Clinical and Translational Science (Mar 2021)

Remote FEV1 Monitoring in Asthma Patients: A Pilot Study

  • Chengrui Huang,
  • Elena S. Izmailova,
  • Natalie Jackson,
  • Robert Ellis,
  • Gaurav Bhatia,
  • Marcella Ruddy,
  • Dave Singh

DOI
https://doi.org/10.1111/cts.12901
Journal volume & issue
Vol. 14, no. 2
pp. 529 – 535

Abstract

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Forced expiratory volume in one second (FEV1) is a critical parameter for the assessment of lung function for both clinical care and research in patients with asthma. While asthma is defined by variable airflow obstruction, FEV1 is typically assessed during clinic visits. Mobile spirometry (mSpirometry) allows more frequent measurements of FEV1, resulting in a more continuous assessment of lung function over time and its variability. Twelve patients with moderate asthma were recruited in a single‐center study and were instructed to perform pulmonary function tests at home twice daily for 28 days and weekly in the clinic. Daily and mean subject compliances were summarized. The agreement between clinic and mobile FEV1 was assessed using correlation and Bland‐Altman analyses. The test‐retest reliability for clinic and mSpirometry was assessed by interclass correlation coefficient (ICC). Simulation was conducted to explore if mSpirometry could improve statistical power over clinic counterparts. The mean subject compliance with mSpirometry was 70% for twice‐daily and 85% for at least once‐daily. The mSpirometry FEV1 were highly correlated and agreed with clinic ones from the same morning (r = 0.993) and the same afternoon (r = 0.988) with smaller mean difference for the afternoon (0.0019 L) than morning (0.0126 L) measurements. The test‐retest reliability of mobile (ICC = 0.932) and clinic (ICC = 0.942) spirometry were comparable. Our simulation analysis indicated greater power using dense mSpirometry than sparse clinic measurements. Overall, we have demonstrated good compliance for repeated at‐home mSpirometry, high agreement and comparable test‐retest reliability with clinic counterparts, greater statistical power, suggesting a potential for use in asthma clinical research.