Evidence map›Paper›PMID 31555916›Full record

ReviewJournal of anesthesia2020

Automated systems for perioperative goal-directed hemodynamic therapy.

Sean Coeckelenbergh, Cedrick Zaouter, Brenton Alexander, Maxime Cannesson, Joseph Rinehart, Jacques Duranteau, Philippe Van der Linden, Alexandre Joosten

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of anesthesia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.3field-weighted citation impact, top 8% of its field
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

11 citing papers in PubMed, 23 citations in OpenAlex.

  1. Trial
  2. Observational
  3. Article
  4. Review
  5. Closing the loop: automation in anesthesiology is coming.Journal of clinical monitoring and computing · 2024
    Article
  6. Article
  7. Article
  8. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 6 institutions in 3 countries.

Sean CoeckelenberghDepartment of Anesthesiology, Erasme University Hospital, University Libre de Bruxelles, Brussels, Belgium.
Cedrick ZaouterDepartment of Cardiac Anaesthesia and Intensive Care, CHU Bordeaux, Service d'Anesthésie-Réanimation II, 33000, Bordeaux, France.
Brenton AlexanderDepartment of Anesthesiology Department of Anesthesiology, University of California, San Diego, CA, USA.
Maxime CannessonDepartment of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, 757 Westwood Plaza, Los Angeles, CA, USA.
Joseph RinehartDepartment of Anesthesiology and Perioperative Care, University of California Irvine (UCI), 101 the City Drive South, Orange, USA.
Jacques DuranteauDepartment of Anesthesiology and Intensive Care, Hôpitaux Universitaires Paris-Sud, Université Paris-Sud, Université Paris-Saclay, Hôpital De Bicêtre, Assistance Publique Hôpitaux de Paris (AP-HP), 94270, Le Kremlin-Bicêtre, France.
Philippe Van der LindenDepartment of Anesthesiology, Brugmann Hospital, Université Libre de Bruxelles, Brussels, Belgium.
Alexandre JoostenDepartment of Anesthesiology and Intensive Care, Hôpitaux Universitaires Paris-Sud, Université Paris-Sud, Université Paris-Saclay, Hôpital De Bicêtre, Assistance Publique Hôpitaux de Paris (AP-HP), 94270, Le Kremlin-Bicêtre, France. joosten-alexandre@hotmail.com.ORCID 0000-0002-5214-4589
Université Libre de Bruxelles · BEUniversité Paris-Sud · FRCentre Hospitalier Universitaire de Bordeaux · FRUniversity of California, Irvine · USUniversity of California, Los Angeles · USUniversity of California San Diego · US

Funding

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
6 · The paper itself

Abstract

Perioperative goal-directed hemodynamic therapy (GDHT) has evolved from invasive "supra-physiological" maximization of oxygen delivery to minimally or even noninvasively guided automated stroke volume optimization. Over the past four decades, investigators have simultaneously developed novel monitors, updated strategies, and automated technologies to improve GDHT. Decision support technology, which proposes an intervention based on the patient's real time physiologic status, was an important step towards automation. Closed-loop systems have now been created to both increase GDHT compliance and decrease physician workload. These automated systems offer an elegant approach to optimize cardiac output and end-organ perfusion during the perioperative period. Most notably, automated preload optimization guided by dynamic indicators of fluid responsiveness has shown its feasibility, safety, and impact. Making the leap into fully automated GDHT has been accomplished on a small scale, but there are considerable challenges that must be surpassed before integrating all hemodynamic components into an automated system during general anesthesia. In this review, we will discuss the evolution and potential future of automated GDHT during the perioperative period.

Indexed as

Fluid TherapyGoalsCardiac OutputHemodynamicsHumansStroke VolumeAutomationClosed-loopFluid therapyIntraoperative monitoringVasoconstrictor agents

Identifiers

PMID31555916
OpenAlexW2975406323

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.