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. 2023 Dec 9;11(24):3131.
doi: 10.3390/healthcare11243131.

Identification of the Best Predictive Model for Mortality in Outborn Neonates-Retrospective Cohort Study

Maria Livia Ognean  1 , Bianca Coțovanu  2 , Dumitru Alin Teacoe  1   2 , Ioana Andrada Radu  1 , Samuel Bogdan Todor  1 , Cristian Ichim  1 , Iris Codruța Mureșan  1 , Adrian-Gheorghe Boicean  1 , Radu Galiș  3   4 , Manuela Cucerea  4
Affiliations

Identification of the Best Predictive Model for Mortality in Outborn Neonates-Retrospective Cohort Study

Maria Livia Ognean et al. Healthcare (Basel). .

Abstract

Background: Transportation of sick newborns is a major predictor of outcome. Prompt identification of the sickest newborns allows adequate intervention and outcome optimization. An optimal scoring system has not yet been identified.

Aim: To identify a rapid, accurate, and easy-to-perform score predictive for neonatal mortality in outborn neonates.

Material and methods: All neonates admitted by transfer in a level III regional neonatal unit between 1 January 2015 and 31 December 2021 were included. Infants with congenital critical abnormalities were excluded (N = 15). Gestational age (GA), birth weight (BW), Apgar score, place of birth, time between delivery and admission (AT), early onset sepsis, and sick neonatal score (SNS) were collected from medical records and tested for their association with mortality, including in subgroups (preterm vs. term infants); GA, BW, and AT were used to develop MSNS-AT score, to improve mortality prediction. The main outcome was all-cause mortality prediction. Univariable and multivariable analysis, including Cox regression, were performed, and odds ratio and hazard ratios were calculated were appropriate.

Results: 418 infants were included; 217/403 infants were born prematurely (53.8%), and 20 died (4.96%). Compared with the survivors, the non-survivors had lower GA, BW, and SNS scores (p < 0.05); only the SNS scores remained lower in the subgroup analysis. Time to admission was associated with an increased mortality rate in the whole group and preterm infants (p < 0.05). In multiple Cox regression models, a cut-off value of MSNS-AT score ≤ 10 was more precise in predicting mortality as compared with SNS (AUC 0.735 vs. 0.775) in the entire group and in the preterm infants group (AUC 0.885 vs. 0.810).

Conclusions: The new MSNS-AT score significantly improved mortality prediction at admission in the whole study group and in preterm infants as compared with the SNS score, suggesting that, besides GA and BW, AT may be decisive for the outcome of outborn preterm infants.

Keywords: mortality; neonatal transportation; newborn; outborn; scoring system; sick neonatal score.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow chart for selecting study groups.
Figure 2
Figure 2
Representation of the number of transferred newborn infants during the study period.
Figure 3
Figure 3
(A) Survival differences between newborns based on the time-to-admission period. (B) Improvement of hazard estimation by adding a time-to-admission variable to the SNS score regression model.
Figure 4
Figure 4
Differences between the SNS score and the novel MSNS-AT score in our study groups: (A) term newborns; (B) the entire group; (C) preterm infants.
Figure 5
Figure 5
The overall risk of mortality assessed with MSNS-AT score.
See this image and copyright information in PMC

References

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