Juan Martín Gómez Penedo, Paula Errázuriz, Alice E Coyne, Christoph Flückiger
{"title":"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。","authors":"Juan Martín Gómez Penedo, Paula Errázuriz, Alice E Coyne, Christoph Flückiger","doi":"10.1037/ccp0000931","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Method: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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).</p>","PeriodicalId":15447,"journal":{"name":"Journal of consulting and clinical psychology","volume":"92 12","pages":"836-842"},"PeriodicalIF":4.5000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Individual risk of not responding to psychotherapy in Latin America: Bringing data-informed precision care to underresourced clinical settings.\",\"authors\":\"Juan Martín Gómez Penedo, Paula Errázuriz, Alice E Coyne, Christoph Flückiger\",\"doi\":\"10.1037/ccp0000931\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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.</p><p><strong>Method: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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).</p>\",\"PeriodicalId\":15447,\"journal\":{\"name\":\"Journal of consulting and clinical psychology\",\"volume\":\"92 12\",\"pages\":\"836-842\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of consulting and clinical psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1037/ccp0000931\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, CLINICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of consulting and clinical psychology","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/ccp0000931","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, CLINICAL","Score":null,"Total":0}
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).
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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