Development and validation of a mechanical power-oriented nomogram model for predicting the risk of weaning failure in mechanically ventilated patients: an analysis using the data from MIMIC-IV.
10.3760/cma.j.cn121430-20221115-00997
- Author:
Yao YAN
1
;
Yongpeng XIE
2
;
Jiye LUO
2
;
Yanli WANG
2
;
Xiaobing CHEN
2
;
Zhiqiang DU
1
;
Xiaomin LI
2
Author Information
1. Department of Critical Care Medicine, the Second People's Hospital of Lianyungang City, Lianyungang 222000, Jiangsu, China.
2. Department of Emergency Medicine, Lianyungang Clinical College of Nanjing Medical University (the First People's Hospital of Lianyungang City), Lianyungang 222000, Jiangsu, China. Corresponding author: Li Xiaomin, Email: lyglxm1@163.com.
- Publication Type:Journal Article
- MeSH:
Humans;
Respiration, Artificial/methods*;
Ventilator Weaning/methods*;
Nomograms;
Lung;
Risk Factors
- From:
Chinese Critical Care Medicine
2023;35(7):707-713
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To develop and validate a mechanical power (MP)-oriented nomogram prediction model of weaning failure in mechanically ventilated patients.
METHODS:Patients who underwent invasive mechanical ventilation (IMV) for more than 24 hours and were weaned using a T-tube ventilation strategy were collected from the Medical Information Mart for Intensive Care-IV v1.0 (MIMIC-IV v1.0) database. Demographic information and comorbidities, respiratory mechanics parameters 4 hours before the first spontaneous breathing trial (SBT), laboratory parameters preceding the SBT, vital signs and blood gas analysis during SBT, length of intensive care unit (ICU) stay and IMV duration were collected and all eligible patients were enrolled into the model group. Lasso method was used to screen the risk factors affecting weaning outcomes, which were included in the multivariate Logistic regression analysis. R software was used to construct the nomogram prediction model and build the dynamic web page nomogram. The discrimination and accuracy of the nomogram were assessed by receiver operator characteristic curve (ROC curve) and calibration curves, and the clinical validity was assessed by decision curve analysis (DCA). The data of patients undergoing mechanical ventilation hospitalized in ICU of the First People's Hospital of Lianyungang City and the Second People's Hospital of Lianyungang City from November 2021 to October 2022 were prospectively collected to externally validate the model.
RESULTS:A total of 3 695 mechanically ventilated patients were included in the model group, and the weaning failure rate was 38.5% (1 421/3 695). Lasso regression analysis finally screened out six variables, including positive end-expiratory pressure (PEEP), MP, dynamic lung compliance (Cdyn), inspired oxygen concentration (FiO2), length of ICU stay and IMV duration, with coefficients of 0.144, 0.047, -0.032, 0.027, 0.090 and 0.098, respectively. Logistic regression analysis showed that the six variables were all independent risk factors for predicting weaning failure risk [odds ratio (OR) and 95% confidence interval (95%CI) were 1.155 (1.111-1.200), 1.048 (1.031-1.066), 0.968 (0.963-0.974), 1.028 (1.017-1.038), 1.095 (1.076-1.113), and 1.103 (1.070-1.137), all P < 0.01]. The MP-oriented nomogram prediction model of weaning failure in mechanically ventilated patients showed accurate discrimination both in the model group and external validation group, with area under the ROC curve (AUC) and 95%CI of 0.832 (0.819-0.845) and 0.879 (0.833-0.925), respectively. Furthermore, its predictive accuracy was significantly higher than that of individual indicators such as MP, Cdyn, and PEEP. Calibration curves showed good correlation between predicted and observed outcomes. DCA indicated that the nomogram model had high net benefits, and was clinically beneficial.
CONCLUSIONS:The MP-oriented nomogram prediction model of weaning failure accurately predicts the risk of weaning failure in mechanical ventilation patients and provides valuable information for clinicians making decisions on weaning.
