Minimal Lung Mechanics Basis-functions for a Mechanical Ventilation Virtual Patient

Q.H. Sun*, J.G. Chase, C. Zhou, M.H. Tawhai, J.L. Knopp, K. Moller, S.J. Heines, D.C. Bergmans, G.M. Shaw

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Abstract

Mechanical ventilation (MV) is used in the intensive care unit (ICU) to treat patients with respiratory failure. However, MV settings are not standardized due to significant inter- and intra- patient variability in response to care, leading to variability in care, outcome, and cost. There is thus a need to personalize MV. This research extends a single compartment lung mechanics model with physiologically relevant basis functions, to identify patient-specific lung mechanics and predict response to changes in MV care. The nonlinear evolution of pulmonary elastance as positive-end-expiratory pressure (PEEP) changes is captured by a physiologically relevant, simplified compensatory equation as a function of PEEP and pressure identification error at the baseline PEEP level. It allows both patient-specific and general prediction of lung elastance of higher PEEP. The prediction outcome is validated with data from two volume-controlled ventilation (VCV) trials and one pressure-controlled ventilation (PCV) trial, where the biggest PEEP prediction interval is a clinically unrealistic 20cmH(2)O, comprising 210 prediction cases over 36 patients (22 VCV; 14 PCV). Predicted absolute peak inspiratory pressure (PIP) errors are within 1.0cmH(2)O and 3.3cmH(2)O for 90% cases in the two VCV trials, while predicted peak inspiratory tidal volume (PIV) errors are within 0.073L for 85% cases in studied PCV trial. The model presented provides a highly accurate, predictive virtual patient model across multiple MV modes and delivery methods, and over clinically unrealistically large changes. Low computational cost, and fast, easy parameterization enable model-based, predictive decision support in real-time to safely personalize and optimize MV care. Copyright (C) 2021 The Authors.
Original languageEnglish
Title of host publicationIFAC PAPERSONLINE
PublisherElsevier
Pages127-132
Number of pages6
Volume54
Edition15
DOIs
Publication statusPublished - 2021
Event11th IFAC Symposium on Biological and Medical Systems - Ghent, Belgium
Duration: 19 Sep 202122 Sep 2021
Conference number: 11
https://bms2021.ugent.be/

Symposium

Symposium11th IFAC Symposium on Biological and Medical Systems
Country/TerritoryBelgium
CityGhent
Period19/09/2122/09/21
Internet address

Keywords

  • Mechanical ventilation
  • PEEP
  • Respiratory mechanics
  • Elastance
  • Prediction
  • VILI
  • Basis function
  • System identification
  • Virtual patient
  • PRESSURE
  • MODEL

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