2026 (publication date) through Feb 2, 2026
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Publication Name
World Journal of Psychiatry
ISSN
2220-3206
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Liao S, Tang JW, Li Y. Gradient boosting machine model predicts psychiatric complications after deep brain stimulation in Parkinson’s disease. World J Psychiatry 2026; 16(2): 113124 [DOI: 10.5498/wjp.v16.i2.113124]
World J Psychiatry. Feb 19, 2026; 16(2): 113124 Published online Feb 19, 2026. doi: 10.5498/wjp.v16.i2.113124
Gradient boosting machine model predicts psychiatric complications after deep brain stimulation in Parkinson’s disease
Sha Liao, Ji-Wei Tang, Yong Li
Sha Liao, Ji-Wei Tang, Yong Li, Department of Anesthesiology, The Second People’s Hospital of Hunan Province (Brain Hospital of Hunan Province), Changsha 410000, Hunan Province, China
Author contributions: Li Y designed the study; Liao S and Tang JW performed the research and collected the data; Liao S and Li Y analyzed the data and wrote the manuscript; all authors have read and approve the final manuscript.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of the Second People’s Hospital of Hunan Province (Brain Hospital of Hunan Province).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: No additional data are available.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yong Li, MD, Chief Physician, Department of Anesthesiology, The Second People’s Hospital of Hunan Province (Brain Hospital of Hunan Province), Section 3, No. 427 Furong Middle Road, Changsha 410000, Hunan Province, China. ly13975136864@163.com
Received: September 3, 2025 Revised: October 8, 2025 Accepted: November 5, 2025 Published online: February 19, 2026 Processing time: 148 Days and 22.2 Hours
Core Tip
Core Tip: This study developed a gradient boosting machine (GBM) model to predict psychiatric complications (depression, anxiety, cognitive impairment, and delirium) in patients with Parkinson’s disease after deep brain stimulation surgery. By analyzing data from 234 patients, the model identified five critical risk factors: Age, surgery duration, fasting time, Family Relationship Health Assessment Scale score, and motor symptom severity (Unified Parkinson’s Disease Rating Scale Part III score). These factors collectively explained 37.6% complication incidence. The GBM model achieved high predictive accuracy (80.0%), sensitivity (95.7%), and area under the curve (0.896) in external validation (65 patients). Decision curve analysis confirmed the optimal clinical utility for risk thresholds between 0.09-0.70, enabling preoperative risk stratification and personalized interventions to mitigate postoperative neuropsychiatric risks.
