ORIGINAL RESEARCH article
Front. Psychol.
Sec. Psychology of Aging
Volume 16 - 2025 | doi: 10.3389/fpsyg.2025.1628719
Predictive features analysis and nomogram construction for predicting depression in elderly patients
Provisionally accepted
- 1Department of Geriatrics, Fuzhou No.1 Hospital Affiliated with Fujian Medical University, Fuzhou,Fujian, China
- 2Department of Neurology, Fujian Medical University Union Hospital, Fuzhou,Fujian, China
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In elderly populations, depression is highly prevalent among those with chronic diseases and cognitive impairment, leading to distress, disability, and poor medical outcomes. With the aging of the population, the prevalence of geriatric depression is rising rapidly. The Comprehensive Geriatric Assessment (CGA), a multidimensional approach, evaluates medical, psychological, and functional capacities to identify highrisk individuals and may be correlated with depression in the elderly.From 2021 to 2023, a total of 219 geriatric patients were recruited. These patients were divided into two groups: a modeling group of 153 patients and a validation group of 66 patients. We collected patients' basic information and CGA results and analyzed them using univariate and multivariate regression. Independent variables influencing depression were identified. Multivariate regression analyses revealed that several factors had an impact on depression in these patients, including social support level ( SSRS ) , Anxiety, Pain, Basic Activities of Daily Living (BADL)and Gender.By integrating these factors into the nomogram, we found that the Receiver Operating Characteristic (ROC) curves and calibration curves of both patient groups showed satisfactory discrimination and model fit. The calibration and discrimination accuracy of the nomograms for predicting depression risk in the elderly were satisfactory, and the decision curve analysis demonstrated significant clinical utility.
Keywords: K e y w o r d s : Depression, p redictive features, nomogram, elderly patients, Depression screening model
Received: 14 May 2025; Accepted: 29 Jul 2025.
Copyright: © 2025 Lin, Zhao, Yu and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hongbin Chen, Department of Neurology, Fujian Medical University Union Hospital, Fuzhou,Fujian, China
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