Publications by authors named "P Sottile"

Mechanical ventilation (MV) is a necessary lifesaving intervention for patients with acute respiratory distress syndrome (ARDS) but it can cause ventilator-induced lung injury (VILI), which contributes to the high ARDS mortality rate (∼40%). Bedside determination of optimally lung-protective ventilation settings is challenging because the evolution of VILI is not immediately reflected in clinically available, patient-level, data. The goal of this work was therefore to test ventilation waveform-derived parameters that represent the degree of ongoing VILI and can serve as targets for ventilator adjustments.

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Study Objective: To examine the association between the neuromuscular blocking agent received (succinylcholine versus rocuronium) and the incidences of successful intubation on the first attempt and severe complications during tracheal intubation of critically ill adults in an emergency department (ED) or ICU.

Methods: We performed a secondary analysis of data from 2 multicenter randomized trials in critically ill adults undergoing tracheal intubation in an ED or ICU. Using a generalized linear mixed-effects model with prespecified baseline covariates, we examined the association between the neuromuscular blocking agent received (succinylcholine versus rocuronium) and the incidences of successful intubation on the first attempt (primary outcome) and severe complications during tracheal intubation (secondary outcome).

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Article Synopsis
  • In a study involving critically ill adults undergoing tracheal intubation, researchers compared preoxygenation methods: noninvasive ventilation versus oxygen mask.
  • The findings revealed that hypoxemia occurred significantly less in the noninvasive-ventilation group (9.1%) compared to the oxygen-mask group (18.5%).
  • Additionally, the incidence of cardiac arrest was lower with noninvasive ventilation (0.2%) compared to the oxygen-mask group (1.1%).
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Article Synopsis
  • Ventilator dyssynchrony (VD) can increase lung injury, and detecting its variability is complex, but machine learning offers potential solutions for automating detection in ventilator waveform data.
  • A systematic framework was developed to quantify features in ventilator signals, which allows for stratifying the severity of dyssynchronous breaths.
  • The study analyzed over 93,000 breaths, achieving a predictive accuracy of over 97% for identifying flow-limited VD breaths, and established a computational approach for understanding the severity and impact of VD in clinical settings.
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