Literature DB >> 29337804

The Association Between Ventilator Dyssynchrony, Delivered Tidal Volume, and Sedation Using a Novel Automated Ventilator Dyssynchrony Detection Algorithm.

Peter D Sottile1, David Albers2, Carrie Higgins1, Jeffery Mckeehan1, Marc M Moss1.   

Abstract

OBJECTIVE: Ventilator dyssynchrony is potentially harmful to patients with or at risk for the acute respiratory distress syndrome. Automated detection of ventilator dyssynchrony from ventilator waveforms has been difficult. It is unclear if certain types of ventilator dyssynchrony deliver large tidal volumes and whether levels of sedation alter the frequency of ventilator dyssynchrony.
DESIGN: A prospective observational study.
SETTING: A university medical ICU. PATIENTS: Patients with or at risk for acute respiratory distress syndrome.
INTERVENTIONS: Continuous pressure-time, flow-time, and volume-time data were directly obtained from the ventilator. The level of sedation and the use of neuromuscular blockade was extracted from the medical record. Machine learning algorithms that incorporate clinical insight were developed and trained to detect four previously described and clinically relevant forms of ventilator dyssynchrony. The association between normalized tidal volume and ventilator dyssynchrony and the association between sedation and the frequency of ventilator dyssynchrony were determined.
MEASUREMENTS AND MAIN RESULTS: A total of 4.26 million breaths were recorded from 62 ventilated patients. Our algorithm detected three types of ventilator dyssynchrony with an area under the receiver operator curve of greater than 0.89. Ventilator dyssynchrony occurred in 34.4% (95% CI, 34.41-34.49%) of breaths. When compared with synchronous breaths, double-triggered and flow-limited breaths were more likely to deliver tidal volumes greater than 10 mL/kg (40% and 11% compared with 0.2%; p < 0.001 for both comparisons). Deep sedation reduced but did not eliminate the frequency of all ventilator dyssynchrony breaths (p < 0.05). Ventilator dyssynchrony was eliminated with neuromuscular blockade (p < 0.001).
CONCLUSION: We developed a computerized algorithm that accurately detects three types of ventilator dyssynchrony. Double-triggered and flow-limited breaths are associated with the frequent delivery of tidal volumes of greater than 10 mL/kg. Although ventilator dyssynchrony is reduced by deep sedation, potentially deleterious tidal volumes may still be delivered. However, neuromuscular blockade effectively eliminates ventilator dyssynchrony.

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Year:  2018        PMID: 29337804      PMCID: PMC5772880          DOI: 10.1097/CCM.0000000000002849

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


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4.  From Big Data to Artificial Intelligence: Harnessing Data Routinely Collected in the Process of Care.

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7.  Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data.

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Review 10.  Ventilator dyssynchrony - Detection, pathophysiology, and clinical relevance: A Narrative review.

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