Objectives: To develop a robust algorithm to accurately calculate 'daily complete dose counts' for inhaled medicines, used in percent adherence calculations, from electronically-captured nebulizer data within the CFHealthHub Learning Health System.
Methods: A multi-center, cross-sectional study involved participants and clinicians reviewing real-world inhaled medicine usage records and triangulating them with objective nebulizer data to establish a consensus on 'daily complete dose counts.' An algorithm, which used only objective nebulizer data, was then developed using a derivation dataset and evaluated using internal validation dataset. The agreement and accuracy between the algorithm-derived and consensus-derived 'daily complete dose counts' was examined, with the consensus-derived count as the reference standard.
Results: Twelve people with CF participated. The algorithm derived a 'daily complete dose count' by screening out 'invalid' doses (those <60s in duration or run in cleaning mode), combining all doses starting within 120s of each other, and then screening out all doses with duration < 480s which were interrupted by power supply failure. The kappa co-efficient was 0.85 (0.71-0.91) in the derivation and 0.86 (0.77-0.94) in the validation dataset.
Conclusions: The algorithm demonstrated strong agreement with the participant-clinician consensus, enhancing confidence in CFHealthHub data. Publishingdata processing methods can encourage trust in digital endpoints and serve as an exemplar for other projects.
Keywords: Adherence; CFHealthHub; cystic fibrosis; data processing; digital endpoints; inhaled therapy; learning health system; nebulizer.