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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Makerere Medical School
2018
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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. |
format | Online Article Text |
id | pubmed-6354852 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Makerere Medical School |
record_format | MEDLINE/PubMed |
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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