Migration is not the perfect answer: How the cross-talk error correction for multiple breath nitrogen washout (MBWN2 ) parameters differs on directly collected vs. legacy data.

Recently, a cross-talk error with commercial multiple breath nitrogen washout (MBWN2 ) software was discovered, which produced an absolute over-reading of N2 of approximately 1%, i.e., 2% N2 read as 3%. This caused an extended tail to the washout, and over-estimated lung clearance index (LCI2.5 ) values. Subsequently an updated and corrected software version has been released. Within the field there have been discussions on how to correct legacy data, whether to migrate or completely "rerun" raw data A-files from the old software into the new corrected software. To our knowledge, no research has been published assessing whether either method is equivalent to directly collecting data in the new corrected software. We prospectively recruited 19 participants, 10 adult healthy controls and 9 people with cystic fibrosis (CF). MBWN2 was performed using the Exhalyzer® D first on the old 3.1.6 software and next, directly on corrected 3.3.1 software. Multiple breath washout (MBW) data directly collected in 3.3.1 was significantly different from both migrated and rerun data. A total of 7 of the 19 participants (37%; 4 CF) had a relative difference in LCI2.5 > 10% for both migrated and rerun data compared to 3.3.1 collected data. Our findings have implications for the Global Lung Initiative MBW project, which is accepting a combination of directly collected, A-file reruns and migrated data to establish normative values. Further, caution must be used in clinical practice when comparing corrected legacy data versus 3.3.1 collected data for clinical interpretation. We recommend that a new baseline is collected directly on 3.3.1. before clinical interpretation and decisions are determined when comparing consecutive MBW tests.

Computed cardiopulmonography and the idealized lung clearance index, iLCI2.5, in early-stage cystic fibrosis.

This study explored the use of computed cardiopulmonography (CCP) to assess lung function in early-stage cystic fibrosis (CF). CCP has two components. The first is a particularly accurate technique for measuring gas exchange. The second is a computational cardiopulmonary model where patient-specific parameters can be estimated from the measurements of gas exchange. Twenty-five participants (14 healthy controls, 11 early-stage CF) were studied with CCP. They were also studied with a standard clinical protocol to measure the lung clearance index (LCI2.5). Ventilation inhomogeneity, as quantified through CCP parameter σlnCl, was significantly greater (P < 0.005) in CF than in controls, and anatomical deadspace relative to predicted functional residual capacity (DS/FRCpred) was significantly more variable (P < 0.002). Participant-specific parameters were used with the CCP model to calculate idealized values for LCI2.5 (iLCI2.5) where extrapulmonary influences on the LCI2.5, such as breathing pattern, had all been standardized. Both LCI2.5 and iLCI2.5 distinguished clearly between CF and control participants. LCI2.5 values were mostly higher than iLCI2.5 values in a manner dependent on the participant's respiratory rate (r = 0.46, P < 0.05). The within-participant reproducibility for iLCI2.5 appeared better than for LCI2.5, but this did not reach statistical significance (F ratio = 2.2, P = 0.056). Both a sensitivity analysis on iLCI2.5 and a regression analysis on LCI2.5 revealed that these depended primarily on an interactive term between CCP parameters of the form σlnCL*(DS/FRC). In conclusion, the LCI2.5 (or iLCI2.5) probably reflects an amalgam of different underlying lung changes in early-stage CF that would require a multiparameter approach, such as potentially CCP, to resolve.NEW & NOTEWORTHY Computed cardiopulmonography is a new technique comprising a highly accurate sensor for measuring respiratory gas exchange coupled with a cardiopulmonary model that is used to identify a set of patient-specific characteristics of the lung. Here, we show that this technique can improve on a standard clinical approach for lung function testing in cystic fibrosis. Most particularly, an approach incorporating multiple model parameters can potentially separate different aspects of pathological change in this disease.