A Nomogram of Weaning Failure for Critical Ventilated Patients in High-Altitude Areas: A Single-Center Cohort Study Using Lasso Logistic Regression
Bin Wang, Li Cheng, GuoYing Lin, Ci Yang, HuiYing Zhao

TL;DR
This study creates a prediction model to identify patients likely to fail weaning from ventilators in high-altitude regions.
Contribution
A novel nomogram model using Lasso logistic regression to predict weaning failure in high-altitude critical care patients.
Findings
Hypertension, low serum albumin, high SOFA scores, low tidal volumes, and high respiratory rates are risk factors for weaning failure.
The model achieved high predictive accuracy (AUC of 0.895 in training and 0.886 in validation).
Abstract
Objective: This study aimed to develop a predictive model for weaning failure in critically ill patients at high altitudes. Methods: Data of patients requiring invasive mechanical ventilation admitted to the Department of Intensive Care Medicine of Xizang Autonomous Region People's Hospital from January 1, 2023, to November 31, 2023, were retrospectively collected as the train set. The patients were weaned according to the conventional clinical strategy and divided into successful and failed weaning groups. Univariate analysis was performed between the weaning success and weaning failure groups. Indicators with inter-group differences were included in the Lasso regression for further screening and then included in the multivariate logistic regression analysis to establish independent risk factors. Subsequently, a nomogram prediction model was constructed. Data of patients from December…
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Taxonomy
TopicsRespiratory Support and Mechanisms · Chronic Obstructive Pulmonary Disease (COPD) Research · High Altitude and Hypoxia
