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© The Author(s). 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background

Intraoperative hypotension is associated with increased morbidity and mortality. Current treatment is mostly reactive. The Hypotension Prediction Index (HPI) algorithm is able to predict hypotension minutes before the blood pressure actually decreases. Internal and external validation of this algorithm has shown good sensitivity and specificity. We hypothesize that the use of this algorithm in combination with a personalized treatment protocol will reduce the time weighted average (TWA) in hypotension during surgery spent in hypotension intraoperatively.

Methods/design

We aim to include 100 adult patients undergoing non-cardiac surgery with an anticipated duration of more than 2 h, necessitating the use of an arterial line, and an intraoperatively targeted mean arterial pressure (MAP) of > 65 mmHg. This study is divided into two parts; in phase A baseline TWA data from 40 patients will be collected prospectively. A device (HemoSphere) with HPI software will be connected but fully covered. Phase B is designed as a single-center, randomized controlled trial were 60 patients will be randomized with computer-generated blocks of four, six or eight, with an allocation ratio of 1:1. In the intervention arm the HemoSphere with HPI will be used to guide treatment; in the control arm the HemoSphere with HPI software will be connected but fully covered. The primary outcome is the TWA in hypotension during surgery.

Discussion

The aim of this trial is to explore whether the use of a machine-learning algorithm intraoperatively can result in less hypotension. To test this, the treating anesthesiologist will need to change treatment behavior from reactive to proactive.

Trial registration

This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT03376347. The trial was submitted on 4 November 2017 and accepted for registration on 18 December 2017.

Details

Title
The use of a machine-learning algorithm that predicts hypotension during surgery in combination with personalized treatment guidance: study protocol for a randomized clinical trial
Author
Wijnberge, M. 1 ; Schenk, J. 2 ; Terwindt, L. E. 2 ; Mulder, M. P. 3 ; Hollmann, M. W. 2 ; Vlaar, A. P. 4 ; Veelo, D. P. 2 ; Geerts, B. F. 2 

 University of Amsterdam, Department of Anesthesiology, Amsterdam UMC, location Academic Medical Center, Amsterdam, The Netherlands (GRID:grid.7177.6) (ISNI:0000000084992262); University of Amsterdam, Department of Intensive Care Medicine, Amsterdam UMC, location Academic Medical Center, Amsterdam, The Netherlands (GRID:grid.7177.6) (ISNI:0000000084992262) 
 University of Amsterdam, Department of Anesthesiology, Amsterdam UMC, location Academic Medical Center, Amsterdam, The Netherlands (GRID:grid.7177.6) (ISNI:0000000084992262) 
 University of Amsterdam, Department of Anesthesiology, Amsterdam UMC, location Academic Medical Center, Amsterdam, The Netherlands (GRID:grid.7177.6) (ISNI:0000000084992262); University of Twente, Department of Technical Medicine, Enschede, The Netherlands (GRID:grid.6214.1) (ISNI:0000 0004 0399 8953) 
 University of Amsterdam, Department of Intensive Care Medicine, Amsterdam UMC, location Academic Medical Center, Amsterdam, The Netherlands (GRID:grid.7177.6) (ISNI:0000000084992262) 
Pages
582
Publication year
2019
Publication date
Dec 2019
Publisher
BioMed Central
e-ISSN
17456215
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2795377012
Copyright
© The Author(s). 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.