How well do clinical and demographic characteristics predict Patient Health Questionnaire‐9 scores among patients with treatment‐resistant major depressive disorder in a real‐world setting?

OBJECTIVES: To create and validate a model to predict depression symptom severity among patients with treatment‐resistant depression (TRD) using commonly recorded variables within medical claims databases. METHODS: Adults with TRD (here defined as > 2 antidepressant treatments in an episode, suggestive of nonresponse) and ≥ 1 Patient Health Questionnaire (PHQ)‐9 record on or after the index TRD date were identified (2013–2018) in Decision Resource Group's Real World Data Repository, which links an electronic health record database to a medical claims database. A total of 116 clinical/demographic variables were utilized as predictors of the study outcome of depression symptom severity, which was measured by PHQ‐9 total score category (score: 0–9 = none to mild, 10–14 = moderate, 15–27 = moderately severe to severe). A random forest approach was applied to develop and validate the predictive model. RESULTS: Among 5,356 PHQ‐9 scores in the study population, the mean (standard deviation) PHQ‐9 score was 10.1 (7.2). The model yielded an accuracy of 62.7%. For each predicted depression symptom severity category, the mean observed scores (8.0, 12.2, and 16.2) fell within the appropriate range. CONCLUSIONS: While there is room for improvement in its accuracy, the use of a machine learning tool that predicts depression symptom severity of patients with TRD can potentially have wide population‐level applications. Healthcare systems and payers can build upon this groundwork and use the variables identified and the predictive modeling approach to create an algorithm specific to their population.