PS2-26 DEVELOPMENT OF A CLINICAL FORECASTING MODEL FOR DETECTING COMORBID DEPRESSION AMONG PATIENTS WITH DIABETES AND AN APPLICATION IN DEPRESSION SCREENING POLICYMAKING

Purpose: Approximately 30% of diabetes patients are suffering from depression, but nearly half of them are undiagnosed. Although universal depression screening improves diagnosis rates, it is a labor-intensive intervention. This study developed a clinical forecasting model for automatically detecting comorbid depression among patients with diabetes and applied the model to derive a screening policy to improve efficiency of depression screening.

Method: Machine learning methods were used to develop the model to forecast occurrence of major depression, measured by Patient Health Questionnaire 9-item score≥10. Predictors were selected using a correlation-based subset evaluation method from 20 risk factors of depression. Two linear models, Ridge logistic regression and multilayer perceptron, and two nonlinear models, support vector machine and random forest, were trained and validated on data pooled from two safety-net clinical trials of diabetes and depression (N=1793). The model with the best overall predictive ability, measured by area under receiver-operating curve (AUROC), was chosen as the ultimate model. Depression identification rate and measures relevant to provider resource and time were compared between a model-based policy that screens only patients predicted as being depression and alternative policies. These policies include universal screening and partial screening based on certain risk factors of depression such as depression history, diabetes severity, or either criteria.

Result: Seven predictors were selected to develop the prediction model: 1) gender, 2) Tolbert diabetes self-care 3) number of diabetes complications, 4) previous diagnosis of major depression, 5) number of ICD-9 diagnoses in past 6 months, 6) chronic pain, and 7) self-rated health status. Ridge logistic regression with the above seven predictors had the best overall predictive ability (AUROC=0.81) and was chosen as the ultimate model. Compared to universal screening, the model-based policy can save about 50-60% of provider resources and time but will miss identification of about 30% of depression cases. Partial-screening policy based on depression history alone yielded a very low rate of depression identification. Two other partial screening policies have depression identification rates similar to 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, healthcare providers can better prioritize the use of their resources and time while increasing efficiency in managing their patient population with depression.