Objective: Major depressive disorder (MDD) is linked to a 61% increased risk of emergency department (ED) visits and frequent ED usage. Collaborative care management (CoCM) models target MDD treatment in primary care, but how best to prioritize patients for CoCM to prevent frequent ED utilization remains unclear. This study aimed to develop and validate a risk identification model to proactively detect patients with MDD in CoCM at high risk of frequent (≥ 3) ED visits.
Study design: This retrospective cohort study utilized electronic health records from Mayo Clinic's primary care system to develop and validate a machine learning-based risk identification model. The model predicts the likelihood of frequent ED visits among patients with MDD within a 12-month period.
Methods: Data were collected from Mayo Clinic's primary care system between May 1, 2006, and December 19, 2018. Risk identification models were developed and validated using machine learning classifiers to estimate frequent ED visit risks over 12 months. The Shapley Additive Explanations model identified variables driving frequent ED visits.
Results: The patient population had a mean (SD) age of 39.78 (16.66) years, with 30.3% being male and 6.1% experiencing frequent ED visits. The best-performing algorithm (elastic-net logistic regression) achieved an area under the curve of 0.79 (95% CI, 0.74-0.84), a sensitivity of 0.71 (95% CI, 0.57-0.82), and a specificity of 0.76 (95% CI, 0.64-0.85) in the development data set. In the validation data set, the best-performing algorithm (random forest) achieved an area under the curve of 0.79, a sensitivity of 0.83, and a specificity of 0.61. Significant variables included male gender, prior frequent ED visits, high Patient Health Questionnaire-9 score, low education level, unemployment, and use of multiple medications.
Conclusions: The risk identification model has potential for clinical application in triaging primary care patients with MDD in CoCM, aiming to reduce future ED utilization.