Development of a Clinical Forecasting Model to Predict Comorbid Depression Among Diabetes Patients and an Application in Depression Screening Policy Making.

Introduction: Depression is a common but often undiagnosed comorbid condition of people with diabetes. Mass screening can detect undiagnosed depression but may require significant resources and time. The objectives of this study were 1) to develop a clinical forecasting model that predicts comorbid depression among patients with diabetes and 2) to evaluate a model-based screening policy that saves resources and time by screening only patients considered as depressed by the clinical forecasting model.

Methods: We trained and validated 4 machine learning models by using data from 2 safety-net clinical trials; we chose the one with the best overall predictive ability as the ultimate model. We compared model-based policy with alternative policies, including mass screening and partial screening, on the basis of depression history or diabetes severity.

Results: Logistic regression had the best overall predictive ability of the 4 models evaluated and was chosen as the ultimate forecasting model. Compared with mass screening, the model-based policy can save approximately 50% to 60% of provider resources and time but will miss identifying about 30% of patients with depression. Partial-screening policy based on depression history alone found only a low rate of depression. Two other heuristic-based partial screening policies identified depression at rates similar to those of the model-based policy but cost more in resources and time.

Conclusion: The depression prediction model developed in this study has compelling predictive ability. By adopting the model-based depression screening policy, health care providers can use their resources and time better and increase their efficiency in managing their patients with depression.