拉丁美洲对心理治疗无反应的个体风险:为资源不足的临床环境提供数据知情的精确护理。

IF 4.5 1区 心理学 Q1 PSYCHOLOGY, CLINICAL
Juan Martín Gómez Penedo, Paula Errázuriz, Alice E Coyne, Christoph Flückiger
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引用次数: 0

摘要

目的:机器学习在预测个体患者对精神卫生保健(MHC)的反应方面具有巨大的潜力,从而实现治疗个性化。然而,以前的努力仅限于主要生活在高收入发达国家的人口。本研究旨在通过开发和测试一种可行且易于实施的算法来扩展精确MHC系统的范围,该算法用于识别智利(拉丁美洲的一个发展中国家)常规心理治疗无反应风险的患者。方法:数据来源于一项基于社区的随机试验,该试验测试了进展反馈对自然传递的心理治疗结果的影响。患者为547名连续入住智利圣地亚哥一家门诊诊所的成年人。使用结果问卷-30的可靠改善标准来定义治疗反应。基于10个社会人口学预测因子和7个临床预测因子,我们在随机选择的训练集(70%;N = 384)。在保留样本(30%;N = 163)。结果:42%的病例得到了可靠的改善。随机森林算法在保留样本中表现中等(曲线下面积= 0.74,Brier评分= 0.21),正确识别73%无反应的患者。结论:本研究开发了一种预测算法,使用常规评估和易于收集的社会人口学和临床信息,在智利识别对自然心理治疗无反应风险的患者方面显示出中等准确性。使用这些工具可能是减少在社会经济条件不利的环境中接受MHC的个体所面临的多层次结果差异的一步。(PsycInfo Database Record (c) 2024 APA,版权所有)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Individual risk of not responding to psychotherapy in Latin America: Bringing data-informed precision care to underresourced clinical settings.

Objective: Machine learning has a great potential for prospectively forecasting individual patient response to mental health care (MHC), thereby enabling treatment personalization. However, previous efforts have been limited to populations living in predominantly higher income, developed countries. This study aimed to extend the reach of precision MHC systems by developing and testing a feasible and readily implementable algorithm for identifying patients at risk of nonresponse to routinely delivered psychotherapy in Chile, a developing country in Latin America.

Method: Data were derived from a community-based, randomized trial that tested the effects of progress feedback on naturalistically delivered psychotherapy outcome. Patients were 547 adults who were consecutively admitted to an outpatient clinic in Santiago, Chile. Treatment response was defined using norms for reliable improvement on the Outcome Questionnaire-30. Based on 10 sociodemographic and seven clinical predictors, we trained elastic net and random forest algorithms on a randomly selected training set (70%; n = 384). The best performing algorithm was tested on a hold-out sample (30%; n = 163).

Results: Reliable improvement was achieved in 42% of the cases. A random forest algorithm demonstrated moderate performance in the hold-out sample (area under the curve = .74, Brier score = .21), correctly identifying 73% of the patients who did not respond.

Conclusion: This study developed a predictive algorithm that demonstrated moderate accuracy in identifying patients at risk of nonresponse to naturalistic psychotherapy in Chile, using routinely assessed and easy-to-collect sociodemographic and clinical information. Using such tools may represent one step toward reducing the multilayered outcome disparities faced by individuals receiving MHC in socioeconomically disadvantaged contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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来源期刊
CiteScore
9.00
自引率
3.40%
发文量
94
期刊介绍: The Journal of Consulting and Clinical Psychology® (JCCP) publishes original contributions on the following topics: the development, validity, and use of techniques of diagnosis and treatment of disordered behaviorstudies of a variety of populations that have clinical interest, including but not limited to medical patients, ethnic minorities, persons with serious mental illness, and community samplesstudies that have a cross-cultural or demographic focus and are of interest for treating behavior disordersstudies of personality and of its assessment and development where these have a clear bearing on problems of clinical dysfunction and treatmentstudies of gender, ethnicity, or sexual orientation that have a clear bearing on diagnosis, assessment, and treatmentstudies of psychosocial aspects of health behaviors. Studies that focus on populations that fall anywhere within the lifespan are considered. JCCP welcomes submissions on treatment and prevention in all areas of clinical and clinical–health psychology and especially on topics that appeal to a broad clinical–scientist and practitioner audience. JCCP encourages the submission of theory–based interventions, studies that investigate mechanisms of change, and studies of the effectiveness of treatments in real-world settings. JCCP recommends that authors of clinical trials pre-register their studies with an appropriate clinical trial registry (e.g., ClinicalTrials.gov, ClinicalTrialsRegister.eu) though both registered and unregistered trials will continue to be considered at this time.
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