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Methods Inf Med 2015; 54(06): 553-559
DOI: 10.3414/ME14-02-0009
Focus Theme – Original Articles
Schattauer GmbH

Predicting Depression among Patients with Diabetes Using Longitudinal Data[*]

A Multilevel Regression Model
H. Jin
1   Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California, USA
,
S. Wu
2   School of Social Work and Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California, USA
,
I. Vidyanti
3   Division of Chronic Disease and Injury Prevention, Los Angeles County Department of Public Health, Schaeffer Center of Health Policy and Economics, University of Southern California, Los Angeles, USA
,
P. Di Capua
4   Baptist Health South Florida, Miami, Florida, USA
,
B. Wu
5   Keck School of Medicine, University of Southern California, Los Angeles, California, USA
› Author Affiliations
Further Information

Publication History

received: 14 September 2014

accepted: 06 July 2015

Publication Date:
23 January 2018 (online)

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Summary

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Big Data and Analytics in Healthcare”.

Background: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords

Depression - diabetes mellitus - comorbidity - machine learning - multilevel regression

* Supplementary online material published on our website http://dx.doi.org/10.3414/ME14-02-0009


 

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