Effects of tracheostomy timing in adult patients receiving mechanical ventilation: A systematic review and network meta-analysis

Kishihara, Yuki; Yasuda, Hideto; Ozawa, Hidechika; Fukushima, Fumihito; Kashiura, Masahiro; Moriya, Takuya

doi:10.1016/j.jcrc.2023.154299

Journal of Critical Care

2023

DOI: 10.1016/j.jcrc.2023.154299

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Effects of tracheostomy timing in adult patients receiving mechanical ventilation: A systematic review and network meta-analysis

Yuki Kishihara¹,

Hideto Yasuda²,

Hidechika Ozawa³

et al.

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2024

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Cited by 2 publications

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Predicting Tracheostomy Need on Admission to the Intensive Care Unit—A Multicenter Machine Learning Analysis

Nguyen,

Amanian,

Wei

et al. 2024

Otolaryngol.--head neck surg.

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ObjectiveIt is difficult to predict which mechanically ventilated patients will ultimately require a tracheostomy which further predisposes them to unnecessary spontaneous breathing trials, additional time on the ventilator, increased costs, and further ventilation‐related complications such as subglottic stenosis. In this study, we aimed to develop a machine learning tool to predict which patients need a tracheostomy at the onset of admission to the intensive care unit (ICU).Study DesignRetrospective Cohort Study.SettingMulticenter Study of 335 Intensive Care Units between 2014 and 2015.MethodsThe eICU Collaborative Research Database (eICU‐CRD) was utilized to obtain the patient cohort. Inclusion criteria included: (1) Age >18 years and (2) ICU admission requiring mechanical ventilation. The primary outcome of interest included tracheostomy assessed via a binary classification model. Models included logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost).ResultsOf 38,508 invasively mechanically ventilated patients, 1605 patients underwent a tracheostomy. The XGBoost, RF, and LR models had fair performances at an AUROC 0.794, 0.780, and 0.775 respectively. Limiting the XGBoost model to 20 features out of 331, a minimal reduction in performance was observed with an AUROC of 0.778. Using Shapley Additive Explanations, the top features were an admission diagnosis of pneumonia or sepsis and comorbidity of chronic respiratory failure.ConclusionsOur machine learning model accurately predicts the probability that a patient will eventually require a tracheostomy upon ICU admission, and upon prospective validation, we have the potential to institute earlier interventions and reduce the complications of prolonged ventilation.

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Predicting Tracheostomy Need on Admission to the Intensive Care Unit—A Multicenter Machine Learning Analysis

Nguyen,

Amanian,

Wei

et al. 2024

Otolaryngol.--head neck surg.

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Epidemiology, timing, technique, and outcomes of tracheostomy in patients with trauma: a multi‐centre retrospective study

Min,

Lee,

Lee

et al. 2024

ANZ Journal of Surgery

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BackgroundTracheostomy is performed in patients with trauma who need prolonged ventilation for respiratory failure or airway management. Although it has benefits, such as reduced sedation and easier care, it also has risks. This study explored the unclear timing, technique, and patient selection criteria for tracheostomy in patients with trauma.MethodsWe included 220 adult patients with trauma who underwent tracheostomy after endotracheal intubation between January 2019 and December 2022. We compared clinical outcomes between patients who underwent early (within 10 days) and late (after 10 days) tracheostomy and between patients who underwent percutaneous dilatational tracheostomy (PDT) and surgical tracheostomy (ST). Factors associated with hospital and intensive care unit (ICU) length of stay (LOS), ICU‐free days, duration of mechanical ventilation, and ventilator‐free days (VFDs) were identified using multiple linear regression analysis.ResultsThe patients' mean age was 61.5 years; 75.9% were men. Most tracheostomies were performed after 10 days (n = 135, 61.4%), with PDT serving as the more common approach during this period. Contrastingly, early tracheostomies (n = 85, 38.6%) were predominantly performed using ST. Early tracheostomy was significantly associated with reduced hospital (P = 0.038) and ICU LOS (P = 0.047), decreased duration of mechanical ventilation (P = 0.001), and increased VFDs (P < 0.001). However, no significant association was found with ICU‐free days (P = 0.072) or in‐hospital mortality (P = 0.917).ConclusionEarly tracheostomy was associated with reduced hospital and ICU LOS, decreased duration of mechanical ventilation, and increased VFDs.

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Effects of tracheostomy timing in adult patients receiving mechanical ventilation: A systematic review and network meta-analysis

Cited by 2 publications

References 22 publications

Predicting Tracheostomy Need on Admission to the Intensive Care Unit—A Multicenter Machine Learning Analysis

Predicting Tracheostomy Need on Admission to the Intensive Care Unit—A Multicenter Machine Learning Analysis

Epidemiology, timing, technique, and outcomes of tracheostomy in patients with trauma: a multi‐centre retrospective study

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