痴迷强迫症长期发展的认知和临床预测因素:在前性队列研究中的机器学习方法
C Segalàs1, E Cernadas2, M Puialto3
1OCD Clinical and Research Unit, Psychiatry Department, Hospital Universitari de Bellvitge, Barcelona, Spain; Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; CIBERSAM (Centro de Investigación en Red de Salud Mental), Instituto de Salud Carlos III, Madrid, Spain; Department of Clinical Sciences, Bellvitge Campus, University of Barcelona, 32 Barcelona, Spain.
Journal of affective disorders
|January 21, 2024
概括
许多患有强迫症 (OCD) 的患者经历了慢性症状,尽管接受了治疗. 机器学习可以使用临床和认知数据预测长期的强迫症结果,帮助优化治疗.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 机器学习 机器学习
背景情况:
- 强迫症 (OCD) 是一种慢性和残疾的疾病.
- 对强迫症患者的长期结局数据有限.
- 了解长期轨迹对于有效管理至关重要.
研究的目的:
- 研究接受多种治疗的患者强迫症的长期病程.
- 使用机器学习识别临床结果的预测因素.
- 评估药物治疗,心理治疗和精神手术的效果.
主要方法:
- 包括来自转诊单位的强迫症门诊患者.
- 收集基线人口统计,神经心理和临床数据.
- 在5-17年内收集的随访数据,包括初始药理治疗反应.
主要成果:
- 67.7%的患者在长期随访 (平均10.6年) 后没有对适当治疗产生反应.
- 一个机器学习模型预测了长期的耶鲁-布朗强迫性强迫性量表 (Y-BOCS) 成绩,相关性为0.63.
- 预测结合了基线临床,神经心理特征和初始治疗反应.
结论:
- 很大一部分强迫症患者表现出持续的症状,尽管进行了全面的治疗.
- 机器学习模型在预测长期强迫症预后方面表现有前途.
- 可以利用临床和认知数据来个性化和优化强迫症治疗策略.
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