Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patch
Abstract Background Change of the corrected flow time (Ftc) is a surrogate for tracking stroke volume (SV) in the intensive care unit. Multiple Ftc equations have been proposed; many have not had their diagnostic characteristics for detecting SV change reported. Further, little is known about the in...
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doaj-97ab9104e91d4979b239533e303c141b2020-11-25T03:03:02ZengSpringerOpenIntensive Care Medicine Experimental2197-425X2020-09-018111110.1186/s40635-020-00339-7Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patchJon-Émile S. Kenny0Igor Barjaktarevic1David C. Mackenzie2Andrew M. Eibl3Matthew Parrotta4Bradley F. Long5Joseph K. Eibl6Health Sciences North Research InstituteDivision of Pulmonary and Critical Care, Department of Medicine, David Geffen School of Medicine at UCLADepartment of Emergency Medicine, Maine Medical CenterHealth Sciences North Research InstituteHealth Sciences North Research InstituteHealth Sciences North Research InstituteHealth Sciences North Research InstituteAbstract Background Change of the corrected flow time (Ftc) is a surrogate for tracking stroke volume (SV) in the intensive care unit. Multiple Ftc equations have been proposed; many have not had their diagnostic characteristics for detecting SV change reported. Further, little is known about the inherent Ftc variability induced by the respiratory cycle. Materials and methods Using a wearable Doppler ultrasound patch, we studied the clinical performance of 11 Ftc equations to detect a 10% change in SV measured by non-invasive pulse contour analysis; 26 healthy volunteers performed a standardized cardiac preload modifying maneuver. Results One hundred changes in cardiac preload and 3890 carotid beats were analyzed. Most of the 11 Ftc equations studied had similar diagnostic attributes. Wodeys’ and Chambers’ formulae had identical results; a 2% change in Ftc detected a 10% change in SV with a sensitivity and specificity of 96% and 93%, respectively. Similarly, a 3% change in Ftc calculated by Bazett’s formula displayed a sensitivity and specificity of 91% and 93%. FtcWodey had 100% concordance and an R 2 of 0.75 with change in SV; these values were 99%, 0.76 and 98%, 0.71 for FtcChambers and FtcBazetts, respectively. As an exploratory analysis, we studied 3335 carotid beats for the dispersion of Ftc during quiet breathing using the equations of Wodey and Bazett. The coefficient of variation of Ftc during quiet breathing for these formulae were 0.06 and 0.07, respectively. Conclusions Most of the 11 different equations used to calculate carotid artery Ftc from a wearable Doppler ultrasound patch had similar thresholds and abilities to detect SV change in healthy volunteers. Variation in Ftc induced by the respiratory cycle is important; measuring a clinically significant change in Ftc with statistical confidence requires a large sample of beats.http://link.springer.com/article/10.1186/s40635-020-00339-7Carotid arteryCorrected flow timeFluid responsivenessHemodynamicsStroke volume |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jon-Émile S. Kenny Igor Barjaktarevic David C. Mackenzie Andrew M. Eibl Matthew Parrotta Bradley F. Long Joseph K. Eibl |
spellingShingle |
Jon-Émile S. Kenny Igor Barjaktarevic David C. Mackenzie Andrew M. Eibl Matthew Parrotta Bradley F. Long Joseph K. Eibl Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patch Intensive Care Medicine Experimental Carotid artery Corrected flow time Fluid responsiveness Hemodynamics Stroke volume |
author_facet |
Jon-Émile S. Kenny Igor Barjaktarevic David C. Mackenzie Andrew M. Eibl Matthew Parrotta Bradley F. Long Joseph K. Eibl |
author_sort |
Jon-Émile S. Kenny |
title |
Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patch |
title_short |
Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patch |
title_full |
Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patch |
title_fullStr |
Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patch |
title_full_unstemmed |
Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patch |
title_sort |
diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable doppler patch |
publisher |
SpringerOpen |
series |
Intensive Care Medicine Experimental |
issn |
2197-425X |
publishDate |
2020-09-01 |
description |
Abstract Background Change of the corrected flow time (Ftc) is a surrogate for tracking stroke volume (SV) in the intensive care unit. Multiple Ftc equations have been proposed; many have not had their diagnostic characteristics for detecting SV change reported. Further, little is known about the inherent Ftc variability induced by the respiratory cycle. Materials and methods Using a wearable Doppler ultrasound patch, we studied the clinical performance of 11 Ftc equations to detect a 10% change in SV measured by non-invasive pulse contour analysis; 26 healthy volunteers performed a standardized cardiac preload modifying maneuver. Results One hundred changes in cardiac preload and 3890 carotid beats were analyzed. Most of the 11 Ftc equations studied had similar diagnostic attributes. Wodeys’ and Chambers’ formulae had identical results; a 2% change in Ftc detected a 10% change in SV with a sensitivity and specificity of 96% and 93%, respectively. Similarly, a 3% change in Ftc calculated by Bazett’s formula displayed a sensitivity and specificity of 91% and 93%. FtcWodey had 100% concordance and an R 2 of 0.75 with change in SV; these values were 99%, 0.76 and 98%, 0.71 for FtcChambers and FtcBazetts, respectively. As an exploratory analysis, we studied 3335 carotid beats for the dispersion of Ftc during quiet breathing using the equations of Wodey and Bazett. The coefficient of variation of Ftc during quiet breathing for these formulae were 0.06 and 0.07, respectively. Conclusions Most of the 11 different equations used to calculate carotid artery Ftc from a wearable Doppler ultrasound patch had similar thresholds and abilities to detect SV change in healthy volunteers. Variation in Ftc induced by the respiratory cycle is important; measuring a clinically significant change in Ftc with statistical confidence requires a large sample of beats. |
topic |
Carotid artery Corrected flow time Fluid responsiveness Hemodynamics Stroke volume |
url |
http://link.springer.com/article/10.1186/s40635-020-00339-7 |
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