A neural network model based on circulating inflammation-related factors for risk of PSD:construction and prediction efficiency analysis
10.3969/j.issn.1009-0126.2025.01.014
- VernacularTitle:循环炎症相关因子神经网络模型预测脑卒中后抑郁发生风险的效能分析
- Author:
Fengling LI
1
;
Xue YANG
1
;
Haiyan CHEN
1
Author Information
1. 430070 武汉科技大学附属老年病医院脑科中心
- Publication Type:Journal Article
- Keywords:
stroke;
depression;
proportional hazards models;
neural network model
- From:
Chinese Journal of Geriatric Heart Brain and Vessel Diseases
2025;27(1):63-67
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a risk prediction model for post-stroke depression based on the neural network algorithm.Methods A prospective study was conducted on 260 stroke patients admitted in our center from March 2021 to March 2024.They were randomly divided into a train-ing set(80％,208 cases)and a verification set(20％,52 cases).According to the occurrence of post-stroke depression within 1 month after stroke,the training set was assigned into post-stroke depression group(62 cases)and non-post-stroke depression group(146 cases).The predictive fac-tors for post-stroke depression occurrence were screened through the training set,and the risk prediction models for post-stroke depression occurrence were constructed with multivariate logis-tic and neural network algorithms in the training set.The prediction efficiency of the two predic-tion models was compared and verified in the verification set.Results Within 1 month after stroke,76 cases(29.23％)experienced post-stroke depression(62 cases in training set and 14 in the validation set).Based on the data in the training set,the levels of CRP,FIB,IL-6,IL-lβ,TNF-αand IL-18,and neutrophil and lymphocyte ratio(NLR)were significant higher in the post-stroke depression group than the non-post-stroke depression group(P＜0.01).Multivariate logistic re-gression analysis showed that CRP(OR=1.494,95％CI:1.239-1.802),FIB(OR=1.924,95％CI:1.191-3.109),IL-6(OR=1.128,95％CI:1.001-1.272),TNF-α(OR=1.051,95％CI:1.010-1.093),IL-1β(OR=1.096,95％CI:1.006-1.194),IL-18(OR=1.019,95％CI:1.002-1.036),and NLR(OR=1.873,95％CI:1.027-3.418)were risk factors for post-stroke depression(P＜0.05,P＜0.01).ROC curve analysis indicated that the AUC value of the predictive model of the neural network algorithm was higher than that of the model of multivariate logistic regression(0.931 vs 0.855,Z=3.448,P＜0.05).Based on the validation set,the former model also had bet-ter accuracy than the latter one(92.31％vs 75.00％,P＜0.05).Conclusion Circulating inflam-matory factors CRP,FIB,IL-6,IL-1β,TNF-α and IL-18,and NLR are related to the risk of post-stroke depression.The prediction model based on above factors combined with neural network al-gorithm can more effectively predict the risk of post-stroke depression.
