Predicting Depression among Patients with Diabetes Using Longitudinal Data. A Multilevel Regression Model

• Published in: Methods of information in medicine Vol. 54; no. 6; p. 553
• Main Authors: Jin, H, Wu, S, Vidyanti, I, Di Capua, P, Wu, B
• Format: Journal Article
• Language: English
• Published: Germany 2015
• Subjects: California - epidemiology; Causality; Computer Simulation; Decision Support Systems, Clinical - organization & administration; Depression - diagnosis; Depression - epidemiology; Diabetes Complications - diagnosis; Diabetes Complications - epidemiology; Electronic Health Records - classification; Electronic Health Records - statistics & numerical data; Humans; Longitudinal Studies; Machine Learning; Natural Language Processing; Prevalence; Prognosis; Proportional Hazards Models; Regression Analysis; Reproducibility of Results; Risk Factors; Sensitivity and Specificity; Depression; diabetes mellitus; comorbidity; multilevel regression; machine learning
• ISSN: 2511-705X
• DOI: 10.3414/ME14-02-0009

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Depression is a common and often undiagnosed condition for patients with diabetes. It is also a condition that significantly impacts healthcare outcomes, use, and cost as well as elevating suicide risk. Therefore, a model to predict depression among diabetes patients is a promising and valuable tool for providers to proactively assess depressive symptoms and identify those with depression. This study seeks to develop a generalized multilevel regression model, using a longitudinal data set from a recent large-scale clinical trial, to predict depression severity and presence of major depression among patients with diabetes. Severity of depression was measured by the Patient Health Questionnaire PHQ-9 score. Predictors were selected from 29 candidate factors to develop a 2-level Poisson regression model that can make population-average predictions for all patients and subject-specific predictions for individual patients with historical records. Newly obtained patient records can be incorporated with historical records to update the prediction model. Root-mean-square errors (RMSE) were used to evaluate predictive accuracy of PHQ-9 scores. The study also evaluated the classification ability of using the predicted PHQ-9 scores to classify patients as having major depression. Two time-invariant and 10 time-varying predictors were selected for the model. Incorporating historical records and using them to update the model may improve both predictive accuracy of PHQ-9 scores and classification ability of the predicted scores. Subject-specific predictions (for individual patients with historical records) achieved RMSE about 4 and areas under the receiver operating characteristic (ROC) curve about 0.9 and are better than population-average predictions. The study developed a generalized multilevel regression model to predict depression and demonstrated that using generalized multilevel regression based on longitudinal patient records can achieve high predictive ability.