Risk factor and predictive index of mechanical ventilation withdrawal failure in patients with cardiogenic respiratory failure
10.3969/j.issn.1009-0754.2024.12.019
- VernacularTitle:心源性呼吸衰竭机械通气撤机失败相关风险及预测指标分析
- Author:
Tiejun ZHOU
1
;
Feifei MA
;
Baorong LU
;
Zhili PENG
Author Information
1. 226500 江苏 南通,如皋市中医院重症医学科
- Keywords:
Cardiogenic respiratory failure;
Mechanical ventilation;
Withdrawal;
Influence factors;
Predictive value
- From:
Journal of Navy Medicine
2024;45(12):1312-1316
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the risk factors and predictive value of mechanical ventilation withdrawal failure in patients with cardiogenic respiratory failure.Methods Eighty patients with cardiogenic respiratory failure who were treated in our Rugao Hospital of Traditional Chinese Medicine from January 2019 to July 2022 were enrolled in this study.They were divided into successful withdrawal group(n=67)and failed withdrawal group(n=13)according to the results of tracheal extubation.Clinical data and ultrasonic parameters were compared between the two groups.The influence factors of mechanical ventilation withdrawal were analyzed.Results The age,proportion of patients with hypertension,the levels of N-terminal precursor of B-type natriuretic peptide(NT-proBNP),procalcitonin(PCT),and C-reactive protein(CRP),acute physiology and chronic health evaluation Ⅱ(APACHEⅡ)score,and systolic left atrial area(LAA)in the failed withdrawal group,were significantly higher than those in the successful withdrawal group(P<0.05).Logistic regression analysis showed that age,NT-proBNP,PCT,CRP,APACHE Ⅱ score and LAA were the influencing factors of mechanical ventilation withdrawal failure in patients with cardiogenic respiratory failure(P<0.05).The area under the ROC curve for predicting withdrawal failure was 0.834(95%CI:0.724-0.944,P<0.05),and the sensitivity and specificity were 84.60%and 68.40%,respectively.Conclusion The mechanical ventilation withdrawal is influenced by age,NT-proBNP,PCT,CRP,APACHE Ⅱ score and LAA in patients with cardiac respiratory failure.The construction of logistic regression model may help to predict the withdrawal failure.
