Abstract
BACKGROUND: Driving pressure (ΔP) and mechanical power (MP) may be important mediators of lung injury in ARDS; however, there is little evidence for strategies directed at reducing these parameters. We applied predictive modeling to estimate the effects of modifying ventilator parameters on ΔP and MP.
METHODS: Two thousand six hundred twenty-two subjects with ARDS (Berlin criteria) from the Medical Information Mart for Intensive Care IV version 1.0 database admitted to the ICU at Beth Israel Deaconess Medical Center between 2008–2019 were included. Flexible confounding-adjusted regression models for time-varying data were fit to estimate the effects of adjusting PEEP and tidal volume (VT) on ΔP and adjusting VT and breathing frequency on MP.
RESULTS: Reduction in VT reduced ΔP and MP, with more pronounced effect on MP with lower compliance. Strategies reducing frequency consistently increased MP (when VT was adjusted to maintain consistent minute ventilation). Adjustment of PEEP yielded a U-shaped effect on ΔP.
CONCLUSIONS: This novel conditional modeling confirmed expected response patterns for ΔP, with the response to adjustments depending on subjects’ lung mechanics. Furthermore, a VT-driven approach should be favored over a breathing frequency-driven approach when aiming to reduce MP.
- ARDS
- driving pressure
- mechanical power
- power
- mechanical ventilation
- PEEP
- tidal volume
- modeling
Footnotes
- Correspondence: Elias Baedorf Kassis, Division of Pulmonary and Critical Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston MA 02115. E-mail: enbaedor{at}bidmc.harvard.edu
Drs Baedorf Kassis and Talmor disclose relationships with Hamilton Medical. At the time of this study, Dr Shahn was employed by IBM Research. The remaining authors have disclosed no conflicts of interest.
Dr Baedorf Kassis received the support of an appointed KL2 award from Harvard Catalyst | The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health [NIH] Award KL2 TR002542). Funding was also provided by the MIT-IBM Watson AI Lab and NIH grant R01 EB017205.
Supplementary material related to this paper is available at http://www.rcjournal.com.
Drs Lehman and Shahn are co-senior authors.
- Copyright © 2023 by Daedalus Enterprises
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