Evidence map›Paper›PMID 42165005›Full record

ArticleClinical interventions in aging2026

Development and Validation of a Risk Screening Model for Depressive Symptoms in Older Inpatients: A Cross-Sectional Study.

Lu Wang, Xin Zhang, Shaoping Cheng, Kaiqin Deng

Abstract readValidation Study
In one paragraph

Article in Clinical interventions in aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Lu WangDepartment of Science and Education, The First Affiliated Hospital of Yangtze University, Jingzhou, People's Republic of China.
Xin ZhangDepartment of Medical Record Statistics, The First Affiliated Hospital of Yangtze University, Jingzhou, People's Republic of China.
Shaoping ChengDepartment of Surgery, The First Affiliated Hospital of Yangtze University, Jingzhou, People's Republic of China.
Kaiqin DengDepartment of Mental Health, The First Affiliated Hospital of Yangtze University, Jingzhou, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Depression is common among the elderly and linked to higher morbidity, mortality, and healthcare costs. Hospitalized elderly patients are especially vulnerable. This study aimed to develop a novel, convenient, and validated tool for preliminary screening of depression symptoms in older inpatients. Patients and Methods: This study utilized clinical data from 11,269 hospitalized geriatric patients aged ≥60 years, collected from January 2023 to December 2024. Data sources included medical record systems,health information management system and inpatient psychological assessment scale information system. Multivariate logistic regression analysis was performed to determine the predictors and further construct a nomogram based on the predictors. Bootstrap with 5000 resamples was used for internal validation of nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA). Results: The prevalence of depressive symptoms in older inpatients was 28.64%. Eight independent influencing factors were identified: gender, age, marital status, income source, sleep disturbance, chronic diseases, pain, and self-rated health. The development set AUC was 0.930, and the corrected AUC after internal validation was 0.832. The Hosmer-Lemeshow test (P = 0.126) and calibration curves indicated favorable calibration. DCA confirmed clinical net benefit across a range of threshold probabilities. Conclusion: An easy-to-use nomogram was developed for identifying depressive symptoms in older inpatients with satisfactory screening ability based on simple and easily accessible clinical features. The nomogram can identify older inpatients at high risk for depressive symptoms and may be a useful preliminary screening tool in clinical.

Indexed as

DepressionInpatientsMass ScreeningAgedAged, 80 and overCross-Sectional StudiesFemaleGeriatric AssessmentHumansLogistic ModelsMaleMiddle AgedNomogramsRisk AssessmentRisk FactorsROC Curveageddepressive symptomsinpatientsprognostic factorsrisk assessment

Identifiers

PMID42165005
PMCPMC13185958

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