No abstract

The development and evolution of the endotracheal tube (ETT) have been closely related to advances in surgery and anesthesia. Modifications were made to accomplish many tasks, including minimizing gross aspiration, isolating a lung, providing a clear facial surgical field during general anesthesia, monitoring laryngeal nerve damage during surgery, preventing airway fires during laser surgery, and administering medications. In critical care management, ventilator-associated pneumonia (VAP) is a major concern, as it is associated with increased morbidity, mortality, and cost. It is increasingly appreciated that the ETT itself is a primary causative risk for developing VAP. Unfortunately, contaminated oral and gastric secretions leak down past the inflated ETT cuff into the lung. Bacteria can also grow within the ETT in biofilm and re-enter the lung. Modifications to the ETT that attempt to prevent bacteria from entering around the ETT include maintaining an adequate cuff pressure against the tracheal wall, changing the material and shape of the cuff, and aspirating the secretions that sit above the cuff. Attempts to reduce bacterial entry through the tube include antimicrobial coating of the ETT and mechanically scraping the biofilm from within the ETT. Studies evaluating the effectiveness of these modifications and techniques demonstrate mixed results, and clear recommendations for which modification should be implemented are weak.

Supplemental Digital Content is available in the text.

Background: COVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19.Objective: This study aims to validate the PICTURE model's ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19. Methods:The PICTURE model was trained and validated on a cohort of hospitalized non-COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non-COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models' ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions.