Developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania

Abstract Background Neonatal mortality remains high in Tanzania at approximately 20 deaths per 1000 live births. Low birthweight, prematurity, and asphyxia are associated with neonatal mortality; however, no studies have assessed the value of combining underlying conditions and vital signs to provid...

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Autores principales:	Dory Kovacs, Delfina R. Msanga, Stephen E. Mshana, Muhammad Bilal, Katarina Oravcova, Louise Matthews
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Early warning systems LMIC Machine learning Neonatal mortality Vital signs Pediatrics RJ1-570
Acceso en línea:	https://doaj.org/article/de3a92a158234132876ac1a0ee668d1c
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spelling	oai:doaj.org-article:de3a92a158234132876ac1a0ee668d1c2021-12-05T12:21:15ZDeveloping practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania10.1186/s12887-021-03012-41471-2431https://doaj.org/article/de3a92a158234132876ac1a0ee668d1c2021-12-01T00:00:00Zhttps://doi.org/10.1186/s12887-021-03012-4https://doaj.org/toc/1471-2431Abstract Background Neonatal mortality remains high in Tanzania at approximately 20 deaths per 1000 live births. Low birthweight, prematurity, and asphyxia are associated with neonatal mortality; however, no studies have assessed the value of combining underlying conditions and vital signs to provide clinicians with early warning of infants at risk of mortality. The aim of this study was to identify risk factors (including vital signs) associated with neonatal mortality in the neonatal intensive care unit (NICU) in Bugando Medical Centre (BMC), Mwanza, Tanzania; to identify the most accurate generalised linear model (GLM) or decision tree for predicting mortality; and to provide a tool that provides clinically relevant cut-offs for predicting mortality that is easily used by clinicians in a low-resource setting. Methods In total, 165 neonates were enrolled between November 2019 and March 2020, of whom 80 (48.5%) died. We competed the performance of GLMs and decision trees by resampling the data to create training and test datasets and comparing their accuracy at correctly predicting mortality. Results GLMs always outperformed decision trees. The best fitting GLM showed that (for standardised risk factors) temperature (OR 0.61, 95% CI 0.40–0.90), birthweight (OR 0.33, 95% CI 0.20–0.52), and oxygen saturation (OR 0.66, 95% CI 0.45–0.94) were negatively associated with mortality, while heart rate (OR 1.59, 95% CI 1.10–2.35) and asphyxia (OR 3.23, 95% 1.25–8.91) were risk factors. To identify the tool that balances accuracy and with ease of use in a low-resource clinical setting, we compared the best fitting GLM with simpler versions, and identified the three-variable GLM with temperature, heart rate, and birth weight as the best candidate. For this tool, cut-offs were identified using receiver operator characteristic (ROC) curves with the optimal cut-off for mortality prediction corresponding to 76.3% sensitivity and 68.2% specificity. The final tool is graphical, showing cut-offs that depend on birthweight, heart rate, and temperature. Conclusions Underlying conditions and vital signs can be combined into simple graphical tools that improve upon the current guidelines and are straightforward to use by clinicians in a low-resource setting.Dory KovacsDelfina R. MsangaStephen E. MshanaMuhammad BilalKatarina OravcovaLouise MatthewsBMCarticleEarly warning systemsLMICMachine learningNeonatal mortalityVital signsPediatricsRJ1-570ENBMC Pediatrics, Vol 21, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Early warning systems LMIC Machine learning Neonatal mortality Vital signs Pediatrics RJ1-570
spellingShingle	Early warning systems LMIC Machine learning Neonatal mortality Vital signs Pediatrics RJ1-570 Dory Kovacs Delfina R. Msanga Stephen E. Mshana Muhammad Bilal Katarina Oravcova Louise Matthews Developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania
description	Abstract Background Neonatal mortality remains high in Tanzania at approximately 20 deaths per 1000 live births. Low birthweight, prematurity, and asphyxia are associated with neonatal mortality; however, no studies have assessed the value of combining underlying conditions and vital signs to provide clinicians with early warning of infants at risk of mortality. The aim of this study was to identify risk factors (including vital signs) associated with neonatal mortality in the neonatal intensive care unit (NICU) in Bugando Medical Centre (BMC), Mwanza, Tanzania; to identify the most accurate generalised linear model (GLM) or decision tree for predicting mortality; and to provide a tool that provides clinically relevant cut-offs for predicting mortality that is easily used by clinicians in a low-resource setting. Methods In total, 165 neonates were enrolled between November 2019 and March 2020, of whom 80 (48.5%) died. We competed the performance of GLMs and decision trees by resampling the data to create training and test datasets and comparing their accuracy at correctly predicting mortality. Results GLMs always outperformed decision trees. The best fitting GLM showed that (for standardised risk factors) temperature (OR 0.61, 95% CI 0.40–0.90), birthweight (OR 0.33, 95% CI 0.20–0.52), and oxygen saturation (OR 0.66, 95% CI 0.45–0.94) were negatively associated with mortality, while heart rate (OR 1.59, 95% CI 1.10–2.35) and asphyxia (OR 3.23, 95% 1.25–8.91) were risk factors. To identify the tool that balances accuracy and with ease of use in a low-resource clinical setting, we compared the best fitting GLM with simpler versions, and identified the three-variable GLM with temperature, heart rate, and birth weight as the best candidate. For this tool, cut-offs were identified using receiver operator characteristic (ROC) curves with the optimal cut-off for mortality prediction corresponding to 76.3% sensitivity and 68.2% specificity. The final tool is graphical, showing cut-offs that depend on birthweight, heart rate, and temperature. Conclusions Underlying conditions and vital signs can be combined into simple graphical tools that improve upon the current guidelines and are straightforward to use by clinicians in a low-resource setting.
format	article
author	Dory Kovacs Delfina R. Msanga Stephen E. Mshana Muhammad Bilal Katarina Oravcova Louise Matthews
author_facet	Dory Kovacs Delfina R. Msanga Stephen E. Mshana Muhammad Bilal Katarina Oravcova Louise Matthews
author_sort	Dory Kovacs
title	Developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania
title_short	Developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania
title_full	Developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania
title_fullStr	Developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania
title_full_unstemmed	Developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania
title_sort	developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in tanzania
publisher	BMC
publishDate	2021
url	https://doaj.org/article/de3a92a158234132876ac1a0ee668d1c
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Developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania

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