iPREDICT: proof-of-concept study to develop a predictive model of changes in asthma control.
Ther Adv Respir Dis
; 18: 17534666241266186, 2024.
Article
em En
| MEDLINE
| ID: mdl-39082063
ABSTRACT
BACKGROUND:
The individualized PREdiction of DIsease Control using digital sensor Technology (iPREDICT) program was developed for asthma management using digital technology. Devices were integrated into daily lives of patients to establish a predictive model of asthma control by measuring changes from baseline health status with minimal device burden.OBJECTIVES:
To establish baseline disease characteristics of the study participants, detect changes from baseline associated with asthma events, and evaluate algorithms capable of identifying triggers and predicting asthma control changes from baseline data. Patient experience and compliance with the devices were also explored.DESIGN:
This was a multicenter, observational, 24-week, proof-of-concept study conducted in the United States.METHODS:
Patients (⩾12 years) with severe, uncontrolled asthma engaged with a spirometer, vital sign monitor, sleep monitor, connected inhaler devices, and two mobile applications with embedded patient-reported outcome (PRO) questionnaires. Prospective data were linked to data from electronic health records and transmitted to a secure platform to develop predictive algorithms. The primary endpoint was an asthma event symptom worsening logged by patients (PRO); peak expiratory flow (PEF) < 65% or forced expiratory volume in 1 s < 80%; increased short-acting ß2-agonist (SABA) use (>8 puffs/24 h or >4 puffs/day/48 h). For each endpoint, predictive models were constructed at population, subgroup, and individual levels.RESULTS:
Overall, 108 patients were selected 66 (61.1%) completed and 42 (38.9%) were excluded for failure to respond/missing data. Predictive accuracy depended on endpoint selection. Population-level models achieved low accuracy in predicting endpoints such as PEF < 65%. Subgroups related to specific allergies, asthma triggers, asthma types, and exacerbation treatments demonstrated high accuracy, with the most accurate, predictive endpoint being >4 SABA puffs/day/48 h. Individual models, constructed for patients with high endpoint overlap, exhibited significant predictive accuracy, especially for PEF < 65% and >4 SABA puffs/day/48 h.CONCLUSION:
This multidimensional dataset enabled population-, subgroup-, and individual-level analyses, providing proof-of-concept evidence for development of predictive models of fluctuating asthma control.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Asma
/
Estudo de Prova de Conceito
Limite:
Adolescent
/
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Ther Adv Respir Dis
Assunto da revista:
PNEUMOLOGIA
/
TERAPEUTICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos