Development of data processing algorithm to calculate adherence for adults with cystic fibrosis using inhaled therapy – a multi-center observational study within the CFHealthHub learning health system

ABSTRACT

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

Article highlights

• Supporting adherence to medicine regimens in long-term conditions requires accurate measurement of adherence.

• The CFHealthHub Learning Health System offers a digital platform which can collect inhaled medicine usage data from nebulizer devices capable of electronic data capture.

• Clinicians and people with cystic fibrosis collaborated to develop a data processing algorithm for these usage data to calculate the number of complete doses taken each day (‘daily complete dose count’).

• The resultant data processing algorithm was considered highly accurate for calculating the “daily complete dose count.”

• Accurate nebulizer usage data processing allows for calculation of accurate adherence measurement, which can be used as both a digital study endpoint in but also as part of optimizing routine care.

Declaration of interest

R D Sandler has been a sponsored delegate at conferences by Lilly & UCB and has also received an educational bursary supported by Chiesi and the European Cystic Fibrosis Charity. S Cameron has undertaken consultant work with Gilead. M J Wildman declares unconditional funding for research support from PARI GmbH. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Part of this work was presented as a poster at the 45th European Cystic Fibrosis Society Annual Conference, Rotterdam, the Netherlands from 7 − 11 July 2022.

Author contributions

Z H Hoo, R D Sandler, L Lai & S Dawson conceptualized and designed this project. R D Sandler, S Dawson, S Cameron and A Lynam worked with participants to collect data. R D Sandler, Z H Hoo & L Lai analyzed and interpreted the data. R D Sandler drafted the paper and revised it in accordance with critical revisions providing important intellectual content from all authors. All authors approved the final submission and agreed to be accountable for all aspects of the work.

Acknowledgments

We thank the people with CF who participated in this work for their time and insights. No specific funding was obtained for this work, but CFHealthHub is supported by grants from the CF Foundation and NHS England. The final version of the manuscript was reviewed by Dr Carola Fuchs, PARI GmBH, solely to confirm the correct nomenclature of PARI devices and associated data processing terminology. This had no impact on the scientific content of this work.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.