Risk prediction model of pre-hospital emergency cardiac arrest based on Logistic regression
10.16753/j.cnki.1008-2344.2025.05.006
- VernacularTitle:构建基于Logistic回归的院前急救心搏骤停风险预测模型
- Author:
Jinling YI
1
;
Xiaoxiao ZENG
1
;
Xin WEN
1
Author Information
1. 江西省新余市紧急救援中心急救科,江西 新余 336500
- Publication Type:Journal Article
- Keywords:
pre-hospital emergency;
cardiac arrest;
risk factor;
prediction model
- From:
Journal of Shenyang Medical College
2025;27(5):482-486
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the risk factors of cardiac arrest during pre-hospital emergency treatment,and to construct the corresponding nomogram model.Methods:A retrospective analysis was performed on 159 patients who were dispatched by our center and received pre-hospital emergency treatment from Jan 2021 to Jan 2023.As the modeling group,these patients were divided into the occurrence group(n=54)and the non-occurrence group(n=105)according to whether they had cardiac arrest.According to the ratio of modeling group∶validation group=1.2∶1.0,132 patients who were dispatched and received pre-hospital emergency treatment by our center from Feb 2023 to Oct 2024 were selected as the validation group.They were divided into the occurrence group(n=45)and the non-occurrence group(n=87).Multivariable Logistic regression analysis was employed to investigate the factors influencing pre-hospital cardiac arrest and to construct a predictive model.The value of the predictive model was evaluated using receiver operating characteristic(ROC)curve,calibration curve,and decision curve.Results:Multivariate Logistic regression analysis showed that age＞59 years old(OR=0.658,95%CI:0.559-0.773),rescue arrival time＞10 min(OR=7.699,95%CI:3.013-19.675)and non-standard pre-hospital emergency measures(OR=2.807,95%CI:1.150-6.853)were independent risk factors for pre-hospital emergency cardiac arrest(P＜0.05).The nomogram model constructed according to the above factors was verified,and the C-index of the modeling group and verification group model was 0.798(95%CI:0.749-0.847)and 0.794(95%CI:0.740-0.848),respectively,which proved that the prediction efficiency of this model was good,and the decision curve showed good clinical benefit.Conclusion:Age＞59 years old,rescue arrival time＞10 min,and non-standard pre-hospital emergency measures are independent risk factors for pre-hospital emergency cardiac arrest,and the constructed prediction model has good clinical application.
