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....
| Publicado en: | Journal of Consulting & Clinical Psychology Vol. 92; no. 12; pp. 836 - 843 |
|---|---|
| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
Dec2024
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=181806006&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 181806006 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0022006X JCC jtl: Journal of Consulting & Clinical Psychology issn: 0022006X maglogo: N pubinfo: dt: Dec2024 vid: 92 iid: 12 pid: 34 pub: American Psychological Association artinfo: ui: 181806006 10.1037/ccp0000931 ppf: 836 ppct: 7 formats: tig: atl: Individual Risk of Not Responding to Psychotherapy in Latin America: Bringing Data-Informed Precision Care to Underresourced Clinical Settings. aug: au: Gómez Penedo, Juan Martín Errázuriz, Paula Coyne, Alice E. Flückiger, Christoph affil: Department of Psychology, Universität Kassel Department of Psychology, University of Buenos Aires School of Psychology, Pontificia Universidad Católica de Chile Millennium Institute for Research on Depression and Personality Department of Psychology, College of Arts and Sciences, American University su: Mental health services Psychotherapy Random forest algorithms Machine learning Clinical medicine Developing countries sug: subj: Mental health services Psychotherapy Psychiatric and Substance Abuse Hospitals Residential Mental Health and Substance Abuse Facilities Offices of Mental Health Practitioners (except Physicians) Random forest algorithms Machine learning Clinical medicine Developing countries keyword: developing countries machine learning algorithm prospective prediction psychotherapy outcome treatment personalization developing countries machine learning algorithm prospective prediction psychotherapy outcome treatment personalization ab: 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. What is the public health significance of this article?: This study provides a freely available algorithm that uses routinely assessed and easy-to-collect sociodemographic and clinical information and demonstrates moderate accuracy in predicting psychotherapy outcome in the context of a developing country in Latin America. This tool represents one step toward reducing the multilayered outcome disparities that are often faced by individuals receiving mental health care in economically disadvantaged contexts. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|