在物质使用障碍中区分创伤后应激并发症的行为和神经认知因素
David C Houghton1,2, Heidi M Spratt3,4, Lori Keyser-Marcus5
1Center for Addiction Sciences and Therapeutics, University of Texas Medical Branch, Galveston, TX, USA. dahought@utmb.edu.
Translational psychiatry
|September 14, 2023
概括
机器学习模型确定了预测创伤后应激障碍 (PTSD) 的关键心理因素. 这些发现强调了负面影响和整体感知意识在理解PTSD-SUD并发症方面的重要性.
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.
- 计算生物学 计算生物学
背景情况:
- 同时出现的物质使用障碍 (SUD) 和创伤后应激障碍 (PTSD) 在治疗和病因学方面存在独特的挑战.
- 了解SUD-PTSD并发症的特定风险因素和潜在机制对于开发有针对性的干预措施至关重要.
研究的目的:
- 通过机器学习在SUD患者中识别PTSD症状和并发症状态的预测因素.
- 探索SUD和PTSD同时发生的个体的独特病因概况.
主要方法:
- 监督机器学习算法 (分类和回归) 应用于160名SUD参与者的数据,使用NIDA表型评估电池.
- 102名参与者的子集还完成了神经认知任务,允许进一步分析并发症预测因子.
主要成果:
- 一个分类模型准确地预测了基于焦虑,抑郁症状,坚持性思维和整体感知意识的PTSD症状 (准确率为75%).
- 在一个子集分析中,使用抑郁症状和与恐惧相关的注意偏差来预测并发病状况的准确率为86.67%.
- 回归模型表明,应急耐受性和整体感知意识是根据PTSD并发症性对样本进行分类的重要因素.
结论:
- 严重的负面影响和整体感知意识的缺陷是SUD患者PTSD的重要指标.
- 容忍和调节厌恶的内部经历的偏差可能是SUD人口中创伤历史的个体的特征.
- 需要进一步的研究来探索额外的并发症诊断,各种SUD和PTSD亚型,以完善对PTSD-SUD并发症的理解.
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