artificial intelligence
decision-making
extubation
machine learning
weaning
Journal
Journal of intensive care medicine
ISSN: 1525-1489
Titre abrégé: J Intensive Care Med
Pays: United States
ID NLM: 8610344
Informations de publication
Date de publication:
24 Oct 2024
24 Oct 2024
Historique:
medline:
24
10
2024
pubmed:
24
10
2024
entrez:
24
10
2024
Statut:
aheadofprint
Résumé
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. To assess bedside EA implementation, perceptions of utility, and identify barriers and facilitators of use. 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. 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. 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. 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. NCT04708509.
Identifiants
pubmed: 39444331
doi: 10.1177/08850666241291524
doi:
Banques de données
ClinicalTrials.gov
['NCT04708509']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8850666241291524Déclaration de conflit d'intérêts
Declaration of Conflicting InterestsDr A Seely and Dr C Herry are named on a patent related to Extubation Advisor technology described in this paper. Dr Seely is founder and board chairman of Therapeutic Monitoring Systems, a company founded to help research, develop, and commercialize clinical decision support tools based on variability analysis, including Extubation Advisor.