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Study protocol: Generation Victoria (GenV) special care nursery registry

INTRODUCTION: Newborn babies who require admission for specialist care can experience immediate and sometimes lasting impacts. For babies admitted to special care nurseries (SCN), there is no dataset comparable to that of the Australian and New Zealand Neonatal Network (ANZNN), which has helped impr...

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Autores principales: Wang, Jing, Hu, Yanhong Jessika, Collins, Lana, Fedyukova, Anna, Aggarwal, Varnika, Mensah, Fiona, Cheong, Jeanie L.Y., Wake, Melissa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Swansea University 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476699/
https://www.ncbi.nlm.nih.gov/pubmed/37670960
http://dx.doi.org/10.23889/ijpds.v8i1.2139
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author Wang, Jing
Hu, Yanhong Jessika
Collins, Lana
Fedyukova, Anna
Aggarwal, Varnika
Mensah, Fiona
Cheong, Jeanie L.Y.
Wake, Melissa
author_facet Wang, Jing
Hu, Yanhong Jessika
Collins, Lana
Fedyukova, Anna
Aggarwal, Varnika
Mensah, Fiona
Cheong, Jeanie L.Y.
Wake, Melissa
author_sort Wang, Jing
collection PubMed
description INTRODUCTION: Newborn babies who require admission for specialist care can experience immediate and sometimes lasting impacts. For babies admitted to special care nurseries (SCN), there is no dataset comparable to that of the Australian and New Zealand Neonatal Network (ANZNN), which has helped improve the quality and consistency of neonatal intensive care through standardised data collection. OBJECTIVES: We aim to establish a proof-of-concept, Victoria-wide registry of babies admitted to SCN, embedded within the whole-of-Victoria Generation Victoria (GenV) cohort. METHODS: This prototype registry is a depth sub-cohort nested within GenV, targeting all babies born in Victoria from Oct-2021 to Oct-2023. Infants admitted to SCN are eligible. The minimum dataset will be harmonised with ANZNN for common constructs but also include SCN-only items, and will cover maternal, antenatal, newborn, respiratory/respiratory support, cardiac, infection, nutrition, feeding, cerebral and other items. As well as the dataset, this protocol outlines the anticipated cohort, timeline for this registry, and how this will serve as a resource for longitudinal research through its integration with the GenV longitudinal cohort and linked datasets. CONCLUSION: The registry will provide the opportunity to better understand the health and future outcomes of the large and growing cohort of children that require specialist care after birth. The data would generate translatable evidence and could lay the groundwork for a stand-alone ongoing clinical quality registry post-GenV.
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spelling pubmed-104766992023-09-05 Study protocol: Generation Victoria (GenV) special care nursery registry Wang, Jing Hu, Yanhong Jessika Collins, Lana Fedyukova, Anna Aggarwal, Varnika Mensah, Fiona Cheong, Jeanie L.Y. Wake, Melissa Int J Popul Data Sci Population Data Science INTRODUCTION: Newborn babies who require admission for specialist care can experience immediate and sometimes lasting impacts. For babies admitted to special care nurseries (SCN), there is no dataset comparable to that of the Australian and New Zealand Neonatal Network (ANZNN), which has helped improve the quality and consistency of neonatal intensive care through standardised data collection. OBJECTIVES: We aim to establish a proof-of-concept, Victoria-wide registry of babies admitted to SCN, embedded within the whole-of-Victoria Generation Victoria (GenV) cohort. METHODS: This prototype registry is a depth sub-cohort nested within GenV, targeting all babies born in Victoria from Oct-2021 to Oct-2023. Infants admitted to SCN are eligible. The minimum dataset will be harmonised with ANZNN for common constructs but also include SCN-only items, and will cover maternal, antenatal, newborn, respiratory/respiratory support, cardiac, infection, nutrition, feeding, cerebral and other items. As well as the dataset, this protocol outlines the anticipated cohort, timeline for this registry, and how this will serve as a resource for longitudinal research through its integration with the GenV longitudinal cohort and linked datasets. CONCLUSION: The registry will provide the opportunity to better understand the health and future outcomes of the large and growing cohort of children that require specialist care after birth. The data would generate translatable evidence and could lay the groundwork for a stand-alone ongoing clinical quality registry post-GenV. Swansea University 2023-06-13 /pmc/articles/PMC10476699/ /pubmed/37670960 http://dx.doi.org/10.23889/ijpds.v8i1.2139 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Population Data Science
Wang, Jing
Hu, Yanhong Jessika
Collins, Lana
Fedyukova, Anna
Aggarwal, Varnika
Mensah, Fiona
Cheong, Jeanie L.Y.
Wake, Melissa
Study protocol: Generation Victoria (GenV) special care nursery registry
title Study protocol: Generation Victoria (GenV) special care nursery registry
title_full Study protocol: Generation Victoria (GenV) special care nursery registry
title_fullStr Study protocol: Generation Victoria (GenV) special care nursery registry
title_full_unstemmed Study protocol: Generation Victoria (GenV) special care nursery registry
title_short Study protocol: Generation Victoria (GenV) special care nursery registry
title_sort study protocol: generation victoria (genv) special care nursery registry
topic Population Data Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476699/
https://www.ncbi.nlm.nih.gov/pubmed/37670960
http://dx.doi.org/10.23889/ijpds.v8i1.2139
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