| Sumario: | 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.
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