Increasing Cardiovascular Data Sampling Frequency and Referencing It to Baseline Improve Hemorrhage Detection.

animal model hemodynamic monitoring hemorrhage machine learning predictive models

Journal

Critical care explorations
ISSN: 2639-8028
Titre abrégé: Crit Care Explor
Pays: United States
ID NLM: 101746347

Informations de publication

Date de publication:
Oct 2019
Historique:
entrez: 14 3 2020
pubmed: 14 3 2020
medline: 14 3 2020
Statut: epublish

Résumé

We hypothesize that knowledge of a stable personalized baseline state and increased data sampling frequency would markedly improve the ability to detect progressive hypovolemia during hemorrhage earlier and with a lower false positive rate than when using less granular data. Prospective temporal challenge. Large animal research laboratory, University Medical Center. Fifty-one anesthetized Yorkshire pigs. Pigs were instrumented with arterial, pulmonary arterial, and central venous catheters and allowed to stabilize for 30 minutes then bled at a constant rate of either 5 mL·min Data during the stabilization period served as baseline. Hemodynamic variables collected at 250 Hz were used to create predictive models of "bleeding" using featurized beat-to-beat and waveform data and compared with models using mean unfeaturized hemodynamic variables averaged over 1-minute as simple hemodynamic metrics using random forest classifiers to identify bleeding with or without baseline data. The robustness of the prediction was evaluated in a leave-one-pig-out cross-validation. Predictive performance of models was compared by their activity monitoring operating characteristic and receiver operating characteristic profiles. Primary hemodynamic threshold data poorly identified bleed onset unless very stable initial baseline reference data were available. When referenced to baseline, bleed detection at a false positive rates of 10 Knowledge of personal stable baseline data allows for early detection of new-onset bleeding, whereas if no personal baseline exists increasing sampling frequency of hemodynamic monitoring data improves bleeding detection earlier and with lower false positive rate.

Identifiants

pubmed: 32166238
doi: 10.1097/CCE.0000000000000058
pmc: PMC7063895
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e0058

Subventions

Organisme : NCATS NIH HHS
ID : KL2 TR002381
Pays : United States
Organisme : NHLBI NIH HHS
ID : T32 HL007820
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002378
Pays : United States

Informations de copyright

Copyright © 2019 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.

Déclaration de conflit d'intérêts

The authors have disclosed that they do not have any potential conflicts of interest.

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Auteurs

Anthony Wertz (A)

Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA.

Andre L Holder (AL)

Cardiopulmonary Research Laboratory, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA.

Mathieu Guillame-Bert (M)

Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA.

Gilles Clermont (G)

Cardiopulmonary Research Laboratory, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA.

Artur Dubrawski (A)

Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA.

Michael R Pinsky (MR)

Cardiopulmonary Research Laboratory, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA.

Classifications MeSH