A developed nomogram based on eight clinical factors demonstrated excellent discrimination for identifying depressive symptoms in older inpatients, with an area under the curve of 0.930.
Cross-Sectional (n=11,269)
No
A newly developed nomogram based on 8 simple clinical features demonstrated satisfactory accuracy for the preliminary screening of depressive symptoms in older inpatients.
p-value: p=<0.001
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. Keywords: aged, inpatients, depressive symptoms, prognostic factors, risk assessment
Wang et al. (Fri,) conducted a cross-sectional in Depressive symptoms (n=11,269). Risk screening nomogram was evaluated on Area under the receiver operating characteristic curve (AUC) of the nomogram (95% CI 0.919-0.941, p=<0.001). A developed nomogram based on eight clinical factors demonstrated excellent discrimination for identifying depressive symptoms in older inpatients, with an area under the curve of 0.930.