Evidence map›Paper›PMID 30539349›Full record

ArticleJournal of clinical monitoring and computing2019

Closed-loop vasopressor control: in-silico study of robustness against pharmacodynamic variability.

Joseph Rinehart, Alexandre Joosten, Michael Ma, Michael-David Calderon, Maxime Cannesson

Abstract read
In one paragraph

Article in Journal of clinical monitoring and computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–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

15 citing papers in PubMed.

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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

5 authors.

Joseph RinehartDepartment of Anesthesiology & Perioperative Care, University of California Irvine, 101 The City Drive South, Orange, CA, 92868, USA. jrinehar@uci.edu.ORCID http://orcid.org/0000-0003-2975-196X
Alexandre JoostenDepartment of Anesthesiology, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium.
Michael MaDepartment of Anesthesiology & Perioperative Care, University of California Irvine, 101 The City Drive South, Orange, CA, 92868, USA.
Michael-David CalderonDepartment of Anesthesiology & Perioperative Care, University of California Irvine, 101 The City Drive South, Orange, CA, 92868, USA.
Maxime CannessonDepartment of Anesthesiology and Perioperative Medicine, Davide Geffen School of Medicine, University of California Los Angeles UCLA, Los Angeles, CA, USA.

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
NHLBI NIH HHS R01 HL144692
6 · The paper itself

Abstract

Initial feasibility of a novel closed-loop controller created by our group for closed-loop control of vasopressor infusions has been previously described. In clinical practice, vasopressor potency may be affected by a variety of factors including other pharmacologic agents, organ dysfunction, and vasoplegic states. The purpose of this study was therefore to evaluate the effectiveness of our controller in the face of large variations in drug potency, where 'effective' was defined as convergence on target pressure over time. We hypothesized that the controller would remain effective in the face up to a tenfold variability in drug response. To perform the robustness study, our physiologic simulator was used to create randomized simulated septic patients. 250 simulated patients were managed by the closed-loop in each of 7 norepinephrine responsiveness conditions: 0.1 ×, 0.2 ×, 0.5 ×, 1 ×, 2 ×, 5 ×, and 10 × expected population response to drug dose. Controller performance was evaluated for each level of norepinephrine response using Varvel's criteria as well as time-out-of-target. Median performance error and median absolute performance error were less than 5% in all response levels. Wobble was below 3% and divergence remained negative (i.e. the controller tended to converge towards the target over time) in all norepinephrine response levels, but at the highest response level of 10 × the value approached zero, suggesting the controller may be approaching instability. Response levels of 0.1 × and 0.2 × exhibited significantly higher time-out-of-target in the lower ranges (p < 0.001) compared to the 1 × response level as the controller was slower to correct the initial hypotension. In this simulation study, the closed-loop vasopressor controller remained effective in simulated patients exhibiting 0.1 to 10 × the expected population drug response.

Indexed as

Computer SimulationAlgorithmsBlood PressureHumansHypotensionMonte Carlo MethodNorepinephrineRandom AllocationSepsisSoftwareVasoconstrictor AgentsNorepinephrineVasoconstrictor AgentsClosed-loop controlHemodynamic managementVasopressor

Identifiers

PMID30539349
PMCPMC6816023

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Registered trials

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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.