International journal of chronic obstructive pulmonary disease · 2026
A Nomogram for Predicting Successful Weaning from Invasive Mechanical Ventilation Withdrawal in Patients with Chronic Obstructive Pulmonary Disease Complicated by Respiratory Failure.
СтатьяВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Bao Hongzhu, Zhu Tianyi, Li Chunhua, Huan Jianbo, Li Ling, Kong Lingyu, Chen Yan, Shi Liang, Li Congcong
Аннотация
OBJECTIVE: To develop and validate a practical nomogram for predicting the success rate of invasive mechanical ventilation (IMV) weaning in patients with chronic obstructive pulmonary disease (COPD) complicated by acute hypercapnic respiratory failure.
METHODS: A total of 159 eligible patients admitted between January 2020 and December 2024 were enrolled in this study, and they were randomly divided into a development cohort (n = 106) and a validation cohort (n = 53) at a 2:1 ratio. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to identify independent risk factors, which were used to construct a predictive nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), as well as through risk stratification validation.
RESULTS: LASSO regression identified 5 candidate variables, and multivariate analysis confirmed that anxiety, pneumothorax, vasoactive drug use, and NT-proBNP were independent risk factors for weaning failure (all P < 0.05). The nomogram achieved excellent discriminative ability, with areas under the ROC curve (AUCs) of 0.942 (95% CI: 0.868-1) in the development cohort and 0.982 (95% CI: 0.944-1) in the validation cohort. The calibration curves indicated good consistency between the predicted and actual probabilities, and DCA demonstrated a favorable net clinical benefit. Using a cutoff value of 0.3, patients were effectively stratified into low-risk and high-risk groups by the nomogram, with significantly different weaning success rates between the cohorts (both P < 0.0001).
CONCLUSION: This study developed a simple, reliable, and clinically applicable nomogram for predicting IMV weaning success in COPD patients with respiratory failure. The nomogram might assist clinicians in implementing early risk stratification, optimizing weaning strategies, and improving clinical outcomes.
METHODS: A total of 159 eligible patients admitted between January 2020 and December 2024 were enrolled in this study, and they were randomly divided into a development cohort (n = 106) and a validation cohort (n = 53) at a 2:1 ratio. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to identify independent risk factors, which were used to construct a predictive nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), as well as through risk stratification validation.
RESULTS: LASSO regression identified 5 candidate variables, and multivariate analysis confirmed that anxiety, pneumothorax, vasoactive drug use, and NT-proBNP were independent risk factors for weaning failure (all P < 0.05). The nomogram achieved excellent discriminative ability, with areas under the ROC curve (AUCs) of 0.942 (95% CI: 0.868-1) in the development cohort and 0.982 (95% CI: 0.944-1) in the validation cohort. The calibration curves indicated good consistency between the predicted and actual probabilities, and DCA demonstrated a favorable net clinical benefit. Using a cutoff value of 0.3, patients were effectively stratified into low-risk and high-risk groups by the nomogram, with significantly different weaning success rates between the cohorts (both P < 0.0001).
CONCLUSION: This study developed a simple, reliable, and clinically applicable nomogram for predicting IMV weaning success in COPD patients with respiratory failure. The nomogram might assist clinicians in implementing early risk stratification, optimizing weaning strategies, and improving clinical outcomes.