Recruitment manoeuvres for adults with acute respiratory distress syndrome receiving mechanical ventilation

Hodgson, Carol; Goligher, Ewan C.; Young, Meredith; Keating, Jennifer Lyn; Holland, Anne E; Romero, Lorena; Bradley, Scott J.; Tuxen, David V.

doi:10.1002/14651858.cd006667.pub3

Cited by 52 publications

(41 citation statements)

References 49 publications

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“…Lung recruitment maneuvers do not significantly reduce mortality but may improve oxygenation and shorten the length of hospital stay in ARDS patients. 42 Overall, recruitment maneuvers are not supported by high-quality evidence, 43 and caution should be exercised when using it because it can be irritating, incite coughing, and generate aerosols.…”

Section: Ventilation Managementmentioning

confidence: 99%

Intubation and Ventilation amid the COVID-19 Outbreak

et al. 2020

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The COVID-19 outbreak has led to 80,409 diagnosed cases and 3,012 deaths in mainland China based on the data released on March 4, 2020. Approximately 3.2% of patients with COVID-19 required intubation and invasive ventilation at some point in the disease course. Providing best practices regarding intubation and ventilation for an overwhelming number of patients with COVID-19 amid an enhanced risk of cross-infection is a daunting undertaking. The authors presented the experience of caring for the critically ill patients with COVID-19 in Wuhan. It is extremely important to follow strict self-protection precautions. Timely, but not premature, intubation is crucial to counter a progressively enlarging oxygen debt despite high-flow oxygen therapy and bilevel positive airway pressure ventilation. Thorough preparation, satisfactory preoxygenation, modified rapid sequence induction, and rapid intubation using a video laryngoscope are widely used intubation strategies in Wuhan. Lung-protective ventilation, prone position ventilation, and adequate sedation and analgesia are essential components of ventilation management.

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Section: Ventilation Managementmentioning

confidence: 99%

Intubation and Ventilation amid the COVID-19 Outbreak

et al. 2020

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“…In accordance with medical publications, efficacy and effectiveness will be assessed via a systematic review, with predefined inclusion and exclusion criteria. Relevant reviews without these inclusion/exclusion criteria include [11,26,27]. Prior systematic reviews assessed extensively whether "protocolised versus non-protocolised" [11] and "automated versus non-automated" [13] weaning reduced the duration of mechanical ventilation.…”

Section: Clinical Efficacy and Effectivenessmentioning

confidence: 99%

The efficacy and effectiveness of machine learning for weaning in mechanically ventilated patients at the intensive care unit: a systematic review

et al. 2018

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Weaning from mechanical ventilation in the intensive care unit (ICU) is a complex clinical problem and relevant for future organ engineering. Prolonged mechanical ventilation (MV) leads to a range of medical complications that increases length of stay and costs as well as contributes to morbidity and even mortality and long-term quality of life. The need to reduce MV is both clinical and economical. Artificial intelligence or machine learning (ML) methods are promising opportunities to positively influence patient outcomes. ML methods have been proposed to enhance clinical decisions processes by using the large amount of digital information generated in the ICU setting. There is a particular interest in empirical methods (such as ML) to improve management of "difficult-to-wean" patients, due to the associated costs and adverse events associated with this population. A systematic literature search was performed using the OVID, IEEEXplore, PubMed, and Web of Science databases. All publications that included (1) the application of ML to weaning from MV in the ICU and (2) a clinical outcome measurement were reviewed. A checklist to assess the study quality of medical ML publications was modified to suit the critical assessment of ML in MV weaning literature. The systematic search identified nine studies that used ML for weaning management from MV in critical care. The weaning management application areas included (1) prediction of successful spontaneous breathing trials (SBTs), (2) prediction of successful extubation, (3) prediction of arterial blood gases, and (4) ventilator setting and oxygenation-adjustment advisory systems. Seven of the nine studies scored seven out of eight on the quality index. The remaining two of the nine studies scored one out of eight on the quality index. This scoring may, in part, be explained by the publications' focus on technical novelty, and therefore focusing on issues most important to a technical audience, instead of issues most important for a systematic medical review. This review showed that only a limited number of studies have started to assess the efficacy and effectiveness of ML for MV in the ICU. However, ML has the potential to be applied to the prediction of SBT failure, extubation failure, and blood gases, and also the adjustment of ventilator and oxygenation settings. The available databases for the development of ML in this clinical area may still be inadequate. None of the reviewed studies reported on the procedure, treatment, or sedation strategy undergone by patients. Such information is unlikely to be required in a technical publication but is potentially vital to the development ML techniques that are sufficiently robust to meet the needs of the "difficult-to-wean" patient population.

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“…Additionally, of the studies including recruitment maneuvers that do demonstrate improvements in mortality, often other recruitment strategies known to improve outcomes (eg, high PEEP, prone positioning) were being used as well. 20 Another important point to highlight is that certain patients seem to respond to lung recruitment while others do not, which adds to the complexity of this process and may be why consistent clinical evidence remains elusive. 21 The results of ongoing large, multi-center randomized control studies 22,23 may shed more light on this subject in the future.…”

Section: See the Original Study On Page 1014mentioning

confidence: 99%