Construction and validation of a nomogram prediction model for risk of depression in elderly patients with hypertension
- VernacularTitle:老年高血压患者抑郁发生风险的列线图预测模型的构建和验证
- Author:
Hua HE
1
;
Wenxue FENG
;
Qinglin LI
;
Jinming SU
;
Kangning SUN
;
Wenjun WANG
Author Information
- Keywords: the elderly; hypertension; depression; nomogram; influencing factors; sleep duration; activity of daily living; self-rated health status
- From: Journal of Clinical Medicine in Practice 2025;29(19):120-124
- CountryChina
- Language:Chinese
- Abstract: Objective To explore the influencing factors of depression risk in elderly patients with hypertension and construct and validate a nomogram prediction model.Methods A total of 869 elderly patients with hypertension were selected from national survey database of the China Health and Retirement Longitudinal Study(CHARLS)in 2018.Multivariate Logistic regression analysis was used to identify the risk factors for depression in elderly patients with hypertension,and a nomogram prediction model was constructed.The accuracy and effectiveness of the model were validated by the Hosmer-Lemeshow(H-L)goodness-of-fit test,the area under the curve(AUC)of the receiver oper-ating characteristic(ROC)curve,and the calibration curve.Results The incidence of depression in elderly patients with hypertension was 47.18％.Factors influencing the risk of depression included rural residence(OR=2.191,P＜0.05),impaired basic activities of daily living(BADL)(OR=2.338,P＜0.05),impaired instrumental activitiesofdaily living(IADL)(OR=1.674,P＜0.05),poor life satisfaction(OR=7.348,P＜0.05),fair self-rated health(OR=0.441,P＜0.05),good self-rated health(OR=0.259,P＜0.05),and sleep duration of 6 to 9 hours(OR=0.510,P＜0.05).The AUC of the ROC curve was 0.795,the slope of the calibration curve was close to 1,and the H-L goodness-of-fit test yielded x2=5.074.The validation set showed an AUC of 0.703.Conclusion The prediction model established in this study has high accuracy and discriminative ability.Healthcare professionals can take effective preventive measures based on individual patient factors.
