Evidence map›Paper›PMID 35332406›Full record

ArticleJournal of clinical monitoring and computing2022

Assessment of neonatal respiratory rate variability.

Jesse Coleman, Amy Sarah Ginsburg, William M Macharia, Roseline Ochieng, Dorothy Chomba, Guohai Zhou, Dustin Dunsmuir, Walter Karlen, J Mark Ansermino

Abstract read
In one paragraph

Article in Journal of clinical monitoring and computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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

9 authors.

Jesse ColemanEvaluation of Technologies for Neonates in Africa (ETNA), Nairobi, Kenya. denots@gmail.com.ORCID 0000-0002-3601-7854
Amy Sarah GinsburgClinical Trials Unit, University of Washington, Seattle, WA, USA.
William M MachariaDepartment of Pediatrics, Aga Khan University, Nairobi, Kenya.
Roseline OchiengDepartment of Pediatrics, Aga Khan University, Nairobi, Kenya.
Dorothy ChombaDepartment of Pediatrics, Aga Khan University, Nairobi, Kenya.
Guohai ZhouCenter for Clinical Investigation, Brigham and Women's Hospital, Boston, MA, USA.
Dustin DunsmuirAnesthesiology, Pharmacology & Therapeutics, The University of British Columbia, Vancouver, BC, Canada.
Walter KarlenMobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
J Mark AnserminoAnesthesiology, Pharmacology & Therapeutics, The University of British Columbia, Vancouver, BC, Canada.

Funding

Bill and Melinda Gates Foundation OPP1196617
6 · The paper itself

Abstract

Accurate measurement of respiratory rate (RR) in neonates is challenging due to high neonatal RR variability (RRV). There is growing evidence that RRV measurement could inform and guide neonatal care. We sought to quantify neonatal RRV during a clinical study in which we compared multiparameter continuous physiological monitoring (MCPM) devices. Measurements of capnography-recorded exhaled carbon dioxide across 60-s epochs were collected from neonates admitted to the neonatal unit at Aga Khan University-Nairobi hospital. Breaths were manually counted from capnograms and using an automated signal detection algorithm which also calculated mean and median RR for each epoch. Outcome measures were between- and within-neonate RRV, between- and within-epoch RRV, and 95% limits of agreement, bias, and root-mean-square deviation. Twenty-seven neonates were included, with 130 epochs analysed. Mean manual breath count (MBC) was 48 breaths per minute. Median RRV ranged from 11.5% (interquartile range (IQR) 6.8-18.9%) to 28.1% (IQR 23.5-36.7%). Bias and limits of agreement for MBC vs algorithm-derived breath count, MBC vs algorithm-derived median breath rate, MBC vs algorithm-derived mean breath rate were - 0.5 (- 2.7, 1.66), - 3.16 (- 12.12, 5.8), and - 3.99 (- 11.3, 3.32), respectively. The marked RRV highlights the challenge of performing accurate RR measurements in neonates. More research is required to optimize the use of RRV to improve care. When evaluating MCPM devices, accuracy thresholds should be less stringent in newborns due to increased RRV. Lastly, median RR, which discounts the impact of extreme outliers, may be more reflective of the underlying physiological control of breathing.

Indexed as

CapnographyRespiratory RateHumansInfant, NewbornKenyaMonitoring, PhysiologicRespirationChild healthCritical careDelivery of health careDiagnosisPatient care

Identifiers

PMID35332406
PMCPMC9637627

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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.