PT  - JOURNAL ARTICLE
AU  - Eui-Sik Suh
AU  - Fiammetta Fedele
AU  - Vincenzo Galgano
AU  - Michelle Ramsay
AU  - Swapna Mandal
AU  - Terry Parlett
AU  - Andrew Coleman
AU  - Patrick Murphy
AU  - John Moxham
AU  - Nicholas Hart
TI  - An automated algorithm to quantify neural respiratory drive in acute exacerbations of COPD
DP  - 2013 Sep 01
TA  - European Respiratory Journal
PG  - P2512
VI  - 42
IP  - Suppl 57
4099  - http://erj.ersjournals.com/content/42/Suppl_57/P2512.short
4100  - http://erj.ersjournals.com/content/42/Suppl_57/P2512.full
SO  - Eur Respir J2013 Sep 01; 42
AB  - Background: Neural respiratory drive (NRD), obtained from parasternal muscle electromyography (pEMG), is a useful physiological biomarker of clinical deterioration and readmission risk in acute exacerbations of COPD (AECOPD) (Murphy et al, Thorax 2012). However, manual analysis of pEMG traces is time-consuming. We aimed to validate a novel automated algorithm for rapid quantification of NRD from pEMG signals.Methods: pEMG was performed daily in AECOPD patients during hospital admission. An automated algorithm was developed in MatLab (MathWorks, MA, USA) to exclude ECG artefact and output the mean inspiratory pEMG activity in 1 minute (autoEMGpara,). Manual and automated analyses of pEMG signals were compared using the intraclass correlation coefficient (ICC). The sensitivity and specificity of the algorithm to detect changes in NRD between consecutive readings was analysed.Results: 91 pEMG traces were analysed in 20 AECOPD patients (age 69±12years, %predicted FEV1 30±9), yielding 71 data pairs. The ICC between manual EMGpara and autoEMGpara was 0.81, p<0.001. The automated algorithm had a sensitivity of 88% and specificity of 90% to detect increases in NRD (figure 1).Conclusion: The agreement between the automated and manual analyses of pEMG traces is sufficient to have clinical utility. Automated analysis will allow NRD to be used as a physiological biomarker in a clinical setting to predict outcome in AECOPD.