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

BMC Pediatrics

Louise Matthews¹  Dory Kovacs¹  Katarina Oravcova¹  Muhammad Bilal²  Stephen E. Mshana³  Delfina R. Msanga⁴ 

[1] Boyd Orr Centre for Population and Ecosystem Health and Institute of Biodiversity, Animal health and Comparative Medicine, University of Glasgow, G12 8QQ, Glasgow, UK;Boyd Orr Centre for Population and Ecosystem Health and Institute of Biodiversity, Animal health and Comparative Medicine, University of Glasgow, G12 8QQ, Glasgow, UK;Quality Operations Laboratory, University of Veterinary and Animal Sciences, Lahore, Pakistan;Department of Microbiology and Immunology, Catholic University of Health and Allied Sciences, Mwanza, Tanzania;Department of Paediatrics and Child Health, Catholic University of Health and Allied Sciences, Mwanza, Tanzania;

关键词: Early warning systems;    LMIC;    Machine learning;    Neonatal mortality;    Vital signs;   

DOI  :  10.1186/s12887-021-03012-4

来源: Springer

【 摘 要 】

BackgroundNeonatal 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.MethodsIn 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.ResultsGLMs 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.ConclusionsUnderlying 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.

【 授权许可】

CC BY