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.
Expert Rev Pharmacoecon Outcomes Res
; 24(6): 759-771, 2024 Jul.
Article
en En
| MEDLINE
| ID: mdl-38458615
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.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
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Nebulizadores y Vaporizadores
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Fibrosis Quística
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Cumplimiento de la Medicación
Límite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Expert Rev Pharmacoecon Outcomes Res
Asunto de la revista:
FARMACOLOGIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Reino Unido