Delgadillo Jaime, Moreea Omar, Lutz Wolfgang
Leeds Community Healthcare NHS Trust, and Department of Health Sciences, University of York, UK.
Centre for Clinical Practice, National Institute for Health and Care Excellence, UK.
Behav Res Ther. 2016 Apr;79:15-22. doi: 10.1016/j.brat.2016.02.003. Epub 2016 Feb 23.
This study aimed to identify patient characteristics associated with poor outcomes in psychological therapy, and to develop a patient profiling method.
Clinical assessment data for 1347 outpatients was analysed. Final treatment outcome was based on reliable and clinically significant improvement (RCSI) in depression (PHQ-9) and anxiety (GAD-7) measures. Thirteen patient characteristics were explored as potential outcome predictors using logistic regression in a cross-validation design.
Disability, employment status, age, functional impairment, baseline depression and outcome expectancy predicted post-treatment RCSI. Regression coefficients for these factors were used to derive a weighting scheme called Leeds Risk Index (LRI), used to assign risk scores to individual cases. After stratifying cases into three levels of LRI scores, we found significant differences in RCSI and treatment completion rates. Furthermore, LRI scores were significantly correlated with the proportion of treatment sessions classified as 'not on track'.
The LRI tool can identify cases at risk of poor progress to inform personalized treatment recommendations for low and high intensity psychological interventions.
本研究旨在确定与心理治疗效果不佳相关的患者特征，并开发一种患者特征分析方法。
分析了1347名门诊患者的临床评估数据。最终治疗结果基于抑郁症（PHQ-9）和焦虑症（GAD-7）测量中的可靠且具有临床意义的改善（RCSI）。在交叉验证设计中，使用逻辑回归探索了13个患者特征作为潜在的结果预测因素。
残疾、就业状况、年龄、功能损害、基线抑郁和结果期望预测了治疗后的RCSI。这些因素的回归系数用于得出一种称为利兹风险指数（LRI）的加权方案，用于为个体病例分配风险分数。将病例分为三个LRI分数水平后，我们发现RCSI和治疗完成率存在显著差异。此外，LRI分数与被归类为“未步入正轨”的治疗疗程比例显著相关。
LRI工具可以识别进展不佳风险的病例，为低强度和高强度心理干预的个性化治疗建议提供依据。
