Evidence map›Paper›PMID 42371566›Full record

ArticleInternational journal of chronic obstructive pulmonary disease2026

A Nomogram for Predicting Successful Weaning from Invasive Mechanical Ventilation Withdrawal in Patients with Chronic Obstructive Pulmonary Disease Complicated by Respiratory Failure.

Hongzhu Bao, Tianyi Zhu, Chunhua Li, Jianbo Huan, Ling Li, Lingyu Kong, Yan Chen, Liang Shi, Congcong Li

Abstract readValidation Study
In one paragraph

Article in International journal of chronic obstructive pulmonary disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Hongzhu Bao *Department of Respiratory and Critical Care Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110013, People's Republic of China.
Tianyi Zhu *Department of Respiratory and Critical Care Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110013, People's Republic of China.
Chunhua LiDepartment of Respiratory and Critical Care Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110013, People's Republic of China.
Jianbo HuanDepartment of Respiratory and Critical Care Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110013, People's Republic of China.
Ling LiDepartment of Respiratory and Critical Care Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110013, People's Republic of China.
Lingyu KongDepartment of Respiratory and Critical Care Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110013, People's Republic of China.
Yan ChenDepartment of Respiratory and Critical Care Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110013, People's Republic of China.
Liang ShiDepartment of Respiratory and Critical Care Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110013, People's Republic of China.
Congcong LiDepartment of Respiratory and Critical Care Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110013, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Decision Support TechniquesNomogramsPulmonary Disease, Chronic ObstructiveRespiration, ArtificialRespiratory InsufficiencyVentilator WeaningAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsCOPDinvasive mechanical ventilationnomogramrisk predictionweaning

Identifiers

PMID42371566
PMCPMC13310405

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.