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Predictor variables for post-discharge mortality modelling in infants: a protocol development project

BACKGROUND: Over two-thirds of the five million annual deaths in children under five occur in infants, mostly in developing countries and many after hospital discharge. However, there is a lack of understanding of which children are at higher risk based on early clinical predictors. Early identifica...

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Autores principales: Nemetchek, Brooklyn R, Liang, Li(Danny), Kissoon, Niranjan, Ansermino, J Mark, Kabakyenga, Jerome, Lavoie, Pascal M, Fowler-Kerry, Susan, Wiens, Matthew O
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Makerere Medical School 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354852/
https://www.ncbi.nlm.nih.gov/pubmed/30766588
http://dx.doi.org/10.4314/ahs.v18i4.43
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author Nemetchek, Brooklyn R
Liang, Li(Danny)
Kissoon, Niranjan
Ansermino, J Mark
Kabakyenga, Jerome
Lavoie, Pascal M
Fowler-Kerry, Susan
Wiens, Matthew O
author_facet Nemetchek, Brooklyn R
Liang, Li(Danny)
Kissoon, Niranjan
Ansermino, J Mark
Kabakyenga, Jerome
Lavoie, Pascal M
Fowler-Kerry, Susan
Wiens, Matthew O
author_sort Nemetchek, Brooklyn R
collection PubMed
description BACKGROUND: Over two-thirds of the five million annual deaths in children under five occur in infants, mostly in developing countries and many after hospital discharge. However, there is a lack of understanding of which children are at higher risk based on early clinical predictors. Early identification of vulnerable infants at high-risk for death post-discharge is important in order to craft interventional programs. OBJECTIVES: To determine potential predictor variables for post-discharge mortality in infants less than one year of age who are likely to die after discharge from health facilities in the developing world. METHODS: A two-round modified Delphi process was conducted, wherein a panel of experts evaluated variables selected from a systematic literature review. Variables were evaluated based on (1) predictive value, (2) measurement reliability, (3) availability, and (4) applicability in low-resource settings. RESULTS: In the first round, 18 experts evaluated 37 candidate variables and suggested 26 additional variables. Twenty-seven variables derived from those suggested in the first round were evaluated by 17 experts during the second round. A final total of 55 candidate variables were retained. CONCLUSION: A systematic approach yielded 55 candidate predictor variables to use in devising predictive models for post-discharge mortality in infants in a low-resource setting.
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spelling pubmed-63548522019-02-14 Predictor variables for post-discharge mortality modelling in infants: a protocol development project Nemetchek, Brooklyn R Liang, Li(Danny) Kissoon, Niranjan Ansermino, J Mark Kabakyenga, Jerome Lavoie, Pascal M Fowler-Kerry, Susan Wiens, Matthew O Afr Health Sci Articles BACKGROUND: Over two-thirds of the five million annual deaths in children under five occur in infants, mostly in developing countries and many after hospital discharge. However, there is a lack of understanding of which children are at higher risk based on early clinical predictors. Early identification of vulnerable infants at high-risk for death post-discharge is important in order to craft interventional programs. OBJECTIVES: To determine potential predictor variables for post-discharge mortality in infants less than one year of age who are likely to die after discharge from health facilities in the developing world. METHODS: A two-round modified Delphi process was conducted, wherein a panel of experts evaluated variables selected from a systematic literature review. Variables were evaluated based on (1) predictive value, (2) measurement reliability, (3) availability, and (4) applicability in low-resource settings. RESULTS: In the first round, 18 experts evaluated 37 candidate variables and suggested 26 additional variables. Twenty-seven variables derived from those suggested in the first round were evaluated by 17 experts during the second round. A final total of 55 candidate variables were retained. CONCLUSION: A systematic approach yielded 55 candidate predictor variables to use in devising predictive models for post-discharge mortality in infants in a low-resource setting. Makerere Medical School 2018-12 /pmc/articles/PMC6354852/ /pubmed/30766588 http://dx.doi.org/10.4314/ahs.v18i4.43 Text en © 2018 Nemetchek et al. Licensee African Health Sciences. This is an Open Access article distributed under the terms of the Creative commons Attribution License (https://creativecommons.org/licenses/BY/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Nemetchek, Brooklyn R
Liang, Li(Danny)
Kissoon, Niranjan
Ansermino, J Mark
Kabakyenga, Jerome
Lavoie, Pascal M
Fowler-Kerry, Susan
Wiens, Matthew O
Predictor variables for post-discharge mortality modelling in infants: a protocol development project
title Predictor variables for post-discharge mortality modelling in infants: a protocol development project
title_full Predictor variables for post-discharge mortality modelling in infants: a protocol development project
title_fullStr Predictor variables for post-discharge mortality modelling in infants: a protocol development project
title_full_unstemmed Predictor variables for post-discharge mortality modelling in infants: a protocol development project
title_short Predictor variables for post-discharge mortality modelling in infants: a protocol development project
title_sort predictor variables for post-discharge mortality modelling in infants: a protocol development project
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354852/
https://www.ncbi.nlm.nih.gov/pubmed/30766588
http://dx.doi.org/10.4314/ahs.v18i4.43
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