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. 2025 Apr 30;14(4):597-607.
doi: 10.21037/tp-2024-563. Epub 2025 Apr 27.

Predictive value of the combined application of multiple critical illness scoring systems in neonatal respiratory distress syndrome

Liqin Yan #  1 , Jiayang Mao #  2 , Qin Li  3 , Tingting Yu  4 , Lianghua Lu  4
Affiliations

Predictive value of the combined application of multiple critical illness scoring systems in neonatal respiratory distress syndrome

Liqin Yan et al. Transl Pediatr. .

Abstract

Background: As a severe complication in premature birth, neonatal respiratory distress syndrome (NRDS) significantly threatens the health and life of the neonate. Therefore, the early identification of the severity and mortality risk of NRDS is considered crucial. This study aimed to compare the predictive roles of the neonatal critical illness score (NCIS), the Score for Neonatal Acute Physiology-II (SNAP-II), and the Score for Neonatal Acute Physiology with Perinatal Extension-II (SNAPPE-II) in predicting comorbidities and mortality in NRDS infants.

Methods: A total of 192 infants diagnosed with NRDS at the neonatal intensive care unit (NICU) of the Children's Hospital of Soochow University between January 2019 and August 2020 were included. Based on discharge outcomes, they were divided into the survival and mortality groups, as well as the complication group and non-complication group. The individual or combination of the NCIS, SNAP-II, and SNAPPE-II scoring systems were applied to evaluate the survival and comorbidities in all infants. Receiver operating characteristic (ROC) curves were generated to compare the area under the curve (AUC) values.

Results: All the three scoring systems exhibited good efficacy in predicting mortality among NRDS infants, with NCIS showing superior performance compared to SNAPPE-II, which was more effective than SNAP-II (P<0.001). The predictive efficacy of the three scoring systems was enhanced when combined in pairs or collectively, with the combination of NCIS and SNAPPE-II exhibiting the highest predictive performance (P<0.001). The three scoring systems were significantly effective in predicting the risk of comorbidities in NRDS (P<0.001).

Conclusions: The NCIS, SNAP-II, and SNAPPE-II scoring systems demonstrate good predictive value for assessing the condition and prognosis of NRDS infants. The combination of NCIS with SNAP-II and SNAPPE-II exhibits high predictive efficacy for estimating mortality in NRDS.

Keywords: Neonatal critical illness score (NCIS); Score for Neonatal Acute Physiology with Perinatal Extension-II (SNAPPE-II); Score for Neonatal Acute Physiology-II (SNAP-II); infant; neonatal respiratory distress syndrome (NRDS).

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2024-563/coif). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
ROC curves for predicting mortality risk in children with NRDS using three scoring methods. NCIS, neonatal critical illness score; NRDS, neonatal respiratory distress syndrome; ROC, receiver operating characteristic; SNAP-II, Score for Neonatal Acute Physiology-II; SNAPPE-II, Score for Neonatal Acute Physiology with Perinatal Extension-II.
Figure 2
Figure 2
ROC curve for predicting the risk of death in children with NRDS using a combination of three scoring methods. NCIS, neonatal critical illness score; NRDS, neonatal respiratory distress syndrome; ROC, receiver operating characteristic; SNAP-II, Score for Neonatal Acute Physiology-II; SNAPPE-II, Score for Neonatal Acute Physiology with Perinatal Extension-II.
Figure 3
Figure 3
ROC curves for predicting the risk of comorbidities in NRDS infants using three scoring methods. NCIS, neonatal critical illness score; NRDS, neonatal respiratory distress syndrome; ROC, receiver operating characteristic; SNAP-II, Score for Neonatal Acute Physiology-II; SNAPPE-II, Score for Neonatal Acute Physiology with Perinatal Extension-II.
See this image and copyright information in PMC

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