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Tracheostomy and long-term mortality in ICU patients undergoing prolonged mechanical ventilation.

ABSTRACT: INTRODUCTION:In critically ill patients undergoing prolonged mechanical ventilation (MV), the difference in long-term outcomes between patients with or without tracheostomy remains unexplored. METHODS:Ancillary study of a prospective international multicentre observational cohort in 21 centres in France and Belgium, including 2087 patients, with a one-year follow-up after admission. We included patients with a MV duration ?10 days, with or without tracheostomy. We explored the one-year mortality with a classical Cox regression model (adjustment on age, SAPS II, baseline diagnosis and withdrawal of life-sustaining therapies) and a Cox regression model using tracheostomy as a time-dependant variable. RESULTS:29.5% patients underwent prolonged MV, out of which 25.6% received tracheostomy and 74.4% did not. At one-year, 45.2% patients had died in the tracheostomy group and 51.5% patients had died in the group without tracheostomy (p = 0.001). In the Cox-adjusted regression model, tracheostomy was not associated with improved one-year outcome (HR CI95 0.7 [0.5-1.001], p = 0.051), as well as in the model using tracheostomy as a time-dependent variable (OR CI 95 1 [0.7-1.4], p = 0.9). CONCLUSIONS:In our study, there was no statistically significant difference in the one-year mortality of patients undergoing prolonged MV when receiving tracheostomy or not. TRIAL REGISTRATION:NCT01367093.

SUBMITTER: Cinotti R

PROVIDER: S-EPMC6774500 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Tracheostomy and long-term mortality in ICU patients undergoing prolonged mechanical ventilation.

Cinotti Raphaël R Voicu Sebastian S Jaber Samir S Chousterman Benjamin B Paugam-Burtz Catherine C Oueslati Haikel H Damoisel Charles C Caillard Anaïs A Roquilly Antoine A Feuillet Fanny F Mebazaa Alexandre A Gayat Etienne E

PloS one 20191002 10

<h4>Introduction</h4>In critically ill patients undergoing prolonged mechanical ventilation (MV), the difference in long-term outcomes between patients with or without tracheostomy remains unexplored.<h4>Methods</h4>Ancillary study of a prospective international multicentre observational cohort in 21 centres in France and Belgium, including 2087 patients, with a one-year follow-up after admission. We included patients with a MV duration ≥10 days, with or without tracheostomy. We explored the one ...[more]

PMID: 31577804

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Project description:BackgroundAmong patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models.MethodsRetrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model.ResultsThere were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality.DiscussionWe developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.

Dataset Information

Tracheostomy and long-term mortality in ICU patients undergoing prolonged mechanical ventilation.

Publications

Tracheostomy and long-term mortality in ICU patients undergoing prolonged mechanical ventilation.

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