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

M Wijnberge,M W Hollmann,L E Terwindt,M P Mulder,J Schenk,B F Geerts,D P Veelo,A P Vlaar

doi:10.1186/s13063-019-3637-4

Abstract

BackgroundIntraoperative 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/designWe 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.DiscussionThe 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 registrationThis 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.

Highlights

Intraoperative hypotension is associated with increased morbidity and mortality
Intraoperative hypotension is usually caused by anesthetics, pre-operative use of medication, existing comorbidities or by the surgery itself [3]
Clinical cohort studies and one randomized controlled clinical trial have shown intraoperative hypotension to be associated with postoperative complications such as myocardial ischemia, renal insufficiency and increased mortality [5,6,7,8,9,10,11]

Summary

Introduction

Intraoperative hypotension is associated with increased morbidity and mortality. Current treatment is mostly reactive. 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. Intraoperative hypotension is usually caused by anesthetics, pre-operative use of medication, existing comorbidities or by the surgery itself [3]. Since both pressure and flow are required to deliver oxygen to the tissues, hypotension can negatively affect organ function [4]. Clinical cohort studies and one randomized controlled clinical trial have shown intraoperative hypotension to be associated with postoperative complications such as myocardial ischemia, renal insufficiency and increased mortality [5,6,7,8,9,10,11]. The time-weighted average (TWA) combines the time and depth of hypotension [13, 14]

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