Evidence map›Paper›PMID 35197529›Full record

ArticleScientific reports2022

Clinical feasibility of a contactless multiparameter continuous monitoring technology for neonates in a large public maternity hospital in Nairobi, Kenya.

Amy Sarah Ginsburg, Sahar Zandi Nia, Dorothy Chomba, Dustin Dunsmuir, Mary Waiyego, Jesse Coleman, Roseline Ochieng, Sichen Liu, Guohai Zhou, J Mark Ansermino and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  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

11 authors.

Amy Sarah GinsburgClinical Trials Center, University of Washington, Building 29, Suite 250, 6200 NE 74th Street, Seattle, WA, 98115, USA. messageforamy@gmail.com.
Sahar Zandi NiaDepartment of Anesthesiology, The University of British Columbia, Vancouver, BC, Canada.
Dorothy ChombaDepartment of Pediatrics, Aga Khan University, Nairobi, Kenya.
Dustin DunsmuirDepartment of Anesthesiology, The University of British Columbia, Vancouver, BC, Canada.
Mary WaiyegoPumwani Maternity Hospital, Nairobi, Kenya.
Jesse ColemanEvaluation of Technologies for Neonates in Africa, Seattle, WA, USA.
Roseline OchiengDepartment of Pediatrics, Aga Khan University, Nairobi, Kenya.
Sichen LiuDepartment of Anesthesiology, The University of British Columbia, Vancouver, BC, Canada.
Guohai ZhouCenter for Clinical Investigation, Brigham and Women's Hospital, Boston, MA, USA.
J Mark AnserminoDepartment of Anesthesiology, The University of British Columbia, Vancouver, BC, Canada.
William M MachariaDepartment of Pediatrics, Aga Khan University, Nairobi, Kenya.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiparameter continuous physiological monitoring (MCPM) technologies are critical in the clinical management of high-risk neonates; yet, these technologies are frequently unavailable in many African healthcare facilities. We conducted a prospective clinical feasibility study of EarlySense's novel under-mattress MCPM technology in neonates at Pumwani Maternity Hospital in Nairobi, Kenya. To assess feasibility, we compared the performance of EarlySense's technology to Masimo's Rad-97 pulse CO-oximeter with capnography technology for heart rate (HR) and respiratory rate (RR) measurements using up-time, clinical event detection performance, and accuracy. Between September 15 and December 15, 2020, we collected and analyzed 470 hours of EarlySense data from 109 enrolled neonates. EarlySense's technology's up-time per neonate was 2.9 (range 0.8, 5.3) hours for HR and 2.1 (range 0.9, 4.0) hours for RR. The difference compared to the reference was a median of 0.6 (range 0.1, 3.1) hours for HR and 0.8 (range 0.1, 2.9) hours for RR. EarlySense's technology identified high HR and RR events with high sensitivity (HR 81%; RR 83%) and specificity (HR 99%; RR 83%), but was less sensitive for low HR and RR (HR 0%; RR 14%) although maintained specificity (HR 100%; RR 95%). There was a greater number of false negative and false positive RR events than false negative and false positive HR events. The normalized spread of limits of agreement was 9.6% for HR and 28.6% for RR, which met the a priori-identified limit of 30%. EarlySense's MCPM technology was clinically feasible as demonstrated by high percentage of up-time, strong clinical event detection performance, and agreement of HR and RR measurements compared to the reference technology. Studies in critically ill neonates, assessing barriers and facilitators to adoption, and costing analyses will be key to the technology's development and potential uptake and scale-up.

Indexed as

Hospitals, MaternityHospitals, PublicFalse Negative ReactionsFalse Positive ReactionsFeasibility StudiesFemaleHeart RateHumansInfant, NewbornInfant, Newborn, DiseasesKenyaLimit of DetectionMonitoring, PhysiologicPregnancyProspective StudiesRespiratory Rate

Identifiers

PMID35197529
PMCPMC8866488

What OpenQuestion holds

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LicenceCC BY
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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.