Computed cardiopulmonography and the idealized lung clearance index, iLCI(2.5), 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 (LCI(2.5)). 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 LCI(2.5) (iLCI(2.5)) where extrapulmonary influences on the LCI(2.5), such as breathing pattern, had all been standardized. Both LCI(2.5) and iLCI(2.5) distinguished clearly between CF and control participants. LCI(2.5) values were mostly higher than iLCI(2.5) values in a manner dependent on the participant’s respiratory rate (r = 0.46, P < 0.05). The within-participant reproducibility for iLCI(2.5) appeared better than for LCI(2.5), but this did not reach statistical significance (F ratio = 2.2, P = 0.056). Both a sensitivity analysis on iLCI(2.5) and a regression analysis on LCI(2.5) revealed that these depended primarily on an interactive term between CCP parameters of the form σlnCL*(DS/FRC). In conclusion, the LCI(2.5) (or iLCI(2.5)) 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.