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1.
ERJ Open Res ; 9(6)2023 Nov.
Artículo en Inglés | MEDLINE | ID: mdl-38152082

RESUMEN

Background: Domiciliary spirometry (DS) is a novel tool that is widely employed in the assessment of respiratory disease. We assessed real-world feasibility, effectiveness and value of a physiologist-led home spirometry programme in patients with treatment-refractory severe asthma. Methods: Patients were referred and provided with a hand-held DS device. Patients completed baseline measurements in a physiologist-led virtual clinic and were instructed to provide further values during any periods of respiratory symptoms. Outcome measures included prevalence of new obstructed events, DS adherence and uptake of this approach. Results: 112 patients were enrolled from November 2020 to January 2023. 102 individuals, mean±sd age 44±13 years (86% female) with median (IQR) forced expiratory volume in 1 s % predicted 88% (77-97%), successfully recorded baseline spirometry values. During follow-up (24 months), 11 (11%) were identified with new obstructive spirometry and were subsequently able to be commenced on biologic therapy. Patient engagement was poor with median (IQR) of 4 (2-6) attempts of contact made before baseline values were recorded, and 2 (1-3) attempts required to record technically acceptable values. Continued DS use was suboptimal; 34% failed to use their device after baseline and only 10% continued at the end of the study period. The cost of DS measurements was greater than a single hospital-based visit but enables multiple event capture. Conclusion: Overall, DS measurement uptake was poor, with a minority of patients continuing to use the device at the end of the study period. However, for those that engage, DS provides an alternative approach to traditional hospital-based spirometry measurements that can alter clinical management.

2.
ERJ Open Res ; 9(4)2023 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-37362883

RESUMEN

Post-COVID-19 breathing pattern disorder can be characterised by application of nonlinear statistical modelling of exercise ventilatory data https://bit.ly/3WlBc7e.

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