Myers Catherine E, Dave Chintan V, Chesin Megan S, Marx Brian P, St Hill Lauren M, Reddy Vibha, Miller Rachael B, King Arlene, Interian Alejandro
Research and Development Service, VA New Jersey Health Care System, East Orange, NJ, USA; Department of Pharmacology, Physiology & Neuroscience, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, USA.
Center for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, USA.
Behav Res Ther. 2024 Dec;183:104637. doi: 10.1016/j.brat.2024.104637. Epub 2024 Sep 18.
Develop and evaluate a treatment matching algorithm to predict differential treatment response to Mindfulness-Based Cognitive Therapy for suicide prevention (MBCT-S) versus enhanced treatment-as-usual (eTAU).
Analyses used data from Veterans at high-risk for suicide assigned to either MBCT-S (n = 71) or eTAU (n = 69) in a randomized clinical trial. Potential predictors (n = 55) included available demographic, clinical, and neurocognitive variables. Random forest models were used to predict risk of suicidal event (suicidal behaviors, or ideation resulting in hospitalization or emergency department visit) within 12 months following randomization, characterize the prediction, and develop a Personalized Advantage Index (PAI).
A slightly better prediction model emerged for MBCT-S (AUC = 0.70) than eTAU (AUC = 0.63). Important outcome predictors for participants in the MBCT-S arm included PTSD diagnosis, decisional efficiency on a neurocognitive task (Go/No-Go), prior-year mental health residential treatment, and non-suicidal self-injury. Significant predictors for participants in the eTAU arm included past-year acute psychiatric hospitalizations, past-year outpatient psychotherapy visits, past-year suicidal ideation severity, and attentional control (indexed by Stroop task). A moderation analysis showed that fewer suicidal events occurred among those randomized to their PAI-indicated optimal treatment.
PAI-guided treatment assignment may enhance suicide prevention outcomes. However, prior to real-world application, additional research is required to improve model accuracy and evaluate model generalization.
开发并评估一种治疗匹配算法，以预测基于正念的认知疗法预防自杀（MBCT-S）与强化常规治疗（eTAU）的不同治疗反应。
分析使用了一项随机临床试验中被分配到MBCT-S组（n = 71）或eTAU组（n = 69）的自杀高危退伍军人的数据。潜在预测因素（n = 55）包括可用的人口统计学、临床和神经认知变量。随机森林模型用于预测随机分组后12个月内自杀事件（自杀行为，或导致住院或急诊就诊的自杀意念）的风险，描述预测情况，并制定个性化优势指数（PAI）。
MBCT-S组出现了一个略优于eTAU组的预测模型（AUC = 0.70）（AUC = 0.63）。MBCT-S组参与者的重要结局预测因素包括创伤后应激障碍诊断、神经认知任务（Go/No-Go）的决策效率、上一年的心理健康住院治疗以及非自杀性自伤。eTAU组参与者的显著预测因素包括过去一年的急性精神病住院、过去一年的门诊心理治疗就诊、过去一年的自杀意念严重程度以及注意力控制（由Stroop任务衡量）。一项调节分析表明，随机分配到PAI指示的最佳治疗的参与者中自杀事件较少。
PAI指导的治疗分配可能会提高自杀预防效果。然而，在实际应用之前，需要进行更多研究以提高模型准确性并评估模型的普遍性。
