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 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.

Geographical validation of the Smart Triage Model by age group

“…Thus far, a review that examined 84032 studies on prediction models concluded that only 5% had been externally validated [ 14 ]. Furthermore, current neonatal predictive models used in low resources settings are developed mainly for estimating in-hospital mortality [ 34 , 36 ]. The outcome of severely ill children is based on more than just in-hospital mortality as admission, readmission, and post-discharge mortality are intricately intertwined in impacting child survival.…”

Development of a Nomogram for Clinical Risk Prediction of Preterm Neonate Death in Ethiopia

“…Kangaroo Mother Care is one prognostic determinant for preterm mortality. One reason is that KMC prevents hypothermia, which causes neonatal mortality (27,32). KMC offers many more benefits besides thermal protection, notably a successful initiation and maintenance of lactation and preventing apnea of prematurity.…”

“…Forty-four (9.6%) preterm neonates had PNA. Seven percent (27) of the preterm neonates had hypoglycemia (Table 4).…”

From Prediction to Practice: A Rapid Review on the Opportunities and Challenges of using Artificial Intelligence in the Neonatal Intensive Care Unit (NICU) (Preprint)

“…The studies, as seen in Figure 2, include various technology interventions in NICU research: seven using Machine Learning (27)(28)(29)(30)(31)(32)(33), three combining Machine and Deep Learning (34)(35)(36), and fourteen exclusively focusing on Deep Learning (37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). Geographically, thirteen studies are from the USA (27,28,32,36,37,39,41,42,44,(46)(47)(48)(49), with others from Austria (two) (29,40), Taiwan (two) (43,45), and six in different countries including Argentina (31), China (35), Denmark (33), Iran (50), Italy (34), and Tanzania (30), along with one multinational dataset…”

“…The 'Other' category spans a variety of outcomes, with different AI techniques employed. 'Mortality' is studied by four articles, mostly using Deep Learning (30,44,47,50). Lastly, 'Length of Stay' involves two studies, evenly split between Deep Learning and Machine Learning (27,45).…”