Evidence map›Paper›PMID 36517635›Full record

ArticleThe ultrasound journal2022

Measuring the accuracy of cardiac output using POCUS: the introduction of artificial intelligence into routine care.

Faisal Shaikh, Jon-Emile Kenny, Omar Awan, Daniela Markovic, Oren Friedman, Tao He, Sidharth Singh, Peter Yan, Nida Qadir, Igor Barjaktarevic

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The ultrasound journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
5.9field-weighted citation impact, top 3% 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

19 citing papers in PubMed, 32 citations in OpenAlex.

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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

10 authors at 5 institutions in 2 countries.

Faisal ShaikhDivision of Interventional Pulmonology, Beth Israel Medical Center and Massachusetts General Hospital, Boston, MA, USA.
Jon-Emile KennyHealth Sciences North Research Institute, Sudbury, ON, Canada.
Omar AwanDivision of Pulmonary and Critical Care, Washington DC VA Medical Center, Washington, DC, USA.
Daniela MarkovicDepartment of Medicine Statistics, David Geffen School of Medicine at University of California, Los Angeles, CA, USA.
Oren FriedmanDivision of Cardiothoracic Surgery, Cedars Sinai Medical Center, Los Angeles, CA, USA.
Tao HeDivision of Pulmonary and Critical Care, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Sidharth SinghDivision of Cardiothoracic Surgery, Cedars Sinai Medical Center, Los Angeles, CA, USA.
Peter YanDivision of Pulmonary and Critical Care Medicine, David Geffen School of Medicine, University of California Los Angeles, 10833 Le Conte Avenue, CHS Building, 43118, Los Angeles, CA, 90095, USA. peterdongyan@gmail.com.ORCID http://orcid.org/0000-0002-6024-8548
Nida QadirDivision of Pulmonary and Critical Care, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Igor BarjaktarevicDivision of Pulmonary and Critical Care, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
University of California, Los Angeles · USCedars-Sinai Medical Center · USBeth Israel Deaconess Hospital · USHealth Sciences North · CAWashington DC VA Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundShock management requires quick and reliable means to monitor the hemodynamic effects of fluid resuscitation. Point-of-care ultrasound (POCUS) is a relatively quick and non-invasive imaging technique capable of capturing cardiac output (CO) variations in acute settings. However, POCUS is plagued by variable operator skill and interpretation. Artificial intelligence may assist healthcare professionals obtain more objective and precise measurements during ultrasound imaging, thus increasing usability among users with varying experience. In this feasibility study, we compared the performance of novice POCUS users in measuring CO with manual techniques to a novel automation-assisted technique that provides real-time feedback to correct image acquisition for optimal aortic outflow velocity measurement.

methods28 junior critical care trainees with limited experience in POCUS performed manual and automation-assisted CO measurements on a single healthy volunteer. CO measurements were obtained using left ventricular outflow tract (LVOT) velocity time integral (VTI) and LVOT diameter. Measurements obtained by study subjects were compared to those taken by board-certified echocardiographers. Comparative analyses were performed using Spearman's rank correlation and Bland-Altman matched-pairs analysis.

resultsAdequate image acquisition was 100% feasible. The correlation between manual and automated VTI values was not significant (p = 0.11) and means from both groups underestimated the mean values obtained by board-certified echocardiographers. Automated measurements of VTI in the trainee cohort were found to have more reproducibility, narrower measurement range (6.2 vs. 10.3 cm), and reduced standard deviation (1.98 vs. 2.33 cm) compared to manual measurements. The coefficient of variation across raters was 11.5%, 13.6% and 15.4% for board-certified echocardiographers, automated, and manual VTI tracing, respectively.

conclusionsOur study demonstrates that novel automation-assisted VTI is feasible and can decrease variability while increasing precision in CO measurement. These results support the use of artificial intelligence-augmented image acquisition in routine critical care ultrasound and may have a role for evaluating the response of CO to hemodynamic interventions. Further investigations into artificial intelligence-assisted ultrasound systems in clinical settings are warranted.

Indexed as

Artificial intelligenceCardiac outputHemodynamic monitoringPOCUSPoint-of-care ultrasoundVelocity time integralVTI

Identifiers

PMID36517635
PMCPMC9751239
OpenAlexW4311442075

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.