COPD · 2025
Can Passive Cough Monitoring Predict COPD Exacerbations?
СтатьяВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Morice A H, den Brinker A C, Crooks M, Thackray-Nocera S, Ouweltjes O, Rietman R
Аннотация
PURPOSE: Validation of an alert mechanism for COPD exacerbations based on coughing detected by a stationary unobtrusive nighttime monitor.
METHODS: This prospective double-blind longitudinal study of cough monitoring included 40 chronic obstructive pulmonary disease (COPD) patients. Participants underwent cough monitoring and completed a daily questionnaire for 12 weeks. If no exacerbation occurred within that period patients were asked to continue being monitored for a further 12 weeks. The automated system identified deteriorating trends in cough based on a personalized cough classifier and the alerts were compared with patient reported exacerbation onsets.
RESULTS: Thirty-eight patients [median age 72 (range 57-84)], median FEV-1% predicted 43% (range 20-106%) completed the study and had 41 exacerbations over a total of 3981 days. For 32 patients, the cough monitor data allowed classifier personalization, trend analysis, and alert generation. Based on the trend data, it is estimated that ∼30% of exacerbations are not associated with an increase in cough. The alert mechanism flagged 59% of the exacerbations. For the cases with alerts preceding the onset, the associated lead time was 4 days or more.
CONCLUSION: Though based on a single variable only, the cough-based alert system captured more than half of the exacerbations in a passive, free-living scenario. No adherence issues were reported, and patients confirmed the unobtrusive and hassle-free nature of the approach.
METHODS: This prospective double-blind longitudinal study of cough monitoring included 40 chronic obstructive pulmonary disease (COPD) patients. Participants underwent cough monitoring and completed a daily questionnaire for 12 weeks. If no exacerbation occurred within that period patients were asked to continue being monitored for a further 12 weeks. The automated system identified deteriorating trends in cough based on a personalized cough classifier and the alerts were compared with patient reported exacerbation onsets.
RESULTS: Thirty-eight patients [median age 72 (range 57-84)], median FEV-1% predicted 43% (range 20-106%) completed the study and had 41 exacerbations over a total of 3981 days. For 32 patients, the cough monitor data allowed classifier personalization, trend analysis, and alert generation. Based on the trend data, it is estimated that ∼30% of exacerbations are not associated with an increase in cough. The alert mechanism flagged 59% of the exacerbations. For the cases with alerts preceding the onset, the associated lead time was 4 days or more.
CONCLUSION: Though based on a single variable only, the cough-based alert system captured more than half of the exacerbations in a passive, free-living scenario. No adherence issues were reported, and patients confirmed the unobtrusive and hassle-free nature of the approach.