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Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patch
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 F...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498524/ https://www.ncbi.nlm.nih.gov/pubmed/32940808 http://dx.doi.org/10.1186/s40635-020-00339-7 |
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author | Kenny, Jon-Émile S. Barjaktarevic, Igor Mackenzie, David C. Eibl, Andrew M. Parrotta, Matthew Long, Bradley F. Eibl, Joseph K. |
author_facet | Kenny, Jon-Émile S. Barjaktarevic, Igor Mackenzie, David C. Eibl, Andrew M. Parrotta, Matthew Long, Bradley F. Eibl, Joseph K. |
author_sort | Kenny, Jon-Émile S. |
collection | PubMed |
description | 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%. Ftc(Wodey) 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 Ftc(Chambers) and Ftc(Bazetts), 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. |
format | Online Article Text |
id | pubmed-7498524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-74985242020-09-28 Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patch Kenny, Jon-Émile S. Barjaktarevic, Igor Mackenzie, David C. Eibl, Andrew M. Parrotta, Matthew Long, Bradley F. Eibl, Joseph K. Intensive Care Med Exp Research 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%. Ftc(Wodey) 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 Ftc(Chambers) and Ftc(Bazetts), 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. Springer International Publishing 2020-09-17 /pmc/articles/PMC7498524/ /pubmed/32940808 http://dx.doi.org/10.1186/s40635-020-00339-7 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Research Kenny, Jon-Émile S. Barjaktarevic, Igor Mackenzie, David C. Eibl, Andrew M. Parrotta, Matthew Long, Bradley F. Eibl, Joseph K. Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patch |
title | 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_short | 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 |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498524/ https://www.ncbi.nlm.nih.gov/pubmed/32940808 http://dx.doi.org/10.1186/s40635-020-00339-7 |
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