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

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.

SUBMITTER: Sottile PD

PROVIDER: S-EPMC5772880 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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

Sottile Peter D PD Albers David D Higgins Carrie C Mckeehan Jeffery J Moss Marc M MM

Critical care medicine 20180201 2

<h4>Objective</h4>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.<h4>Design</h4>A prospective observational study.<h4>Setting</h4>A university medical ICU.<h4>Patients</h ...[more]

PMID: 29337804

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Project description:Tidal volume (VT) has been considered the main determinant of ventilator-induced lung injury (VILI). Recently, experimental studies have suggested that mechanical power transferred from the ventilator to the lungs is the promoter of VILI. We hypothesized that, as long as mechanical power is kept below a safe threshold, high VT should not be injurious. The present study aimed to investigate the impact of different VT levels and respiratory rates (RR) on lung function, diffuse alveolar damage (DAD), alveolar ultrastructure, and expression of genes related to inflammation [interleukin (IL)-6], alveolar stretch (amphiregulin), epithelial [club cell secretory protein (CC)16] and endothelial [intercellular adhesion molecule (ICAM)-1] cell injury, and extracellular matrix damage [syndecan-1, decorin, and metalloproteinase (MMP)-9] in experimental acute respiratory distress syndrome (ARDS) under low-power mechanical ventilation. Twenty-eight Wistar rats received Escherichia coli lipopolysaccharide intratracheally. After 24 h, 21 animals were randomly assigned to ventilation (2 h) with low mechanical power at three different VT levels (n = 7/group): (1) VT = 6 mL/kg and RR adjusted to normocapnia; (2) VT = 13 mL/kg; and 3) VT = 22 mL/kg. In the second and third groups, RR was adjusted to yield low mechanical power comparable to that of the first group. Mechanical power was calculated as [(?[Formula: see text]/Est,L)/2]× RR (?P,L = transpulmonary driving pressure, Est,L = static lung elastance). Seven rats were not mechanically ventilated (NV) and were used for molecular biology analysis. Mechanical power was comparable among groups, while VT gradually increased. ?P,L and mechanical energy were higher in VT = 22 mL/kg than VT = 6 mL/kg and VT = 13 mL/kg (p < 0.001 for both). Accordingly, DAD score increased in VT = 22 mL/kg compared to VT = 6 mL/kg and VT = 13 mL/kg [23(18.5-24.75) vs. 16(12-17.75) and 16(13.25-18), p < 0.05, respectively]. VT = 22 mL/kg was associated with higher IL-6, amphiregulin, CC16, MMP-9, and syndecan-1 mRNA expression and lower decorin expression than VT = 6 mL/kg. Multiple linear regression analyses indicated that VT was able to predict changes in IL-6 and CC16, whereas ?P,L predicted pHa, oxygenation, amphiregulin, and syndecan-1 expression. In the model of ARDS used herein, even at low mechanical power, high VT resulted in VILI. VT control seems to be more important than RR control to mitigate VILI.

Project description:BackgroundIn this study we implemented and developed state-of-the-art machine learning (ML) and natural language processing (NLP) technologies and built a computerized algorithm for medication reconciliation. Our specific aims are: (1) to develop a computerized algorithm for medication discrepancy detection between patients' discharge prescriptions (structured data) and medications documented in free-text clinical notes (unstructured data); and (2) to assess the performance of the algorithm on real-world medication reconciliation data.MethodsWe collected clinical notes and discharge prescription lists for all 271 patients enrolled in the Complex Care Medical Home Program at Cincinnati Children's Hospital Medical Center between 1/1/2010 and 12/31/2013. A double-annotated, gold-standard set of medication reconciliation data was created for this collection. We then developed a hybrid algorithm consisting of three processes: (1) a ML algorithm to identify medication entities from clinical notes, (2) a rule-based method to link medication names with their attributes, and (3) a NLP-based, hybrid approach to match medications with structured prescriptions in order to detect medication discrepancies. The performance was validated on the gold-standard medication reconciliation data, where precision (P), recall (R), F-value (F) and workload were assessed.ResultsThe hybrid algorithm achieved 95.0%/91.6%/93.3% of P/R/F on medication entity detection and 98.7%/99.4%/99.1% of P/R/F on attribute linkage. The medication matching achieved 92.4%/90.7%/91.5% (P/R/F) on identifying matched medications in the gold-standard and 88.6%/82.5%/85.5% (P/R/F) on discrepant medications. By combining all processes, the algorithm achieved 92.4%/90.7%/91.5% (P/R/F) and 71.5%/65.2%/68.2% (P/R/F) on identifying the matched and the discrepant medications, respectively. The error analysis on algorithm outputs identified challenges to be addressed in order to improve medication discrepancy detection.ConclusionBy leveraging ML and NLP technologies, an end-to-end, computerized algorithm achieves promising outcome in reconciling medications between clinical notes and discharge prescriptions.

Dataset Information

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

Publications

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

Similar Datasets

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