Literature DB >> 20234139

Lamellar body count and stable microbubble test on gastric aspirates from preterm infants for the diagnosis of respiratory distress syndrome.

Inah Westphal Batista da Silva Daniel1, Humberto Holmer Fiori, Jefferson Pedro Piva, Terezinha Paz Munhoz, André Valiente Nectoux, Renato Machado Fiori.   

Abstract

BACKGROUND: Lamellar body count (LBC) in amniotic fluid is being used to identify infants at risk of respiratory distress syndrome (RDS) who would benefit from surfactant prophylaxis or very early therapy. The test in gastric aspirates of newborns has not been properly explored.
OBJECTIVE: The main objective of this research was to evaluate the performance of LBC alone or in combination with the stable microbubble test (SMT), done on gastric aspirates from preterm babies to predict RDS.
METHODS: A total of 34 preterm infants with RDS and 29 without RDS, with a gestational age between 24 and 34 weeks, were included in the study. Gastric fluid was collected in the delivery room. A diluent (dithiothreitol) allowed all samples to be processed, even the thickest and non-homogeneous ones, without centrifugation. The SMT was done for comparison.
RESULTS: The best cut-off value was <42,000 lamellar bodies/microl to predict RDS, with a sensitivity of 92% (95% CI 73-100%) and specificity of 86% (95% CI 77-95%). The area under the receiver-operating characteristic curve was 0.928 (95% CI 0.86-0.99). SMT showed similar results. LBC and SMT together in series (positive result if both tests were positive) showed a sensitivity of 100% and a specificity of 86%.
CONCLUSION: LBC on gastric aspirates diluted in a solution of dithiothreitol can be rapidly and easily performed, and may be used alone or in combination with SMT as a predictor of RDS, allowing selective prophylaxis or very early treatment only in surfactant-deficient newborns. Copyright 2010 S. Karger AG, Basel.

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Year:  2010        PMID: 20234139     DOI: 10.1159/000279887

Source DB:  PubMed          Journal:  Neonatology        ISSN: 1661-7800            Impact factor:   4.035


  7 in total

1.  Gastric fluid versus amniotic fluid analysis for the identification of intra-amniotic infection due to Ureaplasma species.

Authors:  Sun Min Kim; Roberto Romero; JoonHo Lee; Piya Chaemsaithong; Nikolina Docheva; Bo Hyun Yoon
Journal:  J Matern Fetal Neonatal Med       Date:  2015-12-02

Review 2.  Preventing Continuous Positive Airway Pressure Failure: Evidence-Based and Physiologically Sound Practices from Delivery Room to the Neonatal Intensive Care Unit.

Authors:  Clyde J Wright; Laurie G Sherlock; Rakesh Sahni; Richard A Polin
Journal:  Clin Perinatol       Date:  2018-02-28       Impact factor: 3.430

3.  Very Preterm Infants Failing CPAP Show Signs of Fatigue Immediately after Birth.

Authors:  Melissa L Siew; Jeroen J van Vonderen; Stuart B Hooper; Arjan B te Pas
Journal:  PLoS One       Date:  2015-06-08       Impact factor: 3.240

4.  Predicting respiratory distress syndrome at birth using a fast test based on spectroscopy of gastric aspirates: 2. Clinical part.

Authors:  Christian Heiring; Henrik Verder; Peter Schousboe; Torben E Jessen; Lars Bender; Finn Ebbesen; Marianne Dahl; Christian Eschen; Jesper Fenger-Grøn; Agnar Höskuldsson; Morgaine Matthews; Jes Reinholdt; Nikolaos Scoutaris; Heidi Smedegaard
Journal:  Acta Paediatr       Date:  2019-05-31       Impact factor: 2.299

Review 5.  Diagnostic accuracy of lamellar body count as a predictor of fetal lung maturity: A systematic review and meta-analysis.

Authors:  Ahmed Mahmoud Abdou; Mohammad S Badr; Khaled F Helal; Mohamed E Rafeek; Amr A Abdelrhman; Mahmoud Kotb
Journal:  Eur J Obstet Gynecol Reprod Biol X       Date:  2019-05-31

6.  Oscillatory mechanics at birth for identifying infants requiring surfactant: a prospective, observational trial.

Authors:  Anna Lavizzari; Chiara Veneroni; Francesco Beretta; Valeria Ottaviani; Claudia Fumagalli; Marta Tossici; Mariarosa Colnaghi; Fabio Mosca; Raffaele L Dellacà
Journal:  Respir Res       Date:  2021-12-20

7.  Prediction of Neonatal Respiratory Distress Biomarker Concentration by Application of Machine Learning to Mid-Infrared Spectra.

Authors:  Waseem Ahmed; Aneesh Vincent Veluthandath; David J Rowe; Jens Madsen; Howard W Clark; Anthony D Postle; James S Wilkinson; Ganapathy Senthil Murugan
Journal:  Sensors (Basel)       Date:  2022-02-23       Impact factor: 3.576

  7 in total

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