Evidence map›Paper›PMID 23592319›Full record

ArticleAmerican journal of perinatology2014

Accurate automated apnea analysis in preterm infants.

Brooke D Vergales, Alix O Paget-Brown, Hoshik Lee, Lauren E Guin, Terri J Smoot, Craig G Rusin, Matthew T Clark, John B Delos, Karen D Fairchild, Douglas E Lake and 2 more

Open access · greenAbstract read
In one paragraph

Article in American journal of perinatology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

37 citing papers in PubMed, 60 citations in OpenAlex.

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  12. The effect of sensory stimulation on apnea of prematurity.Journal of Taibah University Medical Sciences · 2022
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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

12 authors at 4 institutions in 1 country.

Brooke D VergalesDivision of Neonatology, Department of Pediatrics, University of Virginia, Charlottesville, Virginia.
Alix O Paget-BrownDivision of Neonatology, Department of Pediatrics, University of Virginia, Charlottesville, Virginia.
Hoshik LeeDepartment of Physics, The College of William and Mary, Williamsburg, Virginia.
Lauren E GuinDepartment of Internal Medicine, University of Virginia, Charlottesville, Virginia.
Terri J SmootDepartment of Internal Medicine, University of Virginia, Charlottesville, Virginia.
Craig G RusinDepartment of Internal Medicine, University of Virginia, Charlottesville, Virginia.
Matthew T ClarkDepartment of Chemical Engineering, University of Virginia, Charlottesville, Virginia.
John B DelosDepartment of Physics, The College of William and Mary, Williamsburg, Virginia.
Karen D FairchildDivision of Neonatology, Department of Pediatrics, University of Virginia, Charlottesville, Virginia.
Douglas E LakeDepartment of Internal Medicine, University of Virginia, Charlottesville, Virginia.
Randall MoormanDepartment of Internal Medicine, University of Virginia, Charlottesville, Virginia.
John KattwinkelDivision of Neonatology, Department of Pediatrics, University of Virginia, Charlottesville, Virginia.
University of Virginia · USTexas Children's Hospital · USWilliam & Mary · USWilliams (United States) · US

Funding

Neonatal apnea: Online risk score from new analyses of bedside monitor waveformsRC2HD064488 · NICHD · UNIVERSITY OF VIRGINIA · PI KATTWINKEL, JOHN, MOORMAN, JOSEPH RANDALL · 2009 to 2010
$2.0M
NICHD NIH HHS 5RC2HD064488NICHD NIH HHS RC2 HD064488
6 · The paper itself

Abstract

objectiveIn 2006 the apnea of prematurity (AOP) consensus group identified inaccurate counting of apnea episodes as a major barrier to progress in AOP research. We compare nursing records of AOP to events detected by a clinically validated computer algorithm that detects apnea from standard bedside monitors. STUDY

designWaveform, vital sign, and alarm data were collected continuously from all very low-birth-weight infants admitted over a 25-month period, analyzed for central apnea, bradycardia, and desaturation (ABD) events, and compared with nursing documentation collected from charts. Our algorithm defined apnea as > 10 seconds if accompanied by bradycardia and desaturation.

resultsOf the 3,019 nurse-recorded events, only 68% had any algorithm-detected ABD event. Of the 5,275 algorithm-detected prolonged apnea events > 30 seconds, only 26% had nurse-recorded documentation within 1 hour. Monitor alarms sounded in only 74% of events of algorithm-detected prolonged apnea events > 10 seconds. There were 8,190,418 monitor alarms of any description throughout the neonatal intensive care unit during the 747 days analyzed, or one alarm every 2 to 3 minutes per nurse.

conclusionAn automated computer algorithm for continuous ABD quantitation is a far more reliable tool than the medical record to address the important research questions identified by the 2006 AOP consensus group.

Indexed as

AlgorithmsDiagnosis, Computer-AssistedApneaElectrocardiographyHumansInfant, NewbornInfant, PrematureInfant, Premature, DiseasesIntensive Care Units, NeonatalMonitoring, PhysiologicPlethysmography, Impedance

Identifiers

PMID23592319
PMCPMC5321050
OpenAlexW2329143515

What OpenQuestion holds

Textmetadata
LicenceTDM
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.