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Early warning for healthcare acquired infections in neonatal care units in a low-resource setting using routinely collected hospital data: The experience from Haiti, 2014–2018

In low-resource settings, detection of healthcare-acquired outbreaks in neonatal units relies on astute clinical staff to observe unusual morbidity or mortality from sepsis as microbiological diagnostics are often absent. We aimed to generate reliable (and automated) early warnings for potential clu...

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Autores principales: Lenglet, Annick, Contigiani, Omar, Ariti, Cono, Evens, Estivern, Charles, Kessianne, Casimir, Carl-Frédéric, Senat Delva, Rodnie, Badjo, Colette, Roggeveen, Harriet, Pawulska, Barbara, Clezy, Kate, McRae, Melissa, Wertheim, Heiman, Hopman, Joost
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223318/
https://www.ncbi.nlm.nih.gov/pubmed/35737713
http://dx.doi.org/10.1371/journal.pone.0269385
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author Lenglet, Annick
Contigiani, Omar
Ariti, Cono
Evens, Estivern
Charles, Kessianne
Casimir, Carl-Frédéric
Senat Delva, Rodnie
Badjo, Colette
Roggeveen, Harriet
Pawulska, Barbara
Clezy, Kate
McRae, Melissa
Wertheim, Heiman
Hopman, Joost
author_facet Lenglet, Annick
Contigiani, Omar
Ariti, Cono
Evens, Estivern
Charles, Kessianne
Casimir, Carl-Frédéric
Senat Delva, Rodnie
Badjo, Colette
Roggeveen, Harriet
Pawulska, Barbara
Clezy, Kate
McRae, Melissa
Wertheim, Heiman
Hopman, Joost
author_sort Lenglet, Annick
collection PubMed
description In low-resource settings, detection of healthcare-acquired outbreaks in neonatal units relies on astute clinical staff to observe unusual morbidity or mortality from sepsis as microbiological diagnostics are often absent. We aimed to generate reliable (and automated) early warnings for potential clusters of neonatal late onset sepsis using retrospective data that could signal the start of an outbreak in an NCU in Port au Prince, Haiti, using routinely collected data on neonatal admissions. We constructed smoothed time series for late onset sepsis cases, late onset sepsis rates, neonatal care unit (NCU) mortality, maternal admissions, neonatal admissions and neonatal antibiotic consumption. An outbreak was defined as a statistical increase in any of these time series indicators. We created three outbreak alarm classes: 1) thresholds: weeks in which the late onset sepsis cases exceeded four, the late onset sepsis rates exceeded 10% of total NCU admissions and the NCU mortality exceeded 15%; 2) differential: late onset sepsis rates and NCU mortality were double the previous week; and 3) aberration: using the improved Farrington model for late onset sepsis rates and NCU mortality. We validated pairs of alarms by calculating the sensitivity and specificity of the weeks in which each alarm was launched and comparing each alarm to the weeks in which a single GNB positive blood culture was reported from a neonate. The threshold and aberration alarms were the strongest predictors for current and future NCU mortality and current LOS rates (p<0.0002). The aberration alarms were also those with the highest sensitivity, specificity, negative predictive value, and positive predictive value. Without microbiological diagnostics in NCUs in low-resource settings, applying these simple algorithms to routinely collected data show great potential to facilitate early warning for possible healthcare-acquired outbreaks of LOS in neonates. The methods used in this study require validation across other low-resource settings.
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spelling pubmed-92233182022-06-24 Early warning for healthcare acquired infections in neonatal care units in a low-resource setting using routinely collected hospital data: The experience from Haiti, 2014–2018 Lenglet, Annick Contigiani, Omar Ariti, Cono Evens, Estivern Charles, Kessianne Casimir, Carl-Frédéric Senat Delva, Rodnie Badjo, Colette Roggeveen, Harriet Pawulska, Barbara Clezy, Kate McRae, Melissa Wertheim, Heiman Hopman, Joost PLoS One Research Article In low-resource settings, detection of healthcare-acquired outbreaks in neonatal units relies on astute clinical staff to observe unusual morbidity or mortality from sepsis as microbiological diagnostics are often absent. We aimed to generate reliable (and automated) early warnings for potential clusters of neonatal late onset sepsis using retrospective data that could signal the start of an outbreak in an NCU in Port au Prince, Haiti, using routinely collected data on neonatal admissions. We constructed smoothed time series for late onset sepsis cases, late onset sepsis rates, neonatal care unit (NCU) mortality, maternal admissions, neonatal admissions and neonatal antibiotic consumption. An outbreak was defined as a statistical increase in any of these time series indicators. We created three outbreak alarm classes: 1) thresholds: weeks in which the late onset sepsis cases exceeded four, the late onset sepsis rates exceeded 10% of total NCU admissions and the NCU mortality exceeded 15%; 2) differential: late onset sepsis rates and NCU mortality were double the previous week; and 3) aberration: using the improved Farrington model for late onset sepsis rates and NCU mortality. We validated pairs of alarms by calculating the sensitivity and specificity of the weeks in which each alarm was launched and comparing each alarm to the weeks in which a single GNB positive blood culture was reported from a neonate. The threshold and aberration alarms were the strongest predictors for current and future NCU mortality and current LOS rates (p<0.0002). The aberration alarms were also those with the highest sensitivity, specificity, negative predictive value, and positive predictive value. Without microbiological diagnostics in NCUs in low-resource settings, applying these simple algorithms to routinely collected data show great potential to facilitate early warning for possible healthcare-acquired outbreaks of LOS in neonates. The methods used in this study require validation across other low-resource settings. Public Library of Science 2022-06-23 /pmc/articles/PMC9223318/ /pubmed/35737713 http://dx.doi.org/10.1371/journal.pone.0269385 Text en © 2022 Lenglet et al https://creativecommons.org/licenses/by/4.0/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 author and source are credited.
spellingShingle Research Article
Lenglet, Annick
Contigiani, Omar
Ariti, Cono
Evens, Estivern
Charles, Kessianne
Casimir, Carl-Frédéric
Senat Delva, Rodnie
Badjo, Colette
Roggeveen, Harriet
Pawulska, Barbara
Clezy, Kate
McRae, Melissa
Wertheim, Heiman
Hopman, Joost
Early warning for healthcare acquired infections in neonatal care units in a low-resource setting using routinely collected hospital data: The experience from Haiti, 2014–2018
title Early warning for healthcare acquired infections in neonatal care units in a low-resource setting using routinely collected hospital data: The experience from Haiti, 2014–2018
title_full Early warning for healthcare acquired infections in neonatal care units in a low-resource setting using routinely collected hospital data: The experience from Haiti, 2014–2018
title_fullStr Early warning for healthcare acquired infections in neonatal care units in a low-resource setting using routinely collected hospital data: The experience from Haiti, 2014–2018
title_full_unstemmed Early warning for healthcare acquired infections in neonatal care units in a low-resource setting using routinely collected hospital data: The experience from Haiti, 2014–2018
title_short Early warning for healthcare acquired infections in neonatal care units in a low-resource setting using routinely collected hospital data: The experience from Haiti, 2014–2018
title_sort early warning for healthcare acquired infections in neonatal care units in a low-resource setting using routinely collected hospital data: the experience from haiti, 2014–2018
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223318/
https://www.ncbi.nlm.nih.gov/pubmed/35737713
http://dx.doi.org/10.1371/journal.pone.0269385
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