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nSeP: immune and metabolic biomarkers for early detection of neonatal sepsis—protocol for a prospective multicohort study
BMJ Open, Volume: 11, Issue: 12, Start page: e050100
Swansea University Author: Luke Davies
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DOI (Published version): 10.1136/bmjopen-2021-050100
Abstract
Introduction Diagnosing neonatal sepsis is heavily dependent on clinical phenotyping as culture-positive body fluid has poor sensitivity, and existing blood biomarkers have poor specificity.A combination of machine learning, statistical and deep pathway biology analyses led to the identification of...
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ISSN: | 2044-6055 2044-6055 |
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URI: | https://cronfa.swan.ac.uk/Record/cronfa61694 |
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2022-11-07T14:51:33.5508296 v2 61694 2022-10-31 nSeP: immune and metabolic biomarkers for early detection of neonatal sepsis—protocol for a prospective multicohort study ff080296775381560053d5e3a6e81745 0000-0001-7767-4060 Luke Davies Luke Davies true false 2022-10-31 MEDS Introduction Diagnosing neonatal sepsis is heavily dependent on clinical phenotyping as culture-positive body fluid has poor sensitivity, and existing blood biomarkers have poor specificity.A combination of machine learning, statistical and deep pathway biology analyses led to the identification of a tripartite panel of biologically connected immune and metabolic markers that showed greater than 99% accuracy for detecting bacterial infection with 100% sensitivity. The cohort study described here is designed as a large-scale clinical validation of this previous work.Methods and analysis This multicentre observational study will prospectively recruit a total of 1445 newborn infants (all gestations)—1084 with suspected early—or late-onset sepsis, and 361 controls—over 4 years. A small volume of whole blood will be collected from infants with suspected sepsis at the time of presentation. This sample will be used for integrated transcriptomic, lipidomic and targeted proteomics profiling. In addition, a subset of samples will be subjected to cellular phenotype and proteomic analyses. A second sample from the same patient will be collected at 24 hours, with an opportunistic sampling for stool culture. For control infants, only one set of blood and stool sample will be collected to coincide with clinical blood sampling. Along with detailed clinical information, blood and stool samples will be analysed and the information will be used to identify and validate the efficacy of immune-metabolic networks in the diagnosis of bacterial neonatal sepsis and to identify new host biomarkers for viral sepsis.Ethics and dissemination The study has received research ethics committee approval from the Wales Research Ethics Committee 2 (reference 19/WA/0008) and operational approval from Health and Care Research Wales. Submission of study results for publication will involve making available all anonymised primary and processed data on public repository sites.Trial registration number NCT03777670 Journal Article BMJ Open 11 12 e050100 BMJ 2044-6055 2044-6055 30 12 2021 2021-12-30 10.1136/bmjopen-2021-050100 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University The study is funded by a Ser Cymru grant from Welsh Government and EU/ERDF funds to Professor Peter Ghazal. MC was partly supported by a grant from Health and Care Research Wales (CRTA 16–04). 2022-11-07T14:51:33.5508296 2022-10-31T12:36:42.3407224 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Mallinath Chakraborty 0000-0002-1721-6532 1 Patrícia R S Rodrigues 2 W John Watkins 3 Angela Hayward 4 Alok Sharma 5 Rachel Hayward 6 Elisa Smit 7 Rebekka Jones 8 Nitin Goel 9 Amar Asokkumar 10 Jennifer Calvert 11 David Odd 12 Ian Morris 13 Cora Doherty 14 Sian Elliott 15 Angela Strang 16 Robert Andrews 17 Summia Zaher 18 Simran Sharma 0000-0002-6647-9355 19 Sarah Bell 20 Siva Oruganti 21 Claire Smith 22 Judith Orme 23 Sarah Edkins 24 Marie Craigon 25 Daniel White 26 Widad Dantoft 0000-0002-8546-5514 27 Luke Davies 0000-0001-7767-4060 28 Linda Moet 29 James E McLaren 30 Samantha Clarkstone 31 Gareth L Watson 32 Kerenza Hood 33 Sailesh Kotecha 0000-0003-3535-7627 34 B. Paul Morgan 35 Valerie B O’Donnell 36 Peter Ghazal 0000-0003-0035-2228 37 61694__25675__7b19ee7b7a2e4a57b2eccc48dcf3209e.pdf 61694.pdf 2022-11-07T14:50:42.1202547 Output 661207 application/pdf Version of Record true This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license true eng http://creativecommons.org/licenses/by-nc/4.0/ |
title |
nSeP: immune and metabolic biomarkers for early detection of neonatal sepsis—protocol for a prospective multicohort study |
spellingShingle |
nSeP: immune and metabolic biomarkers for early detection of neonatal sepsis—protocol for a prospective multicohort study Luke Davies |
title_short |
nSeP: immune and metabolic biomarkers for early detection of neonatal sepsis—protocol for a prospective multicohort study |
title_full |
nSeP: immune and metabolic biomarkers for early detection of neonatal sepsis—protocol for a prospective multicohort study |
title_fullStr |
nSeP: immune and metabolic biomarkers for early detection of neonatal sepsis—protocol for a prospective multicohort study |
title_full_unstemmed |
nSeP: immune and metabolic biomarkers for early detection of neonatal sepsis—protocol for a prospective multicohort study |
title_sort |
nSeP: immune and metabolic biomarkers for early detection of neonatal sepsis—protocol for a prospective multicohort study |
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ff080296775381560053d5e3a6e81745 |
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ff080296775381560053d5e3a6e81745_***_Luke Davies |
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Luke Davies |
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Mallinath Chakraborty Patrícia R S Rodrigues W John Watkins Angela Hayward Alok Sharma Rachel Hayward Elisa Smit Rebekka Jones Nitin Goel Amar Asokkumar Jennifer Calvert David Odd Ian Morris Cora Doherty Sian Elliott Angela Strang Robert Andrews Summia Zaher Simran Sharma Sarah Bell Siva Oruganti Claire Smith Judith Orme Sarah Edkins Marie Craigon Daniel White Widad Dantoft Luke Davies Linda Moet James E McLaren Samantha Clarkstone Gareth L Watson Kerenza Hood Sailesh Kotecha B. Paul Morgan Valerie B O’Donnell Peter Ghazal |
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description |
Introduction Diagnosing neonatal sepsis is heavily dependent on clinical phenotyping as culture-positive body fluid has poor sensitivity, and existing blood biomarkers have poor specificity.A combination of machine learning, statistical and deep pathway biology analyses led to the identification of a tripartite panel of biologically connected immune and metabolic markers that showed greater than 99% accuracy for detecting bacterial infection with 100% sensitivity. The cohort study described here is designed as a large-scale clinical validation of this previous work.Methods and analysis This multicentre observational study will prospectively recruit a total of 1445 newborn infants (all gestations)—1084 with suspected early—or late-onset sepsis, and 361 controls—over 4 years. A small volume of whole blood will be collected from infants with suspected sepsis at the time of presentation. This sample will be used for integrated transcriptomic, lipidomic and targeted proteomics profiling. In addition, a subset of samples will be subjected to cellular phenotype and proteomic analyses. A second sample from the same patient will be collected at 24 hours, with an opportunistic sampling for stool culture. For control infants, only one set of blood and stool sample will be collected to coincide with clinical blood sampling. Along with detailed clinical information, blood and stool samples will be analysed and the information will be used to identify and validate the efficacy of immune-metabolic networks in the diagnosis of bacterial neonatal sepsis and to identify new host biomarkers for viral sepsis.Ethics and dissemination The study has received research ethics committee approval from the Wales Research Ethics Committee 2 (reference 19/WA/0008) and operational approval from Health and Care Research Wales. Submission of study results for publication will involve making available all anonymised primary and processed data on public repository sites.Trial registration number NCT03777670 |
published_date |
2021-12-30T14:21:25Z |
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11.048237 |