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Prediction models for depression risk among older adults: systematic review and critical appraisal.
Abstract
<h4>Objective</h4>To provide an overview of prediction models for the risk of major
depressive disorder (MDD) among older adults.<h4>Methods</h4>We conducted a systematic
review combined with a meta-analysis and critical appraisal of published studies on
existing geriatric depression risk models.<h4>Results</h4>The systematic search screened
23,378 titles and abstracts; 14 studies including 20 prediction models were included.
A total of 16 predictors were selected in the final model at least twice. Age, physical
health, and cognitive function were the most common predictors. Only one model was
externally validated, two models were presented with a complete equation, and five
models examined the calibration. We found substantial heterogeneity in predictor and
outcome definitions across models; important methodological information was often
missing. All models were rated at high or unclear risk of bias, primarily due to methodological
limitations. The pooled C-statistics of 12 prediction models was 0.83 (95%CI=0.77-0.89).<h4>Conclusion</h4>The
usefulness of all models remains unclear due to several methodological limitations.
Future studies should focus on methodological quality and external validation of depression
risk prediction models.
Type
Journal articlePermalink
https://hdl.handle.net/10161/26244Published Version (Please cite this version)
10.1016/j.arr.2022.101803Publication Info
Tan, Jie; Ma, Chenxinan; Zhu, Chonglin; Wang, Yin; Zou, Xiaoshuang; Li, Han; ... Wu,
Chenkai (2022). Prediction models for depression risk among older adults: systematic review and critical
appraisal. Ageing research reviews. pp. 101803. 10.1016/j.arr.2022.101803. Retrieved from https://hdl.handle.net/10161/26244.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Chenxinan Ma
Affiliate
Chenxinan Ma is a second-year master's student studying global health at Duke Kunshan
University and Duke University. Prior to studying at Duke, he completed a five-year
medical education undergraduate program in China with a focus on preventive medicine
at the Medical College of Soochow University, China. Chenxinan had a background in
life science and population health sciences and his research internests are healthy
aging and the management of chronic diseases.
Chenkai Wu
Assistant Professor of Global Health at Duke Kunshan University
Dr. Chenkai Wu is an Assistant Professor and Director of Graduate Studies in Global
Health at DKU. Before joining DKU, he was a faculty in the Department of Epidemiology
and Community Health at New York Medical College. His research interests include (1)
measurement, epidemiology, and clinical implications of frailty, (2) the interplay
of genetic predisposition with the social and natural environment in shaping healthy
aging, and (3) implications of machine learning for improving clinical pra
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