BRIEF RESEARCH REPORT article
Front. Pediatr.
Sec. Neonatology
Spectral analysis of gastric aspirates obtained shortly after birth predicts the need for prolonged respiratory support in neonates in a development cohort
Provisionally accepted
- Mayo Clinic, Rochester, United States
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Introduction: Spectral analysis of gastric aspirates obtained shortly after birth predicts the diagnosis of respiratory distress syndrome in neonates born <32 completed weeks gestation. We sought to determine whether this prototype point-of-care device measuring surfactant components in gastric aspirates could predict prolonged respiratory support needs in neonates 30 completed weeks gestation. Methods: Gastric aspirates obtained within 30 minutes of birth were analyzed by spectroscopy to quantify surfactant components. These spectral data were entered into an existing algorithm to assess subjects' biochemical lung maturity. This algorithmic output was paired with clinical data to evaluate the performance of the algorithm in predicting subjects' need for respiratory support at six hours of life (prolonged respiratory support). Each element of the algorithm was adjusted via a machine learning framework to optimize predictive performance. Results: Gastric aspirates from 179 subjects (median 36 weeks, range 31-41 weeks) were eligible for analysis. Spectral analysis of gastric aspirates predicted the need for prolonged respiratory support with 70% sensitivity and 92% specificity. Positive-and negative-predictive values were 86% and 82%, respectively, for the overall cohort. Among gestational age subgroups, positive prediction was highest among moderately preterm neonates (32-33 weeks), while negative prediction was highest among term neonates. Discussion: Spectral analysis of surfactant components contained in the gastric fluid of neonates 30 completed weeks gestation predicts the need for prolonged respiratory support with good performance. Predictive performance varied according to subjects' gestational age at birth, suggesting that gestational age-specific algorithms may improve the performance of this point-of-care diagnostic test.
Keywords: Newborn infant, neonatal respiratory distress syndrome, transient tachypnea of thenewborn, Point-of-care diagnostics, artificial intelligence, machine learning, respiratorytherapy
Received: 15 Aug 2025; Accepted: 21 Nov 2025.
Copyright: © 2025 Leigh, Liedl, Amsbaugh and Carey. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Brianna Leigh, leigh.brianna@mayo.edu
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