UNASSIGNED: Pulse wave transit time (PWTT) shows promise for monitoring intravascular fluid status intraoperatively. Presently, it is unknown how PWTT mirrors haemodynamic variables representing preload, inotropy, or afterload.
UNASSIGNED: PWTT was measured continuously in 24 adult volunteers. Stroke volume was assessed by transthoracic echocardiography. Volunteers underwent four randomly assigned manoeuvres: \'Stand-up\' (decrease in preload), passive leg raise (increase in preload), a \'step-test\' (adrenergic stimulation), and a \'Valsalva manoeuvre\' (increase in intrathoracic pressure). Haemodynamic measurements were performed before and 1 and 5 min after completion of each manoeuvre. Correlations between PWTT and stroke volume were analysed using the Pearson correlation coefficient.
UNASSIGNED: \'Stand-up\' caused an immediate increase in PWTT (mean change +55.9 ms, P-value <0.0001, 95% confidence interval 46.0-65.7) along with an increase in mean arterial pressure and heart rate and a drop in stroke volume (P-values <0.0001). Passive leg raise caused an immediate drop in PWTT (mean change -15.4 ms, P-value=0.0024, 95% confidence interval -25.2 to -5.5) along with a decrease in mean arterial pressure (P-value=0.0052) and an increase in stroke volume (P-value=0.001). After 1 min, a \'step-test\' caused no significant change in PWTT measurements (P-value=0.5716) but an increase in mean arterial pressure and heart rate (P-values <0.0001), without changes in stroke volume (P-value=0.1770). After 5 min, however, PWTT had increased significantly (P-value <0.0001). Measurements after the Valsalva manoeuvre caused heterogeneous results.
UNASSIGNED: Noninvasive assessment of PWTT shows promise to register immediate preload changes in healthy adults. The clinical usefulness of PWTT may be hampered by late changes because of reasons different from fluid shifts.
UNASSIGNED: German clinical trial register (DRKS, ID: DRKS00031978, https://www.drks.de/DRKS00031978).

The dynamics of the pulse wave (PW) associated with the PW transit time variability (PWTTV) determines the peripheral pulse rate variability, which is used as a surrogate for heart rate variability (HRV). The aim of the work is to analyze the frequency-dependent dynamics of PWTTV and to identify the possible frequency-phase modulation of PW velocity oscillations on the transit from the heart to the soft tissues of the distal parts of the upper extremities. RR-interval recordings and synchronous records of photoplethysmograms of 12 conditionally healthy subjects from the PhysioNet open database were used in this work. Using the Hilbert-Huang transform 3 spectral components of PWTTV and HRV were identified. It was shown that the amplitudes of PWTTV oscillations were many times (up to 8.4 times) smaller than the amplitudes of HRV, and the peaks of PWTTV spectral components were shifted towards higher frequencies than those of HRV. Functional relations between PWTTV and HRV, which can determine the phase modulation of periodic changes in the PW propagation velocity, were revealed.

Objective. Since pulse wave transit time (PWTT) shortens as pulmonary artery pressure (PAP) increases it was suggested as a potential non-invasive surrogate for PAP. The state of tidal lung filling is also known to affect PWTT independently of PAP. The aim of this retrospective analysis was to test whether respiratory gating improved the correlation coefficient between PWTT and PAP.Approach. In each one of five anesthetized and mechanically ventilated pigs two high-fidelity pressure catheters were placed, one directly behind the pulmonary valve, and the second one in a distal branch of the pulmonary artery. PAP was raised using the thromboxane A2 analogue U46619 and animals were ventilated in a pressure controlled mode (I:E ratio 1:2, respiratory rate 12/min, tidal volume of 6 ml kg-1). All signals were recorded using the multi-channel platform PowerLab®. The arrival of the pulse wave at each catheter tip was determined using a MATLAB-based modified hyperbolic tangent algorithm and PWTT calculated as the time interval between these arrivals.Main results. Correlation coefficient for PWTT and mean PAP wasr= 0.932 for thromboxane. This correlation coefficient increased considerably when heart beats either at end-inspiration (r= 0.978) or at end-expiration (r= 0.985) were selected (=respiratory gating).Significance. The estimation of mean PAP from PWTT improved significantly when taking the respiratory cycle into account. Respiratory gating is suggested to improve for the estimation of PAP by PWTT.

Clinical studies have extensively demonstrated that central aortic blood pressure (CABP) has greater clinical significance in comparison with peripheral blood pressure. Despite the existence of various techniques for noninvasively measuring CABP, the clinical applications of most techniques are hampered by the unsatisfactory accuracy or large variability in measurement errors. In this study, we proposed a new method for noninvasively estimating CABP with improved accuracy and reduced uncertain errors. The main idea was to optimize the estimation of the pulse wave transit time from the aorta to the occluded lumen of the brachial artery under a suprasystolic cuff by identifying and utilizing the characteristic information of the cuff oscillation wave, thereby improving the accuracy and stability of the CABP estimation algorithms under various physiological conditions. The method was firstly developed and verified based on large-scale virtual subject data (n = 800) generated by a computational model of the cardiovascular system coupled to a brachial cuff, and then validated with small-scale in vivo data (n = 34). The estimation errors for the aortic systolic pressure were -0.05 ± 0.63 mmHg in the test group of the virtual subjects and -1.09 ± 3.70 mmHg in the test group of the patients, both demonstrating a good performance. In particular, the estimation errors were found to be insensitive to variations in hemodynamic conditions and cardiovascular properties, manifesting the high robustness of the method. The method may have promising clinical applicability, although further validation studies with larger-scale clinical data remain necessary.

UNASSIGNED: Several monitors have been developed that measure stroke volume (SV) in a beat-to-beat manner. Accordingly, Stroke volume variation (SVV) induced by positive pressure ventilation is widely used to predict fluid responsiveness.
UNASSIGNED: The purpose of this study was to compare the ability of two different methods to predict fluid responsiveness using SVV, stroke volume variation by esCCO (esSVV) and stroke volume variation by FloTrac/VigileoTM (flSVV).
UNASSIGNED: esSVV, flSVV, and stroke volume index (SVI) by both monitoring devices of 37 adult patients who underwent laparotomy surgery, were measured. Receiver operating characteristic (ROC) analysis was performed.
UNASSIGNED: The area under the ROC curve (AUC) of esSVV was significantly higher than that of flSVV (p= 0.030). esSVV and flSVV showed cutoff values of 6.1% and 10% respectively, to predict an increase of more than 10% in SVI after fluid challenge. The Youden index for esSVV was higher than flSVV, even with a cutoff value between 6% and 8%.
UNASSIGNED: Since esSVV and flSVV showed significant differences in AUC and cutoff values, the two systems were not comparable in predicting fluid responsiveness. Furthermore, it seems that SVV needs to be personalized to accurately predict fluid responsiveness for each patient.

To compare the reliability of cardiac index (CI) and stroke-volume variation (SVV) measured by the pulse-wave transit-time (PWTT) method using estimated continuous cardiac output (esCCO) technique with conventional pulse-contour analysis after off-pump coronary artery bypass grafting (OPCAB).
A single-center, prospective, observational study.
At a 1,000-bed university hospital.
A total of 21 patients were enrolled after elective OPCAB.
The study authors performed a method comparison study with simultaneous measurement of CI and SVV based on the esCCO technique (CIesCCO and esSVV, correspondingly) and pulse-contour analysis (CIPCA and SVVPCA, correspondingly). As a secondary analysis, they also assessed the trending ability of CIesCCO versus CIPCA. MEASUREMENTS AND MAIN RESULTS: The authors analyzed 178 measurement pairs for CI, and 174 pairs for SVV during the 10 study stages. The mean bias between CIesCCO and CIPCA was 0.06 L min/m2, with limits of agreement of ± 0.92 L min/m2 and a percentage error (PE) of 35.3%. The analysis of the trending ability of CI measured by PWTT revealed a concordance rate of 70%. The mean bias between esSVV and SVVPCA was -6.1%, with limits of agreement of ± 15.5% and a PE of 137%.
The overall performance of CIesCCO and esSVV versus CIPCA and SVVPCA is not clinically acceptable. A further improvement of the PWTT algorithm may be required for an accurate and precise assessment of CI and SVV.

Aim of this study is to test the predictive value of Pulse Wave Transit Time (PWTT) for fluid responsiveness in comparison to the established fluid responsiveness parameters pulse pressure (ΔPP) and corrected flow time (FTc) during major abdominal surgery.
Forty patients undergoing major abdominal surgery were enrolled with continuous monitoring of PWTT (LifeScope® Modell J BSM-9101 Nihon Kohden Europe GmbH, Rosbach, Germany) and stroke volume (Esophageal Doppler Monitoring CardioQ-ODM®, Deltex Medical Ltd, Chichester, UK). In case of hypovolemia (difference in pulse pressure [∆PP] ≥ 9%, corrected flow time [FTc] ≤ 350 ms) a fluid bolus of 7 ml/kg ideal body weight was administered. Receiver operating characteristics (ROC) curves and corresponding areas under the curve (AUCs) were used to compare different methods of determining PWTT. A Wilcoxon test was used to discriminate fluid responders (increase in stroke volume of ≥ 10%) from non-responders. The predictive value of PWTT for fluid responsiveness was compared by testing for differences between ROC curves of PWTT, ΔPP and FTc using the methods by DeLong.
AUCs (area under the ROC-curve) to predict fluid responsiveness for PWTT-parameters were 0.61 (raw c finger Q), 0.61 (raw c finger R), 0.57 (raw c ear Q), 0.53 (raw c ear R), 0.54 (raw non-c finger Q), 0.52 (raw non-c finger R), 0.50 (raw non-c ear Q), 0.55 (raw non-c ear R), 0.63 (∆ c finger Q), 0.61 (∆ c finger R), 0.64 (∆ c ear Q), 0.66 (∆ c ear R), 0.59 (∆ non-c finger Q), 0.57 (∆ non-c finger R), 0.57 (∆ non-c ear Q), 0.61 (∆ non-c ear R) [raw measurements vs. ∆ = respiratory variation; c = corrected measurements according to Bazett\'s formula vs. non-c = uncorrected measurements; Q vs. R = start of PWTT-measurements with Q- or R-wave in ECG; finger vs. ear = pulse oximetry probe location]. Hence, the highest AUC to predict fluid responsiveness by PWTT was achieved by calculating its respiratory variation (∆PWTT), with a pulse oximeter attached to the earlobe, using the R-wave in ECG, and correction by Bazett\'s formula (AUC best-PWTT 0.66, 95% CI 0.54-0.79). ∆PWTT was sufficient to discriminate fluid responders from non-responders (p = 0.029). No difference in predicting fluid responsiveness was found between best-PWTT and ∆PP (AUC 0.65, 95% CI 0.51-0.79; p = 0.88), or best-PWTT and FTc (AUC 0.62, 95% CI 0.49-0.75; p = 0.68).
ΔPWTT shows poor ability to predict fluid responsiveness intraoperatively. Moreover, established alternatives ΔPP and FTc did not perform better.
Prior to enrolement on clinicaltrials.gov (NC T03280953; date of registration 13/09/2017).

Pulse wave transit time (PWTT) is the time difference between the occurrence of an R-wave on an electrocardiogram and the detection of pulsatile signals on a pulse oximeter, which reflects changes in blood pressure (BP) corresponding to the vessel wall compliance. However, the factors affecting PWTT variability have not been determined. Thus, we investigated the BP changes associated with variations in PWTT and identified the clinical characteristics associated with these variations. Data related to 605 cases of dental procedures performed under intravenous conscious sedation from April 2020 to November 2021 were collected, and 485 cases were enrolled. Heart rate, systolic blood pressure before and after local anesthesia (LA) administration, and crest and trough PWTT waves during LA administration were recorded. Thereafter, PWTT variability was calculated; cases were divided into two groups: large PWTT variability (LPV, n = 357) and small PWTT variability (SPV, n = 128). The index of large PWTT variability could not detect changes in BP. Logistic regression analysis revealed that factors, such as LA use, age, hypertension, and dental treatment phobia were associated with PWTT variability. The use of epinephrine more than 36.25 µg in each LA resulted in PWTT variability of more than 15 ms.

UNASSIGNED: Pulse wave transit time is a novel method of estimating continuous cardiac output (esCCO). Since there are not many studies evaluating esCCO, we compared it with arterial pressure based cardiac output (APCO) method (FloTrac).
UNASSIGNED: In this prospective single-center observational study, we included 50 adult patients planned to undergo supramajor oncosurgeries, where major blood loss and extensive fluid shifts were expected. Cardiac output (CO) measurements were obtained by both methods at five distinct time points, giving us 250 paired readings of stroke volume index (SVI) and cardiac index (CI). We analyzed these readings using Pearson\'s correlation coefficient and Bland-Altman plots, along with other appropriate statistical tests.
UNASSIGNED: There was significant correlation between CI and SVI measured by the esCCO and APCO. Bland-Altman plot analysis for CI showed a bias of -0.44 L/minute/m2, precision of 0.74, and the limits of agreement of -1.89 and +1.01, while the percentage error was 46.29%. Bland-Altman analysis for SVI showed a bias -5.07 mL with a precision of 9.36, and the limits of agreement to be -23.4 to +13.28. The percentage error was 46.56%.
UNASSIGNED: This study demonstrated that esCCO tended to underestimate the CI to a large degree, particularly while estimating the cardiac output in the lower range. We found that the limits of agreement between two methods were wide, which are not likely to be clinically acceptable. Further studies with larger number of data points, obtained in a similar subset of patients, for cardiac output measurement in the perioperative period will certainly help determine if pulse wave transit time (PWTT) is here to stay (CTRI No.: CTRI/2019/08/020543).
UNASSIGNED: Joshi M, Rathod R, Bhosale SJ, Kulkarni AP. Accuracy of Estimated Continuous Cardiac Output Monitoring (esCCO) Using Pulse Wave Transit Time (PWTT) Compared to Arterial Pressure-based CO (APCO) Measurement during Major Surgeries. Indian J Crit Care Med 2022;26(4):496-500.

OBJECTIVE: To establish the relationship between pulse wave transit time (PWTT) before anaesthesia induction and blood pressure variability (BPV) during anaesthesia induction.
METHODS: This prospective observational cohort study enrolled consecutive patients that underwent elective surgery. Invasive arterial pressure, electrocardiography, pulse oximetry, heart rate and bispectral index were monitored. PWTT and BPV were measured with special software. Anaesthesia was induced with propofol, sufentanil and rocuronium.
RESULTS: A total of 54 patients were included in this study. There was no correlation between BPV and the dose of propofol, sufentanil and rocuronium during anaesthesia induction. Bivariate linear regression analysis demonstrated that PWTT (r = -0.54), age (r = 0.34) and systolic blood pressure (r = 0.31) significantly correlated with systolic blood pressure variability (SBPV). Only PWTT (r = -0.38) was significantly correlated with diastolic blood pressure variability (DBPV). Patients were stratified into high PWTT and low PWTT groups according to the mean PWTT value (96.8 ± 17.2 ms). Compared with the high PWTT group, the SBPV of the low PWTT group increased significantly by 3.4%. The DBPV of the low PWTT group increased significantly by 2.1% compared with the high PWTT group.
CONCLUSIONS: PWTT, assessed before anaesthesia induction, may be an effective predictor of haemodynamic fluctuations during anaesthesia induction.