The Frank-Starling relationship is a fundamental concept in cardiovascular physiology, relating change in cardiac filling to its output. Historically, this relationship has been measured by physiologists and clinicians using invasive monitoring tools, relating right atrial pressure (P ra) to stroke volume (SV) because the P ra-SV slope has therapeutic implications. For example, a critically ill patient with a flattened P ra-SV slope may have low P ra yet fail to increase SV following additional cardiac filling (e.g., intravenous fluids). Provocative maneuvers such as the passive leg raise (PLR) have been proposed to identify these fluid non-responders; however, simultaneously measuring cardiac filling and output via non-invasive methods like ultrasound is cumbersome during a PLR. In this Hypothesis and Theory submission, we suggest that a wearable Doppler ultrasound can infer the P ra-SV relationship by simultaneously capturing jugular venous and carotid arterial Doppler in real time. We propose that this method would confirm that low cardiac filling may associate with poor response to additional volume. Additionally, simultaneous assessment of venous filling and arterial output could help interpret and compare provocative maneuvers like the PLR because change in cardiac filling can be confirmed. If our hypothesis is confirmed with future investigation, wearable monitors capable of monitoring both variables of the Frank-Starling relation could be helpful in the ICU and other less acute patient settings.

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 Bazetts formula displayed a sensitivity and specificity of 91% and 93%. FtcWodey had 100% concordance and an R2 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.

OBJECTIVES: Detecting instantaneous stroke volume change in response to altered cardiac preload is the physiologic foundation for determining preload responsiveness. DESIGN: Proof-of-concept physiology study. SETTING: Research simulation laboratory. SUBJECTS: Twelve healthy volunteers. INTERVENTIONS: A wireless continuous wave Doppler ultrasound patch was used to measure carotid velocity time integral and carotid corrected flow time during a squat maneuver. The Doppler patch measurements were compared with simultaneous stroke volume measurements obtained from a noninvasive cardiac output monitor. MEASUREMENTS AND MAIN RESULTS: From stand to squat, stroke volume increased by 24% while carotid velocity time integral and carotid corrected flow time increased by 32% and 9%, respectively. From squat to stand, stroke volume decreased by 13%, while carotid velocity time integral and carotid corrected flow time decreased by 24% and 10%, respectively. Both changes in carotid velocity time integral and corrected flow time were closely correlated with changes in stroke volume (r 2 = 0.81 and 0.62, respectively). The four-quadrant plot found a 100% concordance rate between changes in stroke volume and both changes in carotid velocity time integral and changes in corrected flow time. A change in carotid velocity time integral greater than 15% predicted a change in stroke volume greater than 10% with a sensitivity of 95% and a specificity of 92%. A change in carotid corrected flow time greater than 4% predicted a change in stroke volume greater than 10% with a sensitivity of 90% and a specificity of 92%. CONCLUSIONS: In healthy volunteers, both carotid velocity time integral and carotid corrected flow time measured by a wireless Doppler patch were useful to track changes in stroke volume induced by a preload-modifying maneuver with high sensitivity and specificity.

PURPOSE: We describe the temporal concordance of 3 hemodynamic monitors. MATERIALS AND METHODS: Healthy volunteers performed preload changes while simultaneously wearing a non-invasive, pulse-contour stroke volume (SV) monitor, a bioreactance SV monitor and a wireless, wearable Doppler ultrasound patch over the common carotid artery. The sensitivity and specificity for detecting preload change over 3 temporal windows (early, middle and late) was assessed. RESULTS: 40 preload changes were recorded in total (20 increase, 20 decrease). Immediately, the wearable Doppler had high sensitivity (100%) and specificity (100%) for detecting preload change with an area under the receiver operator curve (AUROC) of 0.98 for both velocity time integral (VTI, 10.5% threshold) and corrected flow time (FTc, 2.5% threshold). The sensitivity, specificity and AUROC for non-invasive pulse contour were equally good (9% SV threshold). For bioreactance, a 13% SV threshold immediately detected preload change with a sensitivity, specificity and AUROC of 60%, 95% and 0.75, respectively. After two SV outputs following preload change, the sensitivity, specificity and AUROC of bioreactance improved to 70%, 90% and 0.85, respectively. CONCLUSIONS: Carotid Doppler ultrasound and non-invasive pulse contour detected rapid hemodynamic change with equal accuracy; bioreactance improved over time. Algorithm-lag should be considered when interpreting clinical studies.

BACKGROUND AND AIMS: To test the feasibility of a novel, wearable carotid Doppler ultrasound to track changes in cardiac output induced by end-inspiratory and end-expiratory occlusion tests. METHODS: We observed the pattern of Doppler change of the common carotid artery during a simulated end-inspiratory and expiratory occlusion test (sEIOT/sEEOT) in 10, nonventilated, healthy subjects. Simultaneously, we measured the Doppler signal of the descending aorta using duplex ultrasound (Xario, Toshiba Medical Systems) and stroke volume (SV) using noninvasive pulse contour analysis (Clearsight, Edwards Lifesciences, Irvine, California). RESULTS: During sEIOT, SV, maximum velocity time integral (VTI) of the descending aorta, and common carotid fell by 25.7% (P = .0131), 26.1% (P < .0001), and 18.5% (P < .0001), respectively. During sEEOT, SV, maximum VTI of the descending aorta, and common carotid rose by: 41.3% (P = .0051), 28.3% (P < .0001), and 41.6% (P < .0001), respectively. There was good correlation between change in aortic VTI and carotid VTI (r 2 = 0.79); SV and aortic VTI (r 2 = 0.82), and SV and carotid VTI (r 2 = 0.95).The coefficient of variation of the VTI measured by the Doppler patch was roughly 60% less than that of the duplex system. CONCLUSIONS: The pattern of SV change induced by a sEIOT/sEEOT in nonmechanically ventilated volunteers is reflected in the common carotid artery and descending aorta. The VTI variability of the Doppler patch was less than that of the traditional, duplex Doppler.

OBJECTIVE: Doppler ultrasonography of the common carotid artery is used to infer stroke volume change and a wearable Doppler ultrasound has been designed to improve this workflow. Previously, in a human model of hemorrhage and resuscitation comprising approximately 50,000 cardiac cycles, we found a strong, linear correlation between changing stroke volume, and measures from the carotid Doppler signal, however, optimal Doppler thresholds for detecting a 10% stroke volume change were not reported. In this Research Note, we present these thresholds, their sensitivities, specificities and areas under their receiver operator curves (AUROC). RESULTS: Augmentation of carotid artery maximum velocity time integral and corrected flowtime by 18% and 4%, respectively, accurately captured 10% stroke volume rise. The sensitivity and specificity for these thresholds were identical at 89% and 100%. These data are similar to previous investigations in healthy volunteers monitored by the wearable ultrasound.

UNLABELLED: Carotid Doppler ultrasound is used as a measure of fluid responsiveness, however, assessing change with statistical confidence requires an adequate beat sample size. The coefficient of variation helps quantify the number of cardiac cycles needed to adequately detect change during functional hemodynamic monitoring. DESIGN: Prospective, observational, human model of hemorrhage and resuscitation. SETTING: Human physiology laboratory at Mayo Clinic. SUBJECTS: Healthy volunteers. INTERVENTIONS: Lower body negative pressure. MEASUREMENTS AND MAIN RESULTS: We measured the coefficient of variation of the carotid artery velocity time integral and corrected flow time during significant cardiac preload changes. Seventeen-thousand eight-hundred twenty-two cardiac cycles were analyzed. The median coefficient of variation of the carotid velocity time integral was 8.7% at baseline and 11.9% during lowest-tolerated lower body negative pressure stage. These values were 3.6% and 4.6%, respectively, for the corrected flow time. CONCLUSIONS: The median coefficient of variation values measured in this large dataset indicates that at least 6 cardiac cycles should be averaged before and after an intervention when using the carotid artery as a functional hemodynamic measure.