331 – Cluster Detection in Big Data
Predicting Clinically Significant Response to Primary Care Treatment for Depression from Electronic Health Records of Veterans
M. Myron Chang
VA Center for Clinical Management Research
To reduce delays in referral to specialty mental health care, we evaluated clinical prediction models estimating the likelihood of response to primary care treatment of depression in the VA healthcare system. We included patients with a primary care depression diagnosis between October 1, 2015 and December 31, 2017, an initial Patient Health Questionnaire (PHQ-9) score ≥ 10 within 30 days, a follow-up PHQ-9 score within 2-8 months, and no specialty mental health care within three months prior to depression diagnosis. We evaluated eight ordinary least squares regression models, each with a different procedure for selecting predictors of percentage change in PHQ-9 score from baseline to follow-up. Predictors included patient characteristics from electronic health records and neighborhood characteristics from US census data. We repeated each modeling procedure 1,000 times, using different training and validation sets of patients. We used R2, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) to evaluate model performance. The final cohort included 3,461 patients. The two best performing models included multiple iterations of backwards stepwise variable selection with R2 of 0.063, RMSE of 41.56, MAE of 33.44; and R2 of 0.064, RMSE of 41.55, MAE of 33.46. Model performance did not suggest its use as a guide in clinical decision-making. Future research should explore whether obtaining additional risk factor data from patients (e.g., duration of symptoms) or modeling PHQ-9 scores over a narrower time interval improves performance of clinical risk prediction tools for depression.