Predicting Depression among Patients with Diabetes Using Longitudinal Data

H. Jin; S. Wu; I. Vidyanti; P. Di Capua; B. Wu

doi:10.3414/ME14-02-0009

Methods of Information in Medicine, Inhaltsverzeichnis

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

¹Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California, USA

,

S. Wu

²School of Social Work and Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California, USA

,

I. Vidyanti

³Division 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

⁴Baptist Health South Florida, Miami, Florida, USA

,

B. Wu

⁵Keck School of Medicine, University of Southern California, Los Angeles, California, USA

› Institutsangaben

Abstract

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

Volltext

Referenzen

References
1 Egede LE, Zheng D, Simpson K. Comorbid depression is associated with increased health care use and expenditures in individuals with diabetes. Diabetes Care 2002; 25 (03) 464-470.
2 Ducat L, Philipson LH, Anderson BJ. The mental health comorbidities of diabetes. JAMA 2014; 312 (07) 691-692.
3 Li C, Ford ES, Zhao G, Ahluwalia IB, Pearson WS, Mokdad AH. Prevalence and correlates of undiagnosed depression among US adults with diabetes: the Behavioral Risk Factor Surveillance System, 2006. Diabetes Res Clin Pract 2009; 83 (02) 268-279.
4 Lin EHB, Von Korff M, Ciechanowski P, Peterson D, Ludman EJ, Rutter CM. et al. Treatment Adjustment and Medication Adherence for Complex Patients With Diabetes, Heart Disease, and Depression: A Randomized Controlled Trial. Annals of Family Medicine 2012; 10 (01) 6-14.
5 Carper M, Traeger L, Gonzalez J, Wexler D, Psaros C, Safren S. The differential associations of depression and diabetes distress with quality of life domains in type 2 diabetes. J Behav Med 2014; 37 (03) 501-510.
6 Simon GE, VonKorff M, Barlow W. Health care costs of primary care patients with recognized depression. Arch Gen Psychiatry 1995; 52 (10) 850
7 Goodwin RD, Kroenke K, Hoven CW, Spitzer RL. Major depression, physical illness, and suicidal ideation in primary care. Psychosom Med 2003; 65 (04) 501-505.
8 Dobson KS, Dozois DJ. Risk factors in depression. Academic Press 2011
9 Kennedy N, Abbott R, Paykel E. Remission and recurrence of depression in the maintenance era: long-term outcome in a Cambridge cohort. Psychol Med 2003; 33 (05) 827-838.
10 Burcusa SL, Iacono WG. Risk for recurrence in depression. Clin Psychol Rev 2007; 27 (08) 959-985.
11 Lustman PJ, Griffith LS, Freedland KE, Clouse RE. The course of major depression in diabetes. Gen Hosp Psychiatry 1997; 19 (02) 138-143.
12 King M, Walker C, Levy G, Bottomley C, Royston P, Weich S. et al. Development and validation of an international risk prediction algorithm for episodes of major depression in general practice attendees: the PredictD study. Arch Gen Psychiatry 2008; 65 (12) 1368-1376.
13 King M, Bottomley C, Bellón-Saameño J, Torres-Gonzalez F. Švab I, Rotar D. et al. Predicting onset of major depression in general practice attendees in Europe: extending the application of the predictD risk algorithm from 12 to 24 months. Psychol Med 2013; 43 (09) 1929-1939.
14 Huang SH, LePendu P, Iyer SV, Tai-Seale M, Carrell D, Shah NH. Toward personalizing treatment for depression: predicting diagnosis and severity. J Am Med Inform Assoc 2014; 21 (06) 1069-1075.
15 Wang J, Sareen J, Patten S, Bolton J, Schmitz N, Birney A. A prediction algorithm for first onset of major depression in the general population: development and validation. J Epidemiol Community Health 2014; 68 (05) 418-424.
16 Diggle P, Heagerty P, Liang KY, Zeger S. Analysis of longitudinal data. Oxford University Press; 2002
17 Kroenke K, Spitzer RL. The PHQ-9: a new depression diagnostic and severity measure. Psychiatr Ann 2002; 32 (09) 1-7.
18 Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001; 16 (09) 606-613.
19 Katon WJ, Lin EH, Von Korff M, Ciechanowski P, Ludman EJ, Young B. et al. Collaborative care for patients with depression and chronic illnesses. N Engl J Med 2010; 363 (27) 2611-2620.
20 Wu S, Ell K, Gross-Schulman SG, Sklaroff LM, Katon WJ, Nezu AM. et al. Technology-facilitated depression care management among predominantly Latino diabetes patients within a public safety net care system: Comparative effectiveness trial design. Contemp Clin Trials 2014; 37 (02) 342-354.
21 Wu B, Jin H, Vidyanti I, Lee P-J. Ell K, Wu S. Collaborative Depression Care Among Latino Patients in Diabetes Disease Management, Los Angeles, 2011-2013. Prev Chronic Dis 2014; 11: E148
22 Wu S, Vidyanti I, Liu P, Hawkins C, Ramirez M, Guterman J. et al. Patient-Centered Technological Assessment and Monitoring of Depression for Low-Income Patients. J Ambul Care Manage 2014; 37 (02) 138-147.
23 Singer JD, Willett JB. Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press; 2003
24 Gardiner JC, Luo Z, Roman LA. Fixed effects, random effects and GEE: what are the differences?. Stat Med 2009; 28 (02) 221-239.
25 Statistical Package R. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2015
26 Venables WN, Ripley BD. Modern applied statistics with S. Springer Science & Business Media 2002
27 Breslow NE, Clayton DG. Approximate inference in generalized linear mixed models. J Am Stat Assoc 1993; 88 (421) 9-25.
28 Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol Evol 2009; 24 (03) 127-135.
29 Pinheiro JC, Bates DM. Mixed-effects models in S and S-PLUS. Springer; 2000
30 Breitung J, Chaganty N, Daniel R, Kenward M, Lechner M, Martus P. et al. Discussion of “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matri”. Methods Inf Med 2010; 49 (05) 426
31 Hubbard AE, Ahern J, Fleischer NL, Van der Laan M, Lippman SA, Jewell N. et al. To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Epidemiology 2010; 21 (04) 467-474.
32 Fisher L, Glasgow RE, Mullan JT, Skaff MM, Polonsky WH. Development of a brief diabetes distress screening instrument. Ann Fam Med 2008; 6 (03) 246-252.
33 Bates D. Lmer, p-values and all that. 2006. URL: https://stat.ethz.ch/pipermail/r-help/2006-May/ 094765.html

Zusatzmaterial

Zusatzmaterial

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

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