Literature DB >> 22797131

Monitoring breathing rate at home allows early identification of COPD exacerbations.

Aina M Yañez1, Dolores Guerrero2, Rigoberto Pérez de Alejo3, Francisco Garcia-Rio4, Jose Luis Alvarez-Sala5, Miriam Calle-Rubio5, Rosa Malo de Molina6, Manuel Valle Falcones6, Piedad Ussetti6, Jaume Sauleda7, Enrique Zamora García8, Jose Miguel Rodríguez-González-Moro9, Mercedes Franco Gay3, Maties Torrent10, Alvar Agustí11.   

Abstract

BACKGROUND: Respiratory frequency increases during exacerbations of COPD (ECOPD). We hypothesized that this increase can be detected at home before ECOPD hospitalization.
METHODS: To test this hypothesis, respiratory frequency was monitored at home daily for 3 months in 89 patients with COPD (FEV₁, 42.3% ± 14.0%; reference) who were receiving domiciliary oxygen therapy (9.6 ± 4.0 h/d).
RESULTS: During follow-up, 30 patients (33.7%) required hospitalization because of ECOPD. In 21 of them (70%), mean respiratory frequency increased (vs baseline) during the 5 days that preceded it (from 15.2 ± 4.3/min to 19.1 ± 5.9/min, P < .05). This was not the case in patients without ECOPD (16.1 ± 4.8/min vs 15.9 ± 4.9/min). Receiver operating characteristic analysis showed that 24 h before hospitalization, a mean increase of 4.4/min (30% from baseline) provided the best combination of sensitivity (66%) and specificity (93%) (area under the curve [AUC] = 0.79, P < .05). Two days before hospitalization, a mean increase of 2.3/min (15% change from baseline) was associated with a sensitivity of 72% and a specificity of 77% (AUC = 0.76, P < .05).
CONCLUSIONS: Respiratory frequency can be monitored daily at home in patients with COPD receiving domiciliary oxygen therapy. In these patients, breathing rate increases significantly days before they require hospitalization because of ECOPD. This may offer a window of opportunity for early intervention.

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Year:  2012        PMID: 22797131     DOI: 10.1378/chest.11-2728

Source DB:  PubMed          Journal:  Chest        ISSN: 0012-3692            Impact factor:   9.410


  28 in total

1.  Statistical Process Control Improves The Feasibility Of Remote Physiological Monitoring In Patients With Chronic Obstructive Pulmonary Disease.

Authors:  Christopher B Cooper; Worawan Sirichana; Eric V Neufeld; Michael Taylor; Xiaoyan Wang; Brett A Dolezal
Journal:  Int J Chron Obstruct Pulmon Dis       Date:  2019-11-13

2.  Investigation of respiratory rate in patients with cystic fibrosis using a minimal-impact biomotion system.

Authors:  Svenja Straßburg; Carolin-Maria Linker; Sebastian Brato; Christoph Schöbel; Christian Taube; Jürgen Götze; Florian Stehling; Sivagurunathan Sutharsan; Matthias Welsner; Gerhard Weinreich
Journal:  BMC Pulm Med       Date:  2022-02-11       Impact factor: 3.317

3.  Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review.

Authors:  Daniel Sanchez-Morillo; Miguel A Fernandez-Granero; Antonio Leon-Jimenez
Journal:  Chron Respir Dis       Date:  2016-04-20       Impact factor: 2.444

4.  A proof of concept for continuous, non-invasive, free-living vital signs monitoring to predict readmission following an acute exacerbation of COPD: a prospective cohort study.

Authors:  Grace Hawthorne; Matthew Richardson; Neil J Greening; Dale Esliger; Samuel Briggs-Price; Emma J Chaplin; Lisa Clinch; Michael C Steiner; Sally J Singh; Mark W Orme
Journal:  Respir Res       Date:  2022-04-26

5.  Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD.

Authors:  Miguel Angel Fernandez-Granero; Daniel Sanchez-Morillo; Antonio Leon-Jimenez
Journal:  Sensors (Basel)       Date:  2015-10-23       Impact factor: 3.576

6.  Monitoring of Physiological Parameters to Predict Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): A Systematic Review.

Authors:  Ahmed M Al Rajeh; John R Hurst
Journal:  J Clin Med       Date:  2016-11-25       Impact factor: 4.241

7.  Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network.

Authors:  Hamid Mcheick; Lokman Saleh; Hicham Ajami; Hafedh Mili
Journal:  Sensors (Basel)       Date:  2017-06-23       Impact factor: 3.576

8.  Novel insights in cough and breathing patterns of patients with idiopathic pulmonary fibrosis performing repeated 24-hour-respiratory polygraphies.

Authors:  Anke Schertel; Manuela Funke-Chambour; Thomas Geiser; Anne-Kathrin Brill
Journal:  Respir Res       Date:  2017-11-13

9.  Full Respiration Rate Monitoring Exploiting Doppler Information with Commodity Wi-Fi Devices.

Authors:  Chendan Dou; Hao Huan
Journal:  Sensors (Basel)       Date:  2021-05-18       Impact factor: 3.576

10.  Machine-learning based feature selection for a non-invasive breathing change detection.

Authors:  Juliana Alves Pegoraro; Sophie Lavault; Nicolas Wattiez; Thomas Similowski; Jésus Gonzalez-Bermejo; Etienne Birmelé
Journal:  BioData Min       Date:  2021-07-18       Impact factor: 2.522

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