Construction and validation of a depression risk prediction model for patients with cognitive impairment
10.3760/cma.j.cn115682-20220830-04248
- VernacularTitle:认知障碍患者抑郁风险预测模型的构建及验证
- Author:
Li LIAO
1
;
Xuefen LI
;
Jingping SHI
;
Xiaofang LI
;
Lili TAN
;
Chen YE
;
Yan KANG
Author Information
1. 南京医科大学附属脑科医院神经内科，南京 210029
- Keywords:
Depression;
Cognitive dysfunction;
Risk factors;
Prediction model
- From:
Chinese Journal of Modern Nursing
2023;29(20):2701-2707
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the risk factors for depression in patients with cognitive impairment and construct a prediction model to preliminarily validate the predictive performance of the model, aiming to provide medical and nursing staff with a screening tool for high-risk groups.Methods:From January 2020 to December 2021, convenience sampling was used to select 1 130 patients with cognitive impairment admitted to the Affiliated Brain Hospital of Nanjing Medical University as the research subject. The research subjects were divided into a modeling group ( n=791) and a validation group ( n=339) at a ratio of 7∶3. The influencing factors of depression in patients with cognitive impairment were determined using binomial Logistic regression and a risk prediction model was established. The predictive performance of the prediction model was tested using the receiver operating characteristic (ROC) curve. Results:The incidence of depression in 1 130 patients with cognitive impairment was 51.3% (580/1 130). Binomial Logistic regression analysis showed that the influencing factors for depression in patients included age, Activities of Daily Living Scale score, Hamilton Anxiety Scale score, Pittsburgh Sleep Quality Index score, and Lewy Body Composite Risk score ( P<0.05). In the modeling group, the area under the ROC curve was 0.921, the Youden index was 0.716, the sensitivity was 0.834, the specificity was 0.882, and the prediction accuracy was 0.858. In the validation group, the area under the ROC curve was 0.896, the Youden index was 0.651, the sensitivity was 0.824, the specificity was 0.827, and the prediction accuracy was 0.825. Conclusions:The depression risk prediction model can effectively predict the risk of depression in patients with cognitive impairment, and can provide a screening tool for high-risk groups for medical and nursing staff.
