Evidence map›Paper›PMID 41689664›Full record

ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2026

Predictors of successful decannulation in patients with chronic disorders of consciousness and tracheostomy.

Zhang Hong, Chen Lang, Shao Qiang, Sun Li, Xiao Kai, Mo Dan, Wang Shu, Xu Jianqi

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Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Zhang HongRehabilitation Department of Yangtze River Shipping General Hospital, Wuhan Brain Hospital, Wuhan, China.
Chen LangNeurosurgery Department of Yangtze River Shipping General Hospital, Wuhan Brain Hospital, Wuhan, China.
Shao QiangNeurosurgery Department of Yangtze River Shipping General Hospital, Wuhan Brain Hospital, Wuhan, China. shaoqiang19@126.com.
Sun LiRehabilitation Department of Yangtze River Shipping General Hospital, Wuhan Brain Hospital, Wuhan, China.
Xiao KaiNeurosurgery Department of Yangtze River Shipping General Hospital, Wuhan Brain Hospital, Wuhan, China.
Mo DanRehabilitation Department of Yangtze River Shipping General Hospital, Wuhan Brain Hospital, Wuhan, China.
Wang ShuRehabilitation Department of Yangtze River Shipping General Hospital, Wuhan Brain Hospital, Wuhan, China.
Xu JianqiRehabilitation Department of Yangtze River Shipping General Hospital, Wuhan Brain Hospital, Wuhan, China. xuanjianqi1987@126.com.ORCID http://orcid.org/0009-0005-0730-9216

Funding

Key Science and Technology Project of the Yangtze River Navigation Administration of the Ministry of Transport of China in 2023 2023-CHKJ-023
6 · The paper itself

Abstract

objectiveTo investigate the independent predictive factors for successful decannulation in patients with chronic disorders of consciousness (pDoC), and to provide evidence for clinical decision-making regarding safe tracheostomy tube removal.

methodsWe conducted a single-center retrospective cohort study and consecutively enrolled 156 patients with tracheotomy and post-disorders of consciousness (pDoC) who were admitted from January 2022 to June 2025. Data on demographics, etiology, level of consciousness (assessed using the Coma Recovery Scale–Revised, CRS-R), and airway function (cough strength, sputum volume, signs of aspiration, airway structure) were collected. All patients underwent a standardized pre-decannulation assessment protocol, including fiberoptic bronchoscopy, a cuff-leak test, and a tube occlusion trial. Univariate analysis and multivariate logistic regression modeling were employed to identify factors independently associated with successful decannulation.

resultsOf the 156 patients, 89 (57.1%) were successfully decannulated. Multivariate logistic regression analysis identified the following independent protective factors for successful decannulation: a minimally conscious state (MCS) (OR = 5.58, 95% CI: 2.18–14.27), strong voluntary cough strength (OR = 5.21, 95% CI: 2.12–12.82), and daily sputum volume ≤ 30 mL (OR = 4.86, 95% CI: 2.02–11.70).

conclusionLevel of consciousness (minimally conscious state), cough strength, and sputum volume are key independent predictors of successful decannulation in patients with pDoC. An assessment pathway based on these indicators can help identify suitable candidates for decannulation, optimize clinical management strategies, and promote patient recovery and prognosis.

Indexed as

Consciousness DisordersTracheostomyAdultAgedChronic DiseaseFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesTracheostomy DecannulationChronic disorders of consciousnessDecannulationLogistic regressionPrognostic factorsTracheostomy

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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.