Construction and validation of a risk prediction model for intraoperative acquired pressure injury in neurosurgical children
10.3761/j.issn.0254-1769.2025.08.005
- VernacularTitle:神经外科患儿术中获得性压力性损伤风险预测模型的构建及验证
- Author:
Shanshan HAN
1
;
Yongping QIN
;
Hong QU
;
Xianlan ZHENG
Author Information
1. 401122 重庆市 国家儿童健康与疾病临床医学研究中心/儿童发育疾病研究教育部重点实验室/儿童代谢与炎症性疾病重庆市重点实验室/重庆医科大学附属儿童医院手术室
- Publication Type:Journal Article
- Keywords:
Neurosurgery;
Intraoperative Acquired Pressure Injury;
Nomograph;
Pediatric Nursing
- From:
Chinese Journal of Nursing
2025;60(8):928-933
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a risk prediction model for intraoperative acquired pressure injury(IAPI)during neurosurgery in pediatric patients,and verify the predictive effect of the model,to provide a reference for preventing IAPI during neurosurgery in pediatric patients.Methods The clinical data of 776 pediatric patients undergoing neurosurgery in a tertiary-level hospital in Chongqing from January to June 2023 were retrospectively collected.The risk factors for IAPI were explored through univariate analysis and binary Logistic regression analysis.The fitting degree and predictive effect of the model were verified by Hosmer-Lemeshow test and receiver operator characteristic(ROC)curve,respectively.The model was validated internally by Bootstrap.Results The incidence of IAPI during neurosurgery in pediatric patients was 7.99％.Logistic regression analysis showed that bleeding volume,anesthesia time,age,intraoperative use of instruments such as drills and milling cutters that increase external force,and surgical position were the factors influencing IAPI in neurosurgical children(all P＜0.05).The results of the Hosmer-Lemeshow test showed that x2=3.636,P=0.888.The results of internal verification showed that the sensitivity of the model was 0.59;the specificity was 0.81;the area under the ROC curve was 0.79.Conclusion This study analyzes the risk factors for IAPI during neurosurgery in pediatric patients and constructs a line chart prediction model with good predictive performance,which can provide a reference for individualized prediction of the risk of IAPI during neurosurgery in pediatric patients.It can provide a scientific basis for clinical nursing staff to identify high-risk children with IAPI early and take personalized preventive measures in time.
