Abstract
BACKGROUND: The diagnostic value of tidal breathing (TB) measurements in infants is controversially discussed. The aim of this study was to investigate to what extent the breathing pattern of sleeping infants with chronic lung diseases (CLD) differ from healthy controls with the same postconceptional age and to assess the predictive value of TB parameters. METHODS: In the age of 36-42 postconceptional weeks TB measurements were performed in 48 healthy newborns (median age and weight 7d, 3100 g) and 48 infants with CLD (80d, 2465 g)) using the deadspace-free flow-through technique. Once the infants had adapted to the mask and were sleeping quietly and breathing regularly, 20-60 breathing cycles were evaluated. Beside the shape of the tidal breathing flow-volume loop (TBFVL) 18 TB parameters were analyzed using ANOVA with Bonferroni correction. Receiver-operator characteristic (ROC) curves were calculated to investigate the discriminative ability of TB parameters. RESULTS: The incidence of concave expiratory limbs in CLD infants was 31% and significantly higher compared to controls (2%) (p < 0.001). Significant differences between CLD infants and controls were found in 11/18 TB parameters. The largest differences were seen in the mean (SD) inspiratory time 0.45(0.11)s vs. 0.65(0.14)s (p < 0.0001) and respiratory rate (RR) 55.4(14.2)/min vs. 39.2(8.6)/min (p < 0.0001) without statistically significant difference in the discriminative power between both time parameters. Most flow parameters were strongly correlated with RR so that there is no additional diagnostic value. No significant differences were found in the tidal volume and commonly used TB parameters describing the expiratory flow profile. CONCLUSION: The breathing pattern of CLD infants differs significantly from that of healthy controls. Concave TBFVL and an increased RR measured during quiet sleep and under standardized conditions may indicate diminished respiratory functions in CLD infants whereas most of the commonly used TB parameters are poorly predictive.