• Cristian Papuzinski, Miguel Durante, Catalina Tobar, Felipe Martinez, and Eduardo Labarca.
• Escuela de Medicina, Universidad de Valparaíso, Valparaiso, Chile.
• Am J Otolaryngol. 2013 Sep 1; 34 (5): 517-22.

BackgroundPatients requiring prolonged invasive mechanical ventilation are prone to complications, such as infections, tracheal stenosis and death. It has been proposed that early tracheostomy could have a role in preventing these outcomes, but the proper identification of patients at risk can be difficult.PurposeThe aim of this study was to develop a multivariate model that allows the early detection of patients that will require prolonged ventilatory support.Patients And MethodsA retrospective cohort study was undertaken in the intensive care unit of the Hospital Naval Almirante Nef, Chile, between June 2011 and June 2012. The charts of all intubated patients were reviewed in search for early predictors of prolonged intubation (>7 days). Multivariate logistic regression analysis was used to detect statistically significant associations and to assess potential confounders.ResultsA total of 349 patients were admitted to the intensive care unit during the study period and 142 (40.7%) required invasive mechanical ventilation. Most of them were male (60.5%), with a mean age of 65.8 ± 16.7 years. Thirty-five patients (24%) required to be ventilated for 7 days or more, and 16 (46%) were tracheostomized for this reason. The regression model showed that older age (p=0.026), a Pa/Fi ratio of less than 200 (p=0.046), and the presence of chronic pulmonary disease (p=0.035) or hypernatremia (p=0.012) on intubation day were significantly associated with the requirement of prolonged intubation.DiscussionInvasive mechanical ventilation is a common reason for admittance to the ICU. The abovementioned predictors can be of assistance when selecting patients that could benefit from early tracheostomies, and are in agreement with earlier reports. Although the model's discriminating capacity was good, it is necessary to formally validate it before recommending its widespread use.Copyright © 2013 Elsevier Inc. All rights reserved.

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