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Computer-aided mechanical ventilation

Author: Brian Kendall WalshRonald BeckettRobert KacmarekJohn H ArnoldEllen A BeckerAll authors
Publisher: [Ann Arbor, Michigan] : Proquest LLC, 2016.
Dissertation: Thesis (Ph. D.) Rush University 2016
Edition/Format:   Thesis/dissertation : Thesis/dissertation : Manuscript : Microfiche   Archival Material : English
Summary:
Statement of the problem: The systematic implementation of evidence-based practice through the use of guidelines, checklists and protocols has been shown to mitigate the risks associated with MV, yet variation in practice remains prevalent. Recent advances in MV, physiologic monitoring, device-to-device communication, computer processing and software engineering have allowed for the development of an automated  Read more...
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Genre/Form: Academic Dissertation
Academic theses
Additional Physical Format: Reproduction of (manifestation):
Walsh, Brian Kendall.
Computer-aided mechanical ventilation.
2016
Material Type: Thesis/dissertation, Manuscript
Document Type: Book, Archival Material
All Authors / Contributors: Brian Kendall Walsh; Ronald Beckett; Robert Kacmarek; John H Arnold; Ellen A Becker; Douglas Kuperman; Rush University. College of Health Sciences,
OCLC Number: 1015347573
Notes: "Submitted to Rush University in partial fulfillment of the requirements for the degree of Doctor of Philosophy."
"A version of chapter 2 has been accepted for publication on 2/16/2016 by Respiratory Care"--Page 21.
Description: 1 microfiche (viii, 85 pages) : positive, illustrations ; 11 x 15 cm
Responsibility: by Brian Kendall Walsh.

Abstract:

Statement of the problem: The systematic implementation of evidence-based practice through the use of guidelines, checklists and protocols has been shown to mitigate the risks associated with MV, yet variation in practice remains prevalent. Recent advances in MV, physiologic monitoring, device-to-device communication, computer processing and software engineering have allowed for the development of an automated point-of-care access to real-time goal setting and practice variance identification. Our aim was to assess the utility of a computer-aided MV (CAMV) system that displays variances and scores the overall MV course. Methods: A retrospective categorization of the ventilation and oxygenation statuses of patients within our pediatric intensive care unit (PICU) over a 2 '/z years period utilizing 15 rule-based algorithms was initiated as a proof of concept. Goals were predetermined based on generally accepted values. All patient categories were calculated and presented as a percent of recording time. Following the feasibility study, a retrospective observational study (baseline), followed by two sequential interventions made over a 2-month period was conducted. Phase I comprised a survey of goals of MV by clinicians caring for patients being monitored by the CAMV system. Phase II intervention was the setting and monitoring of goals of MV with a web browser based data visualization system (T3). An outcome measurement tool was developed to score each MV course. The MV score (MVS) evaluated four outcomes: (1) acceptable ventilation, (2) acceptable oxygenation, (3) barotrauma free and (4) volutrauma-free states as a percent of recording time. Results: Pilot consisted of 222 patients. The Baseline phase evaluated 130 patients, Phase I enrolled 31 patients and Phase II enrolled 36 patients. There were no differences in demographic characteristics between cohorts. One hundred and seventy-one surveys were completed in Phase I. An increase in the use of T3 by 87% was observed in Phase II from Phase I. MVS improved by 8.4% in Phase I and 11.3% in Phase II from Baseline. The largest improvement was in the volutraumafree category. MVS was 9% higher on average in those who survived. Conclusion: The use of CAMV was associated with an improvement in MVS. Further research is needed to determine if improvements in MVS through a targeted, process-oriented intervention such as CAMV will lead to improved patient outcomes.

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