Predicting deaths in a resource-limited neonatal intensive care unit in Nepal

Neonatal death prediction scores: a systematic review and meta-analysis

OBJECTIVE

To compare, through a systematic review and meta-analysis of observational accuracy studies, the main existing neonatal death prediction scores.

METHOD

Systematic review and meta-analysis of observational accuracy studies. The databases accessed were MEDLINE, ELSEVIER, LILACS, SciELO, OpenGrey, Open Access Thesis and Dissertations, EMBASE, Web of Science, SCOPUS and Cochrane Library. For qualitative analysis, Quality Assessment of Diagnostic Accuracy Studies 2 was used. For the quantitative analysis, the area under the curve and the SE were used, as well as the inverse of the variance as a weight measure, DerSimonian and Laird as a measure of random effects, Higgins' I² as an estimate of heterogeneity, Z as a final measure with a 95% confidence level.

RESULTS

55 studies were analysed, 8 scores were compared in a total of 193 849 newborns included. The most accurate neonatal death prediction score was Score for Neonatal Acute Physiology Perinatal Extension II (SNAPPE II) (0.89 (95% CI 0.86 to 0.92)) and the least accurate was gestational age (0.75 (95% CI 0.71 to 0.79)).

CONCLUSION

SNAPPE II was the most accurate score found in this study. Despite this, the choice of score depends on the situation and setting in which the newborn is inserted, and it is up to the researcher to analyse and decide which one to use based on practicality and the possibility of local implementation. Given this, it is interesting to carry out new prospective studies to improve the prediction of neonatal deaths around the world.

PROSPERO REGISTRATION NUMBER

CRD42023462425.

A Study of the Prediction of Mortality in a Tertiary Care Hospital Using the Modified Sick Neonatal Score (MSNS): An Observational Cross-Sectional Study

PURPOSE

India is a major contributor to neonatal deaths worldwide. There is a paucity of amenities for the management of neonatal health issues in rural areas of our country. Hence, there is a need to invent a reliable scoring system for the analysis of neonatal mortality.

AIM

The aim of the study is to evaluate the Modified Sick Neonatal Score (MSNS) as a predictor of mortality in neonatal care units in resource-limited settings.

MATERIALS AND METHODS

This cross-sectional observational study was performed in the intensive care unit of our hospital. All the data were collected and analyzed using IBM Corp.'s Statistical Package for Social Sciences (SPSS) software.

RESULTS

Overall, 71 participants were considered for the present study. The common clinical diagnoses noticed in our participants were meconium aspiration, malformation, and jaundice. The MSNS score compared between expired and discharged participants is found to be statistically significant with p<0.05.

CONCLUSION

The MSNS scoring system is considered an ideal scoring system for detecting early mortality in neonates.

Comparison of four neonatal transport scoring methods in the prediction of mortality risk in full-term, out-born infants: a single-center retrospective cohort study

Neonatal transport scoring systems can assess severity before and after transport, improve transport efficiency, and predict the occurrence of critical illness. The aim of this study was to compare four neonatal transport scoring methods to predict mortality risk and clinical utility within the first week after transportation. This was a single-center retrospective cohort study. All patients were full-term, out-born neonates. Each patient was assessed by the Transport Risk Index of Physiologic Stability (TRIPS), Mortality Index for Neonatal Transportation (MINT), Transport-Related Mortality Score (TREMS), and Neonatal Critical Illness Score (NCIS) scoring methods. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) for each method were compared for their utility in predicting mortality risk within the 1st week after admission. In total, 368 full-term infants were included (368/770, 47.8% of all transported infants). Within the 1st week after admission, five infants (1.36%, 5/368) died while receiving advanced life support and full treatment, and 24 infants (6.52%, 24/368) died soon after they were discharged against medical advice. The areas under the curve (AUCs) for the MINT, TRIPS, TREMS, and NCIS for the prediction of mortality were 0.822, 0.827, 0.643, and 0.731, respectively (all p < 0.05). However, the clinical net benefits for the MINT and TRIPS were far superior than those for the NCIS and TREMS.

CONCLUSION

It was concluded that the TRIPS and MINT might be more suitable for the prediction of mortality in full-term, out-born neonates in the neonatal intensive care unit (NICU) within the 1st week after transportation.

WHAT IS KNOWN

• Neonatal transport scores can assess not only the mortality risk during transportation but also the mortality risk of critically ill newborns after admission to the NICU. • The effectiveness of neonatal transport scores in predicting mortality risk is different.

WHAT IS NEW

• Our data indicate that the diagnostic efficacy of the MINT, TRIPS, and NCIS in the prediction of full-term infant mortality was high. • The TRIPS and MINT scores had better clinical utility and could be used to predict mortality within the 1st week after transportation in full-term out-born neonates.

Non-linear association between admission temperature and neonatal mortality in a low-resource setting

Both neonatal hypothermia and hyperthermia represent important risk factors for neonatal mortality, but information on mortality risk across a full range of neonatal temperatures is lacking in low-resource settings. We evaluated the association between neonatal mortality and a full range of admission temperatures in a low-resource setting. This retrospective observational study was conducted at Beira Central Hospital, Mozambique. The relationship between admission temperature and mortality was evaluated using multivariable analyses with temperature modeled as non-linear term. Among 2098 neonates admitted to the Special Care Unit between January–December 2017, admission temperature was available in 1344 neonates (64%) who were included in the analysis. A non-linear association between mortality rate and temperature was identified. Mortality rate decreased from 84% at 32 °C to 64% at 34.6 °C (− 8% per °C), to 41% at 36 °C (− 16% per °C), to 26% to 36.6 °C (− 25% per °C) and to 22% at 38.3 °C (− 2% per °C), then increased to 40% at 41 °C (+ 7% per °C). Mortality rate was estimated to be at minimum at admission temperature of 37.5 °C. In conclusions, the non-linear relationship highlighted different mortality risks across a full range of neonatal temperatures in a low-resource setting. Admission temperature was not recorded in one third of neonates.

Modified Sick Neonatal Score (MSNS): A Novel Neonatal Disease Severity Scoring System for Resource-Limited Settings

Neonatal disease severity scoring systems are needed to make standardized comparison between performances of different units and to give prognostic information to parents of individual babies admitted. Existing scoring systems are unsuitable for resource-limited settings which lack investigations like pH, pO₂/FiO₂ ratio, and base excess. This study was planned to evaluate Modified Sick Neonatal Score (MSNS), a novel neonatal disease severity score designed for resource-constrained settings. It was a facility-based cross-sectional analytical study, conducted in the “Special Newborn Care Unit” (SNCU) of government district hospital, attached to Kasturba Medical College, Mangalore, India from November 2016 to October 2017. A convenience sample of 585 neonates was included. Disease severity was assessed immediately at admission using MSNS. MSNS had 8 parameters with 0, 1, and 2 scores for each. 41% of study population was preterm (n=240), and 84.1% had birth weight less than 2500 grams (n=492). The mean (SD) of the total MSNS scores for neonates who expired and discharged was, respectively, 8.22 (2.96) and 13.4 (2.14), a difference being statistically significant at P < 0.001. Expired newborns had statistically significant frequency of lower scores across each of the parameters. An optimum cutoff score of ≤10 with 80% sensitivity and 88.8% specificity in predicting mortality was obtained when the ROC curve was generated with the MSNS score as the test variable. Area under the curve was 0.913 (95% CI: 0.879–0.946). In conclusion, MSNS is a practicable disease severity score in resource-restricted settings like district SNCUs. It is for application in both term and preterm neonates. Total score ≤10 has a good sensitivity and specificity in predicting mortality of admitted neonates when used early during the course of hospitalization. MSNS could be used as a tool to compare performance of SNCUs and also enable early referral of individual cases to units with better facilities.