Literature DB >> 25582449

Parameters recorded by software of non-invasive ventilators predict COPD exacerbation: a proof-of-concept study.

Jean-Christian Borel1, Julie Pelletier2, Nellie Taleux3, Amandine Briault4, Nathalie Arnol1, Christophe Pison5, Renaud Tamisier6, Jean-François Timsit7, Jean-Louis Pepin6.   

Abstract

OBJECTIVE: To assess whether daily variations in three parameters recorded by non-invasive ventilation (NIV) software (respiratory rate (RR), percentage of respiratory cycles triggered by the patient (%Trigg) and NIV daily use) predict the risk of exacerbation in patients with chronic obstructive pulmonary disease (COPD) treated by home NIV.
METHODS: Patients completed the EXACT-Pro questionnaire daily to detect exacerbations. The 25th and 75th percentiles of each 24 h NIV parameter were calculated and updated daily. For a given day, when the value of any parameter was >75th or <25th percentile, the day was marked as 'abnormal value' ('high value' >75th, 'low value' <25th). Stratified conditional logistic regressions estimated the risk of exacerbation when ≥2 days (for RR and %Trigg) or ≥3 days (for NIV use) out of five had an 'abnormal value'.
RESULTS: Sixty-four patients were included. Twenty-one exacerbations were detected and medically confirmed. The risk of exacerbation was increased when RR (OR 5.6, 95% CI 1.4 to 22.4) and %Trigg (OR 4.0, 95% CI 1.1 to 14.5) were considered as 'high value' on ≥2 days out of five.
CONCLUSIONS: This proof-of-concept study shows that daily variations in RR and %Trigg are predictors of an exacerbation. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Entities:  

Keywords:  COPD Exacerbations; Non invasive ventilation

Mesh:

Year:  2015        PMID: 25582449     DOI: 10.1136/thoraxjnl-2014-206569

Source DB:  PubMed          Journal:  Thorax        ISSN: 0040-6376            Impact factor:   9.139


  14 in total

1.  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

2.  Using domiciliary non-invasive ventilator data downloads to inform clinical decision-making to optimise ventilation delivery and patient compliance.

Authors:  Stephanie K Mansell; Steven Cutts; Isobel Hackney; Martin J Wood; Kevin Hawksworth; Dean D Creer; Cherry Kilbride; Swapna Mandal
Journal:  BMJ Open Respir Res       Date:  2018-03-03

3.  Is the 2013 American Thoracic Society CPAP-tracking system algorithm useful for managing non-adherence in long-term CPAP-treated patients?

Authors:  Marie-Caroline Rotty; Jean-Pierre Mallet; Carey M Suehs; Christian Martinez; Jean-Christian Borel; Claudio Rabec; Arnaud Bourdin; Nicolas Molinari; Dany Jaffuel
Journal:  Respir Res       Date:  2019-09-12

4.  Remote Patient Monitoring Technologies for Predicting Chronic Obstructive Pulmonary Disease Exacerbations: Review and Comparison.

Authors:  Kathleen G Fan; Jess Mandel; Parag Agnihotri; Ming Tai-Seale
Journal:  JMIR Mhealth Uhealth       Date:  2020-05-21       Impact factor: 4.773

5.  Experiences and views of patients, carers and healthcare professionals on using modems in domiciliary non-invasive ventilation (NIV): a qualitative study.

Authors:  Stephanie K Mansell; Cherry Kilbride; Martin J Wood; Francesca Gowing; Swapna Mandal
Journal:  BMJ Open Respir Res       Date:  2020-03

6.  Day-to-Day Variability of Parameters Recorded by Home Noninvasive Positive Pressure Ventilation for Detection of Severe Acute Exacerbations in COPD.

Authors:  Weipeng Jiang; Yencheng Chao; Xiaoyue Wang; Cuicui Chen; Jian Zhou; Yuanlin Song
Journal:  Int J Chron Obstruct Pulmon Dis       Date:  2021-03-22

7.  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

Review 8.  How will telemedicine change clinical practice in chronic obstructive pulmonary disease?

Authors:  Michele Vitacca; Alessandra Montini; Laura Comini
Journal:  Ther Adv Respir Dis       Date:  2018 Jan-Dec       Impact factor: 4.031

9.  Patterns of adaptive servo-ventilation settings in a real-life multicenter study: pay attention to volume! : Adaptive servo-ventilation settings in real-life conditions.

Authors:  Dany Jaffuel; Claudio Rabec; Carole Philippe; Jean-Pierre Mallet; Marjolaine Georges; Stefania Redolfi; Alain Palot; Carey M Suehs; Erika Nogue; Nicolas Molinari; Arnaud Bourdin
Journal:  Respir Res       Date:  2020-09-21

Review 10.  Telemedicine in the management of patients with chronic respiratory failure.

Authors:  Neeraj M Shah; Georgios Kaltsakas
Journal:  Breathe (Sheff)       Date:  2021-03
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