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Related Experiment Video

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Murine Model of Allergen Induced Asthma
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iPREDICT: proof-of-concept study to develop a predictive model of changes in asthma control.

Mario Castro1, Merrill Zavod2, Annika Rutgersson3

  • 1Chief, Division of Pulmonary, Critical Care and Sleep Medicine, Vice Chair for Clinical and Translational Research, University of Kansas School of Medicine, 4000 Cambridge Street, Mailstop 3007, Kansas City, KS 66160, USA.

Therapeutic Advances in Respiratory Disease
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PubMed
Summary
This summary is machine-generated.

Digital sensors and mobile apps helped predict asthma control changes in patients. Subgroup and individual models showed significant accuracy, especially for predicting increased short-acting beta-agonist use.

Keywords:
algorithmasthma controldigital toolmobile applicationprecision medicineself-managementsevere asthma

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Area of Science:

  • Digital health technologies
  • Asthma management
  • Predictive modeling

Background:

  • The individualized PREdiction of DIsease Control using digital sensor Technology (iPREDICT) program utilizes digital tools for asthma management.
  • Devices are integrated into patients' daily lives to monitor health status changes with minimal burden.

Purpose of the Study:

  • Establish baseline asthma characteristics.
  • Detect health status changes associated with asthma events.
  • Evaluate algorithms for trigger identification and prediction of asthma control fluctuations.
  • Explore patient experience and device compliance.

Main Methods:

  • A 24-week, multicenter, observational study in the US.
  • Included patients aged 12+ with severe, uncontrolled asthma.
  • Utilized spirometers, vital sign monitors, sleep monitors, connected inhalers, and mobile apps with patient-reported outcome questionnaires.
  • Linked prospective data with electronic health records for algorithm development.
  • Defined asthma events based on symptoms, peak expiratory flow (PEF), forced expiratory volume in 1s (FEV1), and short-acting beta-agonist (SABA) use.

Main Results:

  • Out of 108 patients, 66 completed the study.
  • Predictive accuracy varied by endpoint; population-level models showed low accuracy for PEF <65%.
  • Subgroup models demonstrated high accuracy for specific allergies, triggers, and asthma types.
  • The most accurate predictive endpoint was >4 SABA puffs/day/48h.
  • Individual models showed significant accuracy for PEF <65% and >4 SABA puffs/day/48h.

Conclusions:

  • Multidimensional datasets enabled population, subgroup, and individual analyses.
  • Provided proof-of-concept for developing predictive models of fluctuating asthma control.
  • Highlights the potential of digital health technology in personalized asthma management.