Functional morphometry: non-invasive estimation of the alveolar surface area in extremely preterm infants

Abstract

BackgroundThe main pathophysiologic characteristic of chronic respiratory disease following extremely premature birth is arrested alveolar growth, which translates to a smaller alveolar surface area (SA). We aimed to use non-invasive measurements to estimate the SA in extremely preterm infants.MethodsPaired measurements of the fraction of inspired oxygen and transcutaneous oxygen saturation were used to calculate the ventilation/perfusion ratio, which was translated to SA using Fick’s law of diffusion. The SA was then adjusted using volumetric capnography.ResultsThirty infants with a median (range) gestational age of 26.3 (22.9–27.9) weeks were studied. The median (range) adjusted SA was 647.9 (316.4–902.7) cm2. The adjusted SA was lower in the infants who required home oxygen [637.7 (323.5–837.5) cm2] compared to those who did not [799.1 (444.2–902.7) cm2, p = 0.016]. In predicting the need for supplemental home oxygen, the adjusted SA had an area under the receiver operator characteristic curve of 0.815 (p = 0.017). An adjusted SA ≥688.6 cm2 had 86% sensitivity and 77% specificity in predicting the need for supplemental home oxygen.ConclusionsThe alveolar surface area can be estimated non-invasively in extremely preterm infants. The adjusted alveolar surface area has the potential to predict the subsequent need for discharge home on supplemental oxygen.ImpactWe describe a novel biomarker of respiratory disease following extremely preterm birth.The adjusted alveolar surface area index was derived by non-invasive measurements of the ventilation/perfusion ratio and adjusted by concurrent measurements of volumetric capnography.The adjusted alveolar surface area was markedly reduced in extremely preterm infants studied at 7 days of life and could predict the need for discharge home on supplemental oxygen.This method could be used at the bedside to estimate the alveolar surface area and provide an index of the severity of lung disease, and assist in monitoring, clinical management and prognosis.