Extubation Advisor: Implementation and Evaluation of A Novel Extubation Clinical Decision Support Tool

Brett N Hryciw; Natasha Hudek; Jamie C Brehaut; Christophe Herry; Nathan Scales; Emma Lee; Aimee J Sarti; Karen E A Burns; Andrew J E Seely

doi:10.1177/08850666241291524

Extubation Advisor: Implementation and Evaluation of A Novel Extubation Clinical Decision Support Tool

J Intensive Care Med. 2024 Oct 24:8850666241291524. doi: 10.1177/08850666241291524. Online ahead of print.

Authors

Brett N Hryciw¹, Natasha Hudek², Jamie C Brehaut^{2

3}, Christophe Herry², Nathan Scales², Emma Lee⁴, Aimee J Sarti^{1

2}, Karen E A Burns^{5

6

7}, Andrew J E Seely^{1

2

8}

Affiliations

¹ Division of Critical Care, Department of Medicine, University of Ottawa, Ottawa, Canada.
² Clinical Epidemiology Program, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Canada.
³ School of Epidemiology & Public Health, University of Ottawa, Ottawa, Canada.
⁴ Department of Respiratory Therapy, They Ottawa Hospital, Ottawa, Canada.
⁵ Department of Medicine, University of Toronto, Toronto, Canada.
⁶ Interdepartmental Division of Critical Care, University of Toronto, Toronto, Canada.
⁷ Li Ka Shing Knowledge Institute, Unity Health Toronto St. Michael's Hospital, Toronto, Canada.
⁸ Division of Thoracic Surgery, Department of Surgery, The Ottawa Hospital, Ottawa, Canada.

PMID: 39444331
DOI: 10.1177/08850666241291524

Abstract

Importance: Extubation Advisor (EA) is a novel software tool that generates a synoptic report for each Spontaneous Breathing Trial (SBT) conducted to inform extubation decision-making.

Objectives: To assess bedside EA implementation, perceptions of utility, and identify barriers and facilitators of use.

Design, setting and participants: We conducted a phase I mixed-methods interventional study in three mixed intensive care unit (ICUs) in two academic hospitals. We interviewed critical care physicians (MDs) and respiratory therapists (RTs) regarding user-centered design principles and usability.

Analysis: We evaluated our ability to consent participants (feasibility threshold 50%), capture complete data (threshold 90%), generate and review EA reports in real-time (thresholds 75% and 80%, respectively), and MD perception of tool usefulness (6-point Likert scale). We analyzed interview transcripts using inductive coding to identify facilitators and barriers to EA implementation and perceived benefit of tool use.

Results: We enrolled 31 patients who underwent 70 SBTs. Although consent rates [31/31 (100%], complete data capture [68/68 (100%)], and EA report generation [68/70 (97.1%)] exceeded feasibility thresholds, reports were reviewed by MDs for [55/70 (78.6%)] SBTs. Mean MD usefulness score was 4.0/6. Based on feedback obtained from 36 interviews (15 MDs, 21 RTs), we revised the EA report twice and identified facilitators (ability to track patient progress, enhance extubation decision-making, and provide support in resource-limited settings) and barriers (resource constraints, need for education) to tool implementation. Half of respondents (9 MDs, 9 RTs; combined 50%) perceived definite or potential benefit to EA tool use.

Conclusion: This is the first study of a waveform-based variability-derived, predictive clinical decision support tool evaluated in adult ICUs. Our findings support the feasibility of integrating the EA tool into bedside workflow. Clinical trials are needed to assess the utility of the EA tool in practice and its impact on extubation decision-making and outcomes.

Trial registration: NCT04708509.

Keywords: artificial intelligence; decision-making; extubation; machine learning; weaning.

Associated data

ClinicalTrials.gov/NCT04708509