Prediction models for extubation failure in critically ill patients undergoing mechanical ventilation: a systematic review
10.3760/cma.j.cn115682-20240227-00964
- VernacularTitle:重症机械通气患者脱机失败预测模型的系统评价
- Author:
Yaru GUO
1
;
Han JI
;
Ziying WANG
;
Jianhong QIAO
Author Information
1. 山东第一医科大学（山东省医学科学院）护理学院，济南 250000
- Publication Type:Journal Article
- Keywords:
Mechanical ventilation;
Extubation failure;
Prediction model;
Systematic review
- From:
Chinese Journal of Modern Nursing
2025;31(6):797-802
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To systematically review the prediction models for extubation failure in critically ill patients undergoing mechanical ventilation, providing a reference for healthcare professionals in selecting appropriate models to identify high-risk populations.Methods:Literature on the construction of prediction models for extubation failure risk in critically ill patients undergoing mechanical ventilation was retrieved from China National Knowledge Infrastructure, Wanfang Database, VIP, SinoMed, PubMed, Web of Science, Embase, and Cochrane Library. The search was limited from database inception to February 2024. Two researchers independently screened the literature and extracted data, using bias risk assessment tools to evaluate the bias risk and applicability of the prediction models.Results:A total of nine studies were included, with the most common predictive factors being mechanical ventilation duration, Glasgow Coma Scale score, cough reflex strength, age, and 24-hour input/output volume. The area under the receiver operating characteristic curve for the models ranged from 0.689 to 0.926, indicating good predictive performance. However, the risk of bias was high, mainly due to small sample sizes, the selection of predictive factors based on univariate analysis, and lack of proper internal validation.Conclusions:Existing prediction models show good predictive performance, but they carry high bias risk. Future studies should improve research design, adhere to model development and reporting guidelines, and develop well-performing, user-friendly prediction models to more accurately identify high-risk populations for extubation failure.
