P194 Computed cardiopulmonography (CCP) and the idealised lung clearance index (iLCI) in early-stage cystic fibrosis

LCI is a sensitive parameter to track CF lung disease but is influenced by extra-pulmonary factors such as equipment deadspace, breathing volume and rate which can reduce accuracy. This study explores a novel approach, CCP, to calculate an idealised LCI (iLCI). CCP utilises two components: (a) a novel, accurate laser gas analyser (LGA; Smith NMJ et al. Sci Rep. 2021) and (b)a lung model with extra-pulmonary factors removed. 14 healthy controls (HC) and 11 pwCF performed same day multiple breath washout (MBW; Exhalyser D), CCP and spirometry. CCP protocol: 10 min of medical air-breathing followed by 5 min of 100% O2. Collected LGA data is run through the lung model reflecting the physiology of the individual which allows multiple simulations of an MBW protocol. CCP parameters: iLCI, ventilation inhomogeneity (slnCL), anatomical deadspace (VDI) and functional residual capacity (FRC). Mean age was 25(SD±8) yrs. PwCF had a preserved ppFEV1 (86 ± 10%). In HCs, inter-subject variance was significantly smaller for iLCI than LCI (F-ratio = 2.57, P < 0.05). LCI and iLCI were strongly correlated (Spearman’s: rs 0.79, P < 0.0001), but iLCI tended to be lower (Bland-Altman analysis:Bias –1.14 ± 0.14, 95% limits of agreement –0.86 to –1.42). LCI and iLCI discriminated HC from CF (both P < 0.01), but only iLCI showed no overlap in group values. Sensitivity (iLCI) and regression (LCI) analyses revealed the best predictor of a subject’s LCI was interaction between slnCL*VDI/FRC (F-ratio 256.2, P < 0.0001). iLCI appears to improve the reliability of LCI. It is an incorrect assumption that LCI is solely a measure of ventilation inhomogeneity. This lung model demonstrates that the interaction between gas mixing efficiency, ventilation inhomogeneity and anatomical deadspace has the greatest impact on the final LCI value. CCP can separate different aspects of lung disease which may lead to more personalised diagnostics and therapy. Funded by EPSRC and CF Trust.