Quantitative lung ultrasound detects dynamic changes in lung recruitment in the preterm lamb

Abstract

Abstract Background Lung ultrasound (LUS) may not detect small, dynamic changes in lung volume. Mean greyscale measurement using computer-assisted image analysis (Q-LUSMGV) may improve the precision of these measurements. Methods Preterm lambs (n = 40) underwent LUS of the dependent or non-dependent lung during static pressure–volume curve mapping. Total and regional lung volumes were determined using the super-syringe technique and electrical impedance tomography. Q-LUSMGV and gold standard measurements of lung volume were compared in 520 images. Results Dependent Q-LUSMGV moderately correlated with total lung volume (rho = 0.60, 95% CI 0.51–0.67) and fairly with right whole (rho = 0.39, 0.27–0.49), central (rho = 0.38, 0.27–0.48), ventral (rho = 0.41, 0.31–0.51) and dorsal regional lung volumes (rho = 0.32, 0.21–0.43). Non-dependent Q-LUSMGV moderately correlated with total lung volume (rho = 0.57, 0.48–0.65) and fairly with right whole (rho = 0.43, 0.32–0.52), central (rho = 0.46, 0.35–0.55), ventral (rho = 0.36, 0.25–0.47) and dorsal lung volumes (rho = 0.36, 0.25–0.47). All correlation coefficients were statistically significant. Distinct inflation and deflation limbs, and sonographic pulmonary hysteresis occurred in 95% of lambs. The greatest changes in Q-LUSMGV occurred at the opening and closing pressures. Conclusion Q-LUSMGV detected changes in total and regional lung volume and offers objective quantification of LUS images, and may improve bedside discrimination of real-time changes in lung volume. Impact Lung ultrasound (LUS) offers continuous, radiation-free imaging that may play a role in assessing lung recruitment but may not detect small changes in lung volume. Mean greyscale image analysis using computer-assisted quantitative LUS (Q-LUSMGV) moderately correlated with changes in total and regional lung volume. Q-LUSMGV identified opening and closing pressure and pulmonary hysteresis in 95% of lambs. Computer-assisted image analysis may enhance LUS estimation of lung recruitment at the bedside. Future research should focus on improving precision prior to clinical translation.