A Clinical Risk Stratification Tool for Predicting Treatment Resistance in Major Depressive Disorder

Abstract

Background Early identification of depressed individuals at high risk for treatment-resistance could be helpful in selecting optimal setting and intensity of care. At present, validated tools to facilitate this risk stratification are rarely used in psychiatric practice. Methods Data were drawn from the first two treatment levels of a multicenter antidepressant effectiveness study in major depressive disorder, the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) cohort. This cohort was divided into training, testing, and validation subsets. Only clinical or sociodemographic variables available by, or readily amenable to, self-report were considered. Multivariate models were developed to discriminate individuals reaching remission with a first or second pharmacologic treatment trial from those not reaching remission despite two trials. Results A logistic regression model achieved an area under the receiver operating characteristic curve (AUC) exceeding 0.71 in training, testing and validation cohorts, and maintained good calibration across cohorts. Performance of three alternative models using machine learning approaches–a naïve Bayes classifier and a support vector machine, and a random forest model – was less consistent. Similar performance was observed between more and less severe depression, males and females, and primary versus specialty care sites. A web-based calculator was developed which implements this tool and provides graphical estimates of risk. Conclusion Risk for treatment-resistance among outpatients with major depressive disorder can be estimated using a simple model incorporating baseline sociodemographic and clinical features. Future studies should examine the performance of this model in other clinical populations and its utility in treatment selection or clinical trial design. Registration Sequential Treatment Alternatives to Relieve Depression (STAR*D); NCT00021528; www.star-d.org

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