Evidence map›Paper›PMID 33951140›Full record

Trial reportAnesthesiology2021

Computer-assisted Individualized Hemodynamic Management Reduces Intraoperative Hypotension in Intermediate- and High-risk Surgery: A Randomized Controlled Trial.

Alexandre Joosten, Joseph Rinehart, Philippe Van der Linden, Brenton Alexander, Christophe Penna, Jacques De Montblanc, Maxime Cannesson, Jean-Louis Vincent, Eric Vicaut, Jacques Duranteau

Abstract readRandomized Controlled TrialEquivalence Trial
In one paragraph

Trial report in Anesthesiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  20. Goal-directed therapy: what is the goal again?Perioperative medicine (London, England) · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Alexandre JoostenORCID 0000-0002-5214-4589
Joseph Rinehart
Philippe Van der Linden
Brenton Alexander
Christophe Penna
Jacques De Montblanc
Maxime Cannesson
Jean-Louis Vincent
Eric Vicaut
Jacques Duranteau

Funding

University of California Health Participation in the National COVID Cohort Collaborative (N3C)UL1TR001414 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI COOPER, DAN M, VILAIN, ERIC J. · 2015 to 2023
$35.1M
UC BRAID Center for Accelerated Innovation COVID-19 diagnostic project developmentU54HL119893 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PALAZZOLO, MICHAEL J · 2013 to 2020
$18.3M
Machine Learning of Physiological Waveforms and Electronic Health Record Data to Predict, Diagnose, and Treat Hemodynamic Instability in Surgical PatientsR01HL144692 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CANNESSON, MAXIME · 2019 to 2023
$3.5M
Biomedical Informatics Tools for Applied Perioperative PhysiologyR01EB029751 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CANNESSON, MAXIME · 2020 to 2023
$2.5M
NCATS NIH HHS UL1 TR001414NHLBI NIH HHS R01 HL144692NHLBI NIH HHS U54 HL119893NIBIB NIH HHS R01 EB029751
6 · The paper itself

Abstract

backgroundIndividualized hemodynamic management during surgery relies on accurate titration of vasopressors and fluids. In this context, computer systems have been developed to assist anesthesia providers in delivering these interventions. This study tested the hypothesis that computer-assisted individualized hemodynamic management could reduce intraoperative hypotension in patients undergoing intermediate- to high-risk surgery.

methodsThis single-center, parallel, two-arm, prospective randomized controlled single blinded superiority study included 38 patients undergoing abdominal or orthopedic surgery. All included patients had a radial arterial catheter inserted after anesthesia induction and connected to an uncalibrated pulse contour monitoring device. In the manually adjusted goal-directed therapy group (N = 19), the individualized hemodynamic management consisted of manual titration of norepinephrine infusion to maintain mean arterial pressure within 10% of the patient's baseline value, and mini-fluid challenges to maximize the stroke volume index. In the computer-assisted group (N = 19), the same approach was applied using a closed-loop system for norepinephrine adjustments and a decision-support system for the infusion of mini-fluid challenges (100 ml). The primary outcome was intraoperative hypotension defined as the percentage of intraoperative case time patients spent with a mean arterial pressure of less than 90% of the patient's baseline value, measured during the preoperative screening. Secondary outcome was the incidence of minor postoperative complications.

resultsAll patients were included in the analysis. Intraoperative hypotension was 1.2% [0.4 to 2.0%] (median [25th to 75th] percentiles) in the computer-assisted group compared to 21.5% [14.5 to 31.8%] in the manually adjusted goal-directed therapy group (difference, -21.1 [95% CI, -15.9 to -27.6%]; P < 0.001). The incidence of minor postoperative complications was not different between groups (42 vs. 58%; P = 0.330). Mean stroke volume index and cardiac index were both significantly higher in the computer-assisted group than in the manually adjusted goal-directed therapy group (P < 0.001).

conclusionsIn patients having intermediate- to high-risk surgery, computer-assisted individualized hemodynamic management significantly reduces intraoperative hypotension compared to a manually controlled goal-directed approach. EDITOR’S PERSPECTIVE:

Indexed as

HemodynamicsHypotensionIntraoperative ComplicationsSurgical Procedures, OperativeTherapy, Computer-AssistedFemaleHumansMaleMiddle AgedMonitoring, IntraoperativeProspective StudiesSingle-Blind Method

Identifiers

PMID33951140
PMCPMC8277754

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.