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      Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patch

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          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%. 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.

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          Most cited references 32

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          An improved method for adjusting the QT interval for heart rate (the Framingham Heart Study)

          Several formulas have been proposed to adjust the QT interval for heart rate, the most commonly used being the QT correction formula (QTc = QT/square root of RR) proposed in 1920 by Bazett. The QTc formula was derived from observations in only 39 young subjects. Recently, the adequacy of Bazett's formula has been questioned. To evaluate the heart rate QT association, the QT interval was measured on the initial baseline electrocardiogram of 5,018 subjects (2,239 men and 2,779 women) from the Framingham Heart Study with a mean age of 44 years (range 28 to 62). Persons with coronary artery disease were excluded. A linear regression model was developed for correcting QT according to RR cycle length. The large sample allowed for subdivision of the population into sex-specific deciles of RR intervals and for comparison of QT, Bazett's QTc and linear corrected QT (QTLC). The mean RR interval was 0.81 second (range 0.5 to 1.47) heart rate 74 beats/min (range 41 to 120), and mean QT was 0.35 second (range 0.24 to 0.49) in men and 0.36 second (range 0.26 to 0.48) in women. The linear regression model yielded a correction formula (for a reference RR interval of 1 second): QTLC = QT + 0.154 (1-RR) that applies for men and women. This equation corrects QT more reliably than the Bazett's formula, which overcorrects the QT interval at fast heart rates and undercorrects it at low heart rates. Lower and upper limits of normal QT values in relation to RR were generated.(ABSTRACT TRUNCATED AT 250 WORDS)
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            An analysis of the time-relations of electrocardiograms

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              Die Systolendauer im Elektrokardiogramm bei normalen Menschen und bei Herzkranken

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                Author and article information

                Contributors
                jon-emile@heart-lung.org
                Journal
                Intensive Care Med Exp
                Intensive Care Med Exp
                Intensive Care Medicine Experimental
                Springer International Publishing (Cham )
                2197-425X
                17 September 2020
                17 September 2020
                December 2020
                : 8
                Affiliations
                [1 ]GRID grid.420638.b, ISNI 0000 0000 9741 4533, Health Sciences North Research Institute, ; Sudbury, ON P3E 2H2 Canada
                [2 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Division of Pulmonary and Critical Care, Department of Medicine, , David Geffen School of Medicine at UCLA, ; Los Angeles, CA USA
                [3 ]GRID grid.240160.1, Department of Emergency Medicine, , Maine Medical Center, ; Portland, ME USA
                [4 ]GRID grid.67033.31, ISNI 0000 0000 8934 4045, Tufts University School of Medicine, ; Boston, MA USA
                [5 ]GRID grid.436533.4, ISNI 0000 0000 8658 0974, Northern Ontario School of Medicine, ; Sudbury, ON Canada
                Article
                339
                10.1186/s40635-020-00339-7
                7498524
                © 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/.

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