Corrected flow time and respirophasic variation in blood flow peak velocity of radial artery predict fluid responsiveness in gynecological surgical patients with mechanical ventilation

Corrected flow time and respirophasic variation in blood flow peak velocity of radial artery predict fluid responsiveness in gynecological surgical patients with mechanical ventilation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Corrected flow time and respirophasic variation in blood flow peak velocity of radial artery predict fluid responsiveness in gynecological surgical patients with mechanical ventilation Jianjun Shen, Shaobing Dai, Xia Tao, Xinzhong Chen, Lili Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1689029/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUND: Recent evidence suggests that ultrasound measurements of carotid and brachial artery corrected flow time (FTc) and respirophasic variation in blood flow peak velocity (ΔVpeak) are valuable for predicting fluid responsiveness in mechanical ventilated patients. We performed the study to reveal the performance of ultrasonic measurements of radial artery FTc, and ΔVpeak for predicting fluid responsiveness in mechanical ventilated patients undergoing gynecological surgery. METHODS: A total of eighty mechanical ventilated patients were enrolled. Radial artery FTc and ΔVpeak, and non-invasive PPV were measured before and after fluid challenge. Fluid responsiveness was defined as an increase in stroke volume index (SVI) of 15% or more after the fluid challenge. RESULTS: Forty-four (55%) patients were fluid responders. Multivariate logistic regression analysis showed that radial artery FTc and ΔVpeak were the independent predictors of fluid responsiveness, with odds ratios of 1.152 [95% confidence interval (CI) 1.045 to 1.270] and 0.581 (95% CI 0.403 to 0.839). The area under the ROC curve of fluid responsiveness predicted by FTc was 0.802 (95% CI, 0.706-0.898), and ΔVpeak was 0.812 (95% CI, 0.714-0.909). The optimal cut-off values of FTc for fluid responsiveness was 336.6 ms (sensitivity of 75.3%; specificity of 75.9%), ΔVpeak was 14.2% (sensitivity of 88.2%; specificity of 67.9%). The grey zone for FTc was 313.5-336.6 ms, ΔVpeak was 12.2-16.5%. CONCLUSIONS: Ultrasound measurement of radial artery FTc and ΔVpeak are the feasible and reliable methods for predicting fluid responsiveness in mechanically ventilated patients. The trial was registered at the Chinese Clinical Trial Registry (ChiCTR)(www.chictr.org), registration number ChiCTR-ICR-2000040941. This study was approved by the Research Ethics Committee of Women’s Hospital, Zhejiang University School of Medicine (20200197). corrected flow time respirophasic variation in blood flow peak velocity radial artery ultrasonography fluid responsiveness gynecological Figures Figure 1 Figure 2 Introduction Perioperative volume management is an important part of clinical anesthesia work and is crucial to prevent postoperative complications and smooth recovery of patients. Insufficient infusion can cause low perfusion of heart, kidney, brain and other important organs, microcirculation disorder, and organ dysfunction, while excessive infusion can cause postoperative intra-abdominal hypertension, affect the recovery of gastrointestinal function after anastomotic healing, and increase the probability of systemic infection. In this context, a proper assessment of volume status, coupled with proper fluid management, can optimize the hemodynamics of patients and avoid ineffective or even harmful fluid infusion 1 , 2 , 3 . Over the past decades, a large body of evidence has showed that various dynamic parameters (both invasive and non-invasive) have emerged with a high sensitivity and specificity for predicting fluid responsiveness (FR) 4 . Among them, non-invasive pulse pressure variation (PPV) has been suggested as a simple indicator of liquid reactivity, as non-invasive and easy access 5 , however, it has also been reported that volume status cannot be accurately reflected in certain cases such as cardiac arrhythmias, increased intrathoracic or abdominal pressure, and reduced lung compliance 6 , 7 . Recently, ultrasonic Doppler for measuring blood flow of superficial artery has been reported to be used in operation care settings due to its advantages of convenience, non-invasive, far away from the surgical field, and low technical requirements 8 , 9 . Importantly, carotid artery corrected flow time (FTc) has been validated as an acceptable and regenerative method for the identification of fluid responsiveness in critically ill patients with undifferentiated shock 10 . Interestingly, after Kim et al. 11 confirmed that respirophasic variation in blood flow peak velocity (ΔVpeak) of carotid artery has the similar capability to predict fluid responsiveness in infants undergoing cardiac surgery, a systematic meta-analysis of Yao et al. 12 further described that ΔVpeak of carotid artery had more value than brachial artery in predicting fluid Responsiveness in mechanically ventilated patients. However, we still could not know that whether radial artery FTc and ΔVpeak could assess the effect of a fluid challenge and is ideally suited to guide fluid resuscitation in mechanically ventilated patients. Our aim was to compare the ability of ultrasonic measurements of radial artery FTc and ΔVpeak to predict fluid responsiveness with non-invasive PPV in mechanically ventilated patients undergoing gynecological surgery. Methods Patients After approved by the Research Ethics Committee of Women’s Hospital, Zhejiang University School of Medicine (20200197), this study was registered at the Chinese Clinical Trial Registry (ChiCTR)(www.chictr.org) on 2020/12/01 with the registration number ChiCTR-ICR-2000040941. After receiving informed consents, and was This study was performed, The written were provided by all patients. Eighty patients, American Society of Anesthesiologists (ASA) Class I-II, undergoing gynecological surgery under general anesthesia from 2020/12/14 to 2021/02/28 were recruited into the study. Exclusion criteria were pregnancy, ≤18 years of old, BMI>30 or <15kg/m-2, patients receiving vasoactive or inotropic support before induction of anaesthesia, or with left ventricular ejection fraction (LVEF) less than 45%, pre-existing peripheral arterial occlusive disease, cardiovascular disease, hypertension, pulmonary hypertension, chronic lung disease, abnormal chest wall, atrial or ventricular arrhythmia, diabetes mellitus, cerebrovascular disease and so on. induction Anaesthetic management Upon arrival in the operating theatre, a three-lead electrocardiogram (ECG), pulse oximetry (SpO 2 ), and non-invasive arterial pressure monitoring were applied. After anesthesia induction of midazolam 0.04 mg/kg cisatracurium 0.2 mg/kg propofol 2 mg/kg sufentanil 0.5 μg/kg, endotracheal intubation was performed. Respiratory setting of anesthesia machine (Aestiva, GE/Datex-Ohmeda) are set as follows: volume-controlled ventilation (VCV), inspiratory-expiratory (I:E) ratio of 1:2, respiratory rate of 8-10 bpm, tidal volume of 8 mL/kg of ideal weight [45.5+0.91x (height in cm-152.4)], and PEEP of 5 cm H 2 O in 50% oxygen with air. Respiratory settings were adjusted to maintain the P ET CO 2 at less than 50 mmHg. Anesthesia was maintained with continuous infusion of remifentanil (0.05-0.2 μg/kg -1 min -1 ), propofol (50-100μg/kg -1 min -1 ), and sevoflurane (1.5%–2.5%) and intermittent injection of cisatracurium 0.05 mg/kg as needed to keep the entropy scale between 40 and 60 and for muscle relaxation. The mean arterial pressure was keep between 60 and 80mmHg. Study protocol All measurements including the mean arterial pressure, heart rate, FTc and ΔVpeak of radial artery, non-invasive PPV, and stroke volume index (SVI) were recorded 15min after anesthesia induction and 10min after a fluid loading of 6ml/kg of 6% hydroxyethyl starch 130/0.4. Non-invasive PPV was acquired from the radial artery pressure waveform, using a real-time radial artery blood pressure and hemodynamics monitoring system (TL-400, Zhejiang Shanshi Biological Pharmaceutical Equipment Co., Ltd, Hangzhou, China). Averages of PPV auto over four cycles of 8 s were displayed in real time on the monitor and was calculated by automatic detection algorithms without airway pressure acquisition. The parameters were recorded by an anesthesiologist who was unaware of this study to avoid any personal bias. Rartery FTc and ΔVpeak measured by two independent sonographer who were blinded to each other’s Doppler results and haemodynamic variables of the patients, using an 6-13 MHz variable frequency linear probe ((SONIMAGE HS1, Konica Minolta Inc, Shanghai, China) (Figure 2). The optimal long-axis view was gained at the radial artery on the B-mode real-time image. The sampling site was located in the centre of the lumen, adjacent to the radial head and the ultrasonic beams were adjusted to ensure <60 of angle from the direction of blood flow. The radial artery blood flow waveforms were stored using pulsed wave Doppler for the measurement of FTc and ΔVpeak. FT is measured from the beginning of the upstroke to the trough of the incisural notch on a pulse waveform analysis. FTc was calculated by using a simplified formula: FTc=FT+[1.29x (HR-60)] 13 as evaluating a single cycle after several consecutive cycles became stable and reached the level of acceptable quality. The maximum and minimum values of the peak velocity during one respiratory cycle were measured automatically and recorded. ΔVpeak was calculated as follows: (max peak velocity-min peak velocity)/[(max peak velocity+min peak velocity)/2]×100 14 . SVI was recorded by transthoracic echocardiography with a 1.5-4.5 MHz phased array probe from aortic blood flow. The diameter of the left ventricular outflow tract was determined by using the ultrasound images of the largest opening of the aortic valve on the parasternal long-axis view and the left ventricular outflow tract area was calculated as π x (left ventricular outflow tract diameter/2) 15 . The aortic flow time velocity integral was obtained in the apical five-chamber view at the level of the aortic annulus and from the mean of five consecutive beats of a complete respiratory cycle. SVI and BSA were computed by two formulas as follows: (left ventricular out-flow tract area x aortic flow time velocity integral)/body surface area (BSA), BSA (m2) = 0.0061x body length (cm)+0.0128 x body weight (kg)-0.1529 16 . All values represented the mean of three consecutive measurements and the mean of the two sonographer was employed for analysis. Study endpoints The primary endpoint was to determine the predictive value of FTc, ΔVpeak, and non-invasive PPV for fluid responsiveness (≥15% increases in SVI after fluid challenge) in mechanically ventilated patients 17 . Statistical analysis SPSS 23 (SPSS Inc., Chicago, IL, USA) and PASS 14.0.5 (NCSS Statistical Software, Kaysville, UT, USA) were applied for statistical analysis and calculating the sample size. It has been reported that the area under the receiver operating characteristic (AUROC) curve of FTc measured in the radial artery to predict fluid responsiveness was 0.84 18 , so we assumed that the AUROC curve of radial artery FTc was 0.75. At least 42 patients were required to detect a difference of 0.25 between the AUROC curves of radial artery FTc (0.75) and non-invasive PPV (0.5), with an 0.9 power and type I error of 0.05, assuming 55% incidence of fluid responsiveness in patients undergoing elective gynecological surgery 8 . We used a sample size of 46 patients considering a possible 10% drop out rate. Fluid responsiveness was determined by a 15% or more increase in SVI after fluid challenge. Normality of the data distribution was assessed using Kolmogorove-Smirnov and Shapiroe-Wilk tests. Continuous variables were expressed as mean (standard deviation) if data were normally distributed or median (interquartile range) if not. Categorical variables were expressed as absolute number (%). Responder and non-responder groups were compared with a paired t-test for normally distributed data, ManneWhiney U-test for non-normally distributed data, and X 2 test or Fisher’s exact test, as appropriate, for categorical variables. Pearson correlation coefficient was used to test the relationship between baseline values or the relative changes in haemodynamic variables and the percentage change in SVI after fluid challenge. Multivariate logistic regression analyses were performed to identify multivariate predictors of fluid responsiveness and receiver operating characteristic (ROC) curve was performed to assess the abilities of the two ultrasound-derived parameters, FTc and ΔVpeak, non-invasive PPV to predict fluid responsiveness. The predictive accuracy of the ROC analysis was described as follows: poor [area under the curve (AUC) 0.6–0.7], fair (AUC 0.7–0.8), good (AUC 0.8–0.9), and excellent (AUC 0.9–1.0). The 95% confidence interval (CI) was calculated, and statistical significance was accepted as p<0.05. The “optimal” cut-off values were assessed by using maximizing Youden’s index (J=Sensitivity+Specificity-1=Sensitivity-False-Positive Rate) 19 . The cut-off values defining the gray area was determined by a correlation value of 90% sensitivity and 90 specificity 20 . Importantly, the intra-observer variability (repeatability) and inter-observer variability (reproducibility) were evaluated in 30 randomly chosen selected sets of assessments of FTc and ΔVpeak. Variability was tested by dividing the absolute difference between the two values by their average value. Accordingly, the inter-observer reproducibility for FTc and ΔVpeak was also recognized in all data sets by calculating a coefficient of variation (CV) and an intraclass correlation coefficient (ICC). BlandeAltman plot was applied to test the inter-observer agreement in estimating FTc and ΔVpeak. A P-value <0.05 (two-tailed). Results Patients Of the 85 patients assessed for eligibility, 5 were excluded because of history of cardiovascular disease (n = 1), refusal to participate (n = 2), and other reasons(n = 2). Therefore, 80 subjects were enrolled in the final analysis (Supplementary Fig. 1). The main characteristics of the subjects were comparable between responders (n = 44) and non-responders (n = 36) (Table 1 ). Table 1 Patient Characteristics. Responders group (n = 44) Non-responders group (n = 36) P value Age (yr) 33.3 ± 4.9 31.3 ± 5.6 0.090 ASA (I/II) 44/0 33/3 0.087 Height (cm) 160.0 ± 5.5 161.2 ± 5.6 0.321 Weight (kg) 55.4 ± 6.7 59.0 ± 8.9 0.054 BMI 21.7 ± 2.2 22.7 ± 3.1 0.096 Duration of Surgery (min) 108.4 ± 60.5 93.3 ± 31.1 0.154 Values are numbers or means ± SD. *p < 0.05 compared with Responders group BMI: Body mass index (kg/m 2 ); HR: Heart rate; MAP: Mean arterial pressure. Haemodynamic variables before and after fluid challenge In both responders and non-responders, fluid challenge significantly increased FTc and SVI, while significantly decreased ΔVpeak and non-invasive PPV(p < 0.05) (Table 2 ) (Fig. 1 ). Before the fluid challenge, FTc and SVI were significantly lower in responders than in non-responders (p < 0.05), however, ΔVpeak and non-invasive PPV was significantly higher in responders than in non-responders (p < 0.05) (Table 2 ). In contrast, after fluid challenge, FTc, ΔVpeak, and SVI were all not significantly different between the two groups (Table 2 ). Both MAP and HR were not significantly different between the two groups before and after the fluid challenge (Table 2 ). Table 2 Hemodynamic variables before and after fluid challenge. Responders group (n = 44) Non-responders group (n = 36) P value P value Before After Before After Before After FT C (ms) 315.9 ± 15.0 348.2 ± 19.0* 335.1 ± 16.2# 354.7 ± 24.5* 0.000000 0.184 ΔVpeak (%) 16.8 ± 3.7 10.4 ± 2.5* 12.7 ± 3.9# 9.4 ± 2.9* 0.000007 0.136 PPV (%) 13.2 ± 4.5 7.4 ± 3.6* 8.3 ± 1.5# 7.4 ± 2.0* 0.000000 0.957 SVI (ml m − 2 ) 30.0 ± 5.9 39.7 ± 8.2* 36.1 ± 6.8# 38.7 ± 6.9 0.000018 0.545 MAP (mmHg) 72.9 ± 12.4 77.8 ± 13.2* 75.0 ± 9.6 75.1 ± 8.6 0.411 0.301 HR (beat min-1) 73.8 ± 13.5 64.1 ± 9.8* 71.0 ± 13.5 60.9 ± 8.9* 0.356 0.134 Data are reported as mean ± SD *p < 0.05 compared with before fluid challenge. #p < 0.05 compared with Responders group FT C : radial artery corrected flow time;ΔVpeak༚ respirophasic variation in radial artery blood flow peak velocity༛PPV༚pulse pressure variation; SVI༚stroke volume index. The ability of FTc and ΔVpeak to predict fluid responsiveness FTc, ΔVpeak, and non-invasive PPV were proved to be the independent predictors for fluid responsiveness by multivariate logistic regression, with the odds ratios of 1.152(95% CI 1.045 to 1.270), 0.581 (95% CI 0.403 to 0.839), and 0.361 (95% CI, 0.193 to 0.676), respectively (Table 3 ). The regression equation for predicting fluid responsiveness in pregnant women is logit P =-28.153 + 0.142 FTc-0.543ΔVpeak-1.018 PPV. The area under the ROC curve of fluid responsiveness predicted by FTC was 0.802 (95% CI, 0.706–0.898), and ΔVpeak was 0.812 (95% CI, 0.091–0.286), which were comparable with non-invasive PPV (0.846, 95%CI, 0.070–0.238)(Table 4 ). The sensitivity and specificity for FTc and Vpeak are 75.3%, 75.9% and 88.2%, 67.9% (Table 4 ). Their cut-off values for FTc and Vpeak are 336.6 ms and 14.2% (Table 4 ). Table 3 Multivariate logistic regression analyses identified the factors that were independently associated with fluid responsiveness. B value P value Odds ratio (95% CI) FTC (ms) 0.142 0.004 1.152 (1.045–1.270) ΔVpeak (%) -0.543 0.004 0.581(0.403–0.839) PPV (%) -1.018 0.001 0.361(0.193–0.676) FT C : radial artery corrected flow time;ΔVpeak༚respirophasic variation in radial artery blood flow peak velocity; PPV༚pulse pressure variation. Table 4 Prediction of fluid responsiveness by receiver operating characteristic curves of the baseline FTc, ΔVpeak and PPV. AUROC curve (95% CI) P-value Optimal cut-off value Grey zone Patients in grey zone (%) Sensitivity (95% CI) Specificity (95% CI) Youden index (95% CI) FTc 0.802 (0.706–0.898) 0.0004 336.6 ms 313.5-336.6 ms 40(50%) 0.75(0.66–0.85) 0.76(0.71-1.00) 0.477 ΔVpeak 0.812 (0.714–0.909) 0.0002 14.2% 12.2–16.5% 37(46%) 0.88(0.79–0.97) 0.68(0.60–0.77) 0.540 PPV 0.846 (0.762–0.930) 0.0001 11.5% 7.5–11.5% 48(60%) 0.74(0.55–0.93) 0.54(0.46–0.63) 0.614 AUROC, area under the receiver operating characteristic; CI, confidence interval; FTC: radial artery corrected flow time;ΔVpeak༚respirophasic variation in radial artery blood flow peak velocity; PPV༚pulse pressure variation. * Optimal cut-off values were determined by maximising the Youden index. The inter-observer agreement in estimating FTc and ΔVpeak For FTc measurements, intra-observer variability and inter-observer variability were 0.5 (0.3)% and 1.1 (0.9)%, respectively. For ΔVpeak measurements, inter-observer variability was 7.0 (9.3)% and 5.6 (2.8)%, respectively. Inter-observer reproducibility for estimating FTc was excellent, with an ICC of 0.97 (95% CI, 0.948–0.984) and a CV of 5.6%. Inter-observer reproducibility for estimating ΔVpeak was also excellent, with an ICC of 0.98 (95% CI, 0.973–0.988) and a CV of 28.7%. Using Bland-Altman analysis for evaluating inter-observer agreement in estimating FTc and ΔVpeak, the mean biases were − 0.26 ms [with 95% limits of agreement (LOA) between − 9.34 and 8.82 ms] and 0.41% (with 95% LOA between − 1.19% and 2.00%), respectively (Supplementary Fig. 2). Discussion Over the last decade, numerous studies have proved the usefulness of dynamic indices, such as SVV and PPV, based on the observation that changes in respiratory phase of stroke volume in patients on mechanical ventilation are closely related to the position of the Frank-Starling curve guiding volume resuscitation because commonly used static indicators of fluid responsiveness are not accurate predictors of the effects of fluid administration 6 , 21 . In conjunction, efforts are being made to validate the role of these variables as the integral parts of goal-directed therapy to improve patient outcomes 22 . Among the dynamic indices, PPV originated from arterial waveform analysis is the representative indices for fluid responsiveness and reflects the percentage change in pulse pressure attributable to periodic changes in intrathoracic pressure caused by mechanical ventilation 23 . Based on the premise that non-invasive and readily accessed indices are undoubtedly advantageous, non-invasive PPV has been suggested as a simple indicator of fluid responsiveness and have been shown to be able to track changes in PPV reliably and yield a concordance rate of 91% in comparison to invasive PPV measured by PiCCO technology during major open abdominal surgery 5 . In general, our results showed that non-invasive PPV were predictive of fluid responsiveness in mechanical ventilated patients undergoing gynecological surgery, with the area under the ROC curve of 0.846 (95%CI, 0.762–0.930), a cut-off value of 11.5%, and a grey zone between 7.5 and 11.5%, which are in accordance with the above-mentioned study. However, several exiting factors including technical factors, arterial compliance, cardiac arrhythmias, increased intrathoracic pressure by large tidal volume or PEEP, increased abdominal pressure, and reduced lung compliance restrict its widespread use during surgery 6 , 7 . Recently, ultrasonic Doppler for measuring blood flow of superficial artery has been proved to be successful in predicting the fluid responsiveness 8 , 9 . FTc is a complex static index and has been used and evaluated as a preload indication to predict fluid responsiveness in different surgical settings and the use of FTc for intraoperative volume optimization has been reported to reduce the incidence of complications, improve patients’ recovery, and decrease postoperative hospital stay 24 , 25 . It is affected by several factors, such as preload afterload and inotropic state and is even inversely related to afterload and systemic vascular resistance 26 . However, low FTc does not always correspond to low left ventricular preload and can even represent a volume overload state, which means that simple fluid challenge guided by only FTc could further aggravate deterioration in haemodynamic conditions 27 . We therefore tested the predictive accuracy of FTc to discriminate between responders and non-responders according to a volume load during gynecological surgery compared with non-invasive PPV. Based on our findings, FTc and non-invasive PPV accurately predicted FR attributable to both volume-loading manoeuvres, indicating interchangeability of these variables in this specific patient population. In this context, ROC analysis yielded similar levels of AUCs for non-invasive approaches during fluid resuscitation in the OR. With respect to statistical comparison of the ascertained AUC values, we found that FTc can predict fluid response in mechanical ventilated patient and no significant differences between FTc and non-invasive PPV. However, our findings appear to contradict those of two previous studies 28 , 29 , where FTc was not a predictor of fluid responsiveness. It is possible that patients with haemodynamic conditions that would prevent FTc from predicting fluid responsiveness were not excluded and vasoconstriction by norepinephrine may cause low FTc regardless of left ventricular preload state, which could be why FTc failed to predict fluid responsiveness in both studies. Instead, several recent studies strongly confirmed our results. For example, Lee et al. 8 demonstrated that FTc and PPV are better than CVP and LVEDAI in predicting fluid responsiveness in neurosurgical patients. Yang et al. 30 both FTc and PPVauto were accurate predictors of fluid responsiveness in patients in the supine position and the prone position using a Wilson frame undergoing lumbar spine surgery. In addition, MAITRA et al. 18 addressed that pressure transducer derived radial artery cFT correlated with Doppler derived carotid artery cFT and may be a reasonable predictor of volume responsiveness. In this context, FTc can be used to evaluate the effect of the treatments administered or can be integrated as a limit to optimize CO while avoiding excessive fluid loading. However, there may be no single parameter that can guide fluid therapy under all situations, FTc may be extremely useful when interpreted in conjunction with other clinical information, and measurements such as non-invasive PPV. Up to now, numerous studies have been conducted to determine the ability of ΔVpeak to predict fluid responsiveness and its cut-off value in discriminating between responders and non-responders to fluid resuscitation 14 , 31 , 32 . Accordingly, the ΔVpeak of aortic artery, carotid artery and brachial artery have been successively prove to be the accurate method of evaluating preload and the promising variable shown to predict fluid responsiveness in ventilated surgical patients, critically ill patients or different kinds of shock 11 , 33 , 34 . Of note, the finding of Neto et al. 33 confirmed that ΔVpeak were the most appropriate for prediction of fluid responsiveness when compared with the invasive and noninvasive dynamic variables derived from the arterial pressure and the plethysmographic waveforms in children under general anaesthesia and mechanical ventilation in the operating theatre. Suggested cut-off values of ΔVpeak are 7–20% 33 , which is possibly attributable to the variations in study population, such as surgical patients without concomitant disease or critically ill patients. In the same context, the radial artery is peripheral artery and also provides easy accessibility 32 . The radial artery ΔVpeak as determined by ultrasonic Doppler is a non-invasive and practical bedside monitor, there might be a role for radial artery ΔVpeak as a predictor of fluid responsiveness in certain clinical situations 32 . Based on these theoretical advantages, we investigated the feasibility and predictive power of Doppler-acquired respirophasic radial flow dynamics on fluid responsiveness in mechanically ventilated patients. As our results indicated, the predictability of radial artery ΔVpeak was superior to that of non-invasive PPV with excellent interobserver agreement. Moreover, radial artery ΔVpeak yielded a cut-off value with the highest sensitivity and specificity. Interestingly, we also found that radial artery ΔVpeak also showed a significant increase in non-responders after the fluid challenge, suggesting its strong association with preload, while non-invasive PPV showed a significant decrease after fluid challenge even in non-responders. However, as previous study mentioned, the most commonly accessed radial artery could yield erroneous information regarding systemic vascular resistance and respirophasic variations in stroke volume as well the PPV (or SVV) obtained from the radial artery would yield inconclusive or inaccurate information 14 , 35 . In the present study, we comprehensively evaluated the ability of the radial artery to predict volume responsiveness from both the respiratory variability of the radial artery pressure and blood flow, which can provide more favorable evidence for the clinical use of the radial artery in evaluating volume state. Thus, it remains to be verified through further studies and more clinical experience. There are several limitations in our study. First, we did not study the ability of FTc and ΔVpeak of radial artery and non-invasive PPV to predict fluid responsiveness in patients during persistent hypotension, hypothermia, septic shock, heart failure, significant valvular heart diseases, or significant radial artery stenosis, therefore, our results cannot be extrapolated to these patients and the generalization of these results may be limited. Further researches are needed to more clearly identify the confounders in order to determine the limitations and indications of these non-invasive assessments of fluid responsiveness. Second, as other dynamic indices based on heart-lung interactions, FTc and ΔVpeak of radial artery and non-invasive PPV have their limitations and could not be used in patients with cardiac arrhythmias or spontaneous breathing. Nevertheless, as previous studies reported 36 , few studies have been conducted to determine the ability of non-invasive indices to predict fluid responsiveness and its cut-off value in discriminating between responders and non-responders to fluid resuscitation in the spontaneously breathing or cardiac arrhythmias patient. Consequently, there is a clear need for a reliable non-invasive method for the assessment of volume status and fluid responsiveness in these patient population and clinical setting. Third, FTc and ΔVpeak might be still reliable in patients with decreased arterial compliance, when the predictive power of non-invasive PPV for fluid responsiveness is reduced. Further studies are needed to test their performance in haemodynamically unstable patients under low perfusion status. In conclusion, the principal finding of this study is that the measures of FTc and ΔVpeak in the radial artery assessed by Doppler ulrasound appears to be the highly feasible and reliable methods to predict fluid responsiveness, which are valuable and interchangeable with non-invasive PPV in patients undergoing gynecological surgery. Thereby, these results suggested that a non-invasive approach using the dynamic variables of fluid responsiveness in order to maintain or to achieve euvolaemia is as possible as the invasive approach and could serve as useful indices to guide fluid therapy during gynecological surgery. Nevertheless, there may be no single index that can guide fluid therapy in all cases, so every clinical finding and all haemodynamic data should be applied when needed. Combining FTc, ΔVpeak, and non-invasive PPV can be used to predict fluid responsiveness in pregnant women and the regression equation for predicting fluid responsiveness is logit P =-28.153 + 0.142FTc-0.543ΔVpeak-1.018PPV. In the future, more clinical investigations and application experience remained to furtherly illuminate and verify their ability for predicting fluid responsiveness and guiding clinic fluid therapy. Declarations Ethics approval and consent to participate This study was approved by the Research Ethics Committee of Women’s Hospital, Zhejiang University School of Medicine (20200197). The written informed consents were provided by all patients. Consent for publication : Not applicable. Availability of data and materials : Not applicable. Competing interests: The authors declare that they have no competing interests. Funding : No. Authors' contributions: Lili Xu, Shaobing Dai, and Jianjun Shen were the major contributors in writing the manuscript. Jianjun Shen experimented and collected the patient data. Shaobing Dai analyzed and interpreted the patient data. Xia Tao directed ultrasonic operation and controled ultrasonic quality. Lili Xu and Xinzhong Chen read and approved the final manuscript. Acknowledgements: This work was supported by Exploration Project of Zhejiang Natural Science Foundation (LY21H090006), Zhejiang Health Science and Technology Planning Project (2021KY768), Bureau of Chinese Medicine, Zhejiang, China (2018ZB065). References Mohsenin V. Assessment of preload and fluid responsiveness in intensive care unit. How good are we? J Crit Care. 2015;30(3):567–73. Pinsky MR. Functional haemodynamic monitoring. Curr Opin Crit Care. 2014; 20: 288–3. Benes J, Giglio M, Brienza N, Michard F. The effects of goal directed fluid therapy based on dynamic parameters on post-surgical outcome: a meta-analysis of randomized controlled trials. Crit Care. 2014; 18: 584. Alvarado Sánchez JI, Caicedo Ruiz JD, Diaztagle Fernández JJ, Amaya Zuñiga WF, Ospina-Tascón GA, Cruz Martínez LE. Predictors of fluid responsiveness in critically ill patients mechanically ventilated at low tidal volumes: systematic review and meta-analysis. Ann Intensive Care. 2021;11(1):28. Renner J, Gruenewald M, Hill M, Mangelsdorff L, Aselmann H, Ilies C, Steinfath M, Broch O. Non-invasive assessment of fluid responsiveness using CNAP™ technology is interchangeable with invasive arterial measurements during major open abdominal surgery. Br J Anaesth. 2017;118(1):58–67. Mesquida J, Kim HK, Pinsky MR. Effect of tidal volume, intrathor-acic pressure, and cardiac contractility on variations in pulse pressure, stroke volume, and intrathoracic blood volume. Intensive Care Med. 2011; 37: 1672–9. Jacques D, Bendjelid K, Duperret S, Colling J, Piriou V, Viale JP. Pulse pressure variation and stroke volume variation during increased intra-abdominal pressure: an experimental study. 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Yao B, Liu JY, Sun YB. Respiratory variation in peripheral arterial blood flow peak velocity to predict fluid responsiveness in mechanically ventilated patients: a systematic review and meta-analysis. BMC Anesthesiol. 2018;18(1):168. Wodey E, Carre F, Beneux X, Schaffuser A, Ecoffey C. Limits of corrected flow time to monitor hemodynamic status in children. J Clin Monit Comput. 2000;16:223–8. Song Y, Kwak YL, Song JW, Kim YJ, Shim JK. Respirophasic carotid artery peak velocity variation as a predictor of fluid responsiveness in mechanically ventilated patients with coronary artery disease. Br J Anaesth. 2014;113(1):61–6. Brandt S, Regueira T, Bracht H, et al. Effect of fluid resuscitation on mortality and organ function in experimental sepsis models. Crit Care. 2009; 13: 186. Wang Y, Gao L, Li JB, Yu C. Assessment of left atrial function by full volume real-time three-dimensional echocardiography and left atrial tracking in essential hypertension patients with different patterns of left ventricular geometric models. Chin Med Sci J. 2013;28:152–8. Muller L, Louart G, Bengler C, et al. The intrathoracic blood volume indexas an indicatorof fluid responsivenessin critically ill patients with acute circulatory failure: a comparison with central venous pressure. Anesth Analg 2008;107: 607–13. Maitra S, Bhattacharjee S, Baidya DK. Correlation Between Doppler Derived Carotid Artery Corrected Flow Time and Pressure Transducer Derived Radial Artery Corrected Flow Time: A Prospective Observational Study. Cardiovasc Eng Technol. 2020;11(2):128–133. Cannesson M, Le Manach Y, Hofer CK, Goarin JP, Lehot JJ, Vallet B, Tavernier B. Assessing the diagnostic accuracy of pulse pressure variations for the prediction of fluid responsiveness: a "gray zone" approach. Anesthesiology. 2011;115(2):231–41. Coste J, Pouchot J. A grey zone for quantitative diagnostic and screening tests. Int J Epidemiol. 2003;32(2):304–13. Derichard A, Robin E, Tavernier B, et al. Automated pulse pressure and stroke volume variations from radial artery: evaluation during major abdominal surgery. Br J Anaesth. 2009; 103: 678–84. Willars C, Dada A, Hughes T, Green D. Functional haemodynamic monitoring: the value of SVV as measured by the LiDCO Rapid in predicting fluid responsiveness in high risk vascular surgical patients. Int J Surg. 2012; 10: 148–52. Monnet X, Dres M, Ferré A, Le Teuff G, Jozwiak M, Bleibtreu A, Le Deley MC, Chemla D, Richard C, Teboul JL. Prediction of fluid responsiveness by a continuous non-invasive assessment of arterial pressure in critically ill patients: comparison with four other dynamic indices. Br J Anaesth. 2012;109(3):330–8. Blehar DJ, Glazier S, Gaspari RJ. Correlation of corrected flow time in the carotid artery with changes in intravascular volume status. J Crit Care. 2014; 29: 486e8. Gan TJ, Soppitt A, Maroof M, et al. Goal-directed intraoperative fluid administration reduces length of hospital stay after major surgery. Anesthesiology. 2002; 97: 820–6. Singer M, Allen MJ, Webb AR, Bennett ED. Effects of alterations in left ventricular fifilling, contractility and systemic vascular resistance on the ascending aortic blood velocity waveform of normal subjects. Crit Care Med. 1991; 19: 1138–45. Singer M, Bennett ED. Noninvasive optimization of left ventricular filling using esophageal Doppler. Crit Care Med 1991; 19: 1132–7. Monnet X, Rienzo M, Osman D, et al. Esophageal Doppler monitoring predicts fluid responsiveness in critically ill ventilated patients. Intensive Care Med. 2005; 31: 1195–201. Vallee F, Fourcade O, De Soyres O, et al. Stroke output variations calculated by esophageal Doppler is a reliable predictor of fluid response. Intensive Care Med. 2005; 31: 1388–93. Yang SY, Shim JK, Song Y, Seo SJ, Kwak YL. Validation of pulse pressure variation and corrected flow time as predictors of fluid responsiveness in patients in the prone position. Br J Anaesth. 2013;110(5):713–20. Feissel M, Michard F, Mangin I, Ruyer O, Faller JP, Teboul JL.Respiratory changes in aortic blood velocity as an indicator of fluid responsiveness in ventilated patients with septic shock. Chest. 2001; 119: 867–73. Brennan JM, Blair JEA, Hampole C, et al. Radial artery pulse pressure variation correlates with brachial artery peak velocity variation in ventilated subjects when measured by internal medicine residents using hand-carried ultrasound devices. Chest 2007;131:1301–7. Pereira de Souza Neto E, Grousson S, Duflo F, Ducreux C, Joly H, Convert J, Mottolese C, Dailler F, Cannesson M. Predicting fluid responsiveness in mechanically ventilated children under general anaesthesia using dynamic parameters and transthoracic echocardiography. Br J Anaesth. 2011;106(6):856–64. Monge Garcia MI, Gil Cano A, Diaz Monrove JC. Brachial artery peak velocity variation to predict fluid responsiveness in mechanically ventilated patients. Crit Care 2009;13: R142. Hong SW, Shim JK, Choi YS, et al. Predictors of ineffectual radial arterial pressure monitoring in valvular heart surgery. J Heart Valve Dis 2009; 18: 546–53. Kim DH, Shin S, Kim N, Choi T, Choi SH, Choi YS. Carotid ultrasound measurements for assessing fluid responsiveness in spontaneously breathing patients: corrected flow time and respirophasic variation in blood flow peak velocity. Br J Anaesth. 2018;121(3):541–549. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfig1.png Supplementary Figure 1. Subject selection process. A total of 85 patients fit inclusion criteria, 5 patients excluded, 80 patients approached for consent, 70 patients studied, 44 patients in the Responders group, 36 patients in the Non-responders group. Supplementaryfig2.jpg Supplementary Figure 2. BlandeAltman plots for inter-observer agreement of radial artery FTc and ΔVpeak.Red dotted lines indicate the mean difference (bias), and black dotted lines indicate the 95% limits of agreement (1.96 x standard deviation). Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1689029","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":108928691,"identity":"1965d9d0-2f31-4b44-ab86-2487a705b87a","order_by":0,"name":"Jianjun Shen","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianjun","middleName":"","lastName":"Shen","suffix":""},{"id":108928692,"identity":"f184c27d-99b9-407a-964f-e94d766f98eb","order_by":1,"name":"Shaobing Dai","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shaobing","middleName":"","lastName":"Dai","suffix":""},{"id":108928693,"identity":"6b60e5ad-8821-4f0a-9415-4efea8f3c271","order_by":2,"name":"Xia Tao","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Tao","suffix":""},{"id":108928694,"identity":"d271d726-14f7-470c-8cab-db553f7b37a5","order_by":3,"name":"Xinzhong Chen","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinzhong","middleName":"","lastName":"Chen","suffix":""},{"id":108928696,"identity":"94db9a59-66d5-4b45-bc97-308ff6262a3d","order_by":4,"name":"Lili Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYFAC5oYDDwxsGBgbgGwe4rQwNhxIMEgjUQtDAsNhCJsoLQY3EhsPJBSct2eekcD44G0bg7w5IS2SMxJBDrvNzDgjgdlwbhuD4c4GAlr4JSBa2IBa2KR52xgSDA4Q0MIG0XKOB6iF/TdRWqC2HJAA2cJMlBbJnocgLckGjD0PmyXnnJMw3EBIi8Hx5MMfPvyxszdsTz744U2ZjTxBW+DAsAEcmRLEqgcCeRLUjoJRMApGwQgDANoyP/hRJZJwAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2022-05-24 13:59:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1689029/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1689029/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21964454,"identity":"878bc3a6-9e62-4819-bc95-f525f8410204","added_by":"auto","created_at":"2022-05-27 14:25:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":846138,"visible":true,"origin":"","legend":"\u003cp\u003eIndividual responses to fluid challenge and ROC curve for FTc, ΔVpeak and PPV. \u003c/p\u003e\u003cp\u003eUpper row: individual responses to fluid challenge for FTc (A), ΔVpeak (B) and PPV (C). Responders are presented as blue full line and closed circles; Non-responders are presented as red dashed line and open circles. \u003c/p\u003e\u003cp\u003eLower row: receiver operating characteristic curves showing the ability of FTc (D), ΔVpeak (E) and PPV (F) before fluid challenge to discriminate responders and non-responders. \u003c/p\u003e\u003cp\u003eThe areas under the curves for FTc, ΔVpeak and PPV were 0.802 (95% confidence interval 0.706-0.898), 0.812 (95% confidence interval 0.714-0.909), and 0.846 (95% confidence interval 0.762-0.930), respectively.\u003c/p\u003e\u003cp\u003eFTc：radial artery corrected flow time；ΔVpeak：respirophasic variation in radial artery blood flow peak velocity; PPV：pulse pressure variation.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1689029/v1/d88c9e77ada154d57ab78d9a.png"},{"id":21963905,"identity":"6af2e7c8-c10e-471c-b5cd-e6cb9417ea48","added_by":"auto","created_at":"2022-05-27 14:20:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150793,"visible":true,"origin":"","legend":"\u003cp\u003eExample ultrasound images of radial artery FT\u003csub\u003e \u003c/sub\u003eand Vpeak.\u0026nbsp;\u003c/p\u003e\u003cp\u003eRadial artery FT and Vpeak were measured adjacent to the radial head. FTc was calculated by using a simplified formula FTc = FT+ [1.29 x (HR - 60)]\u003csup\u003e12\u003c/sup\u003e. ΔVpeak was calculated as follows: (maximum peak velocity-minimum peak velocity) / [(maximum peak velocity+minimum peak velocity)/2]x100\u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFT： radial artery flow time；Vpeak： radial artery blood flow peak velocity.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1689029/v1/812b9ccab824b3906f0e6df4.png"},{"id":21964455,"identity":"07177ffc-51c4-4133-a9f3-b443d7a5ff0d","added_by":"auto","created_at":"2022-05-27 14:25:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":641530,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1689029/v1/3245db51-ec5b-4f1e-9706-7c1a0a966a50.pdf"},{"id":21963892,"identity":"f027ec27-3ed0-4717-bc01-2ae54f0c1913","added_by":"auto","created_at":"2022-05-27 14:20:44","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":68373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1.\u003c/strong\u003e Subject selection process. \u003c/p\u003e\u003cp\u003eA total of 85 patients fit inclusion criteria, 5 patients excluded, 80 patients approached for consent, 70 patients studied, 44 patients in the Responders group, 36 patients in the Non-responders group.\u003c/p\u003e","description":"","filename":"Supplementaryfig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1689029/v1/efd12df079b3eac3588aa463.png"},{"id":21963911,"identity":"e2e1b1a6-5b2c-44d9-ab9f-2568a84fa73a","added_by":"auto","created_at":"2022-05-27 14:20:44","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":108069,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2.\u003c/strong\u003e BlandeAltman plots for inter-observer agreement of radial artery FTc and ΔVpeak.\u003c/p\u003e\u003cp\u003eRed dotted lines indicate the mean difference (bias), and black dotted lines indicate the 95% limits of agreement (1.96 x standard deviation).\u003c/p\u003e","description":"","filename":"Supplementaryfig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1689029/v1/0c35e3a369589de920e7d32c.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Corrected flow time and respirophasic variation in blood flow peak velocity of radial artery predict fluid responsiveness in gynecological surgical patients with mechanical ventilation","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePerioperative volume management is an important part of clinical anesthesia work and is crucial to prevent postoperative complications and smooth recovery of patients. Insufficient infusion can cause low perfusion of heart, kidney, brain and other important organs, microcirculation disorder, and organ dysfunction, while excessive infusion can cause postoperative intra-abdominal hypertension, affect the recovery of gastrointestinal function after anastomotic healing, and increase the probability of systemic infection. In this context, a proper assessment of volume status, coupled with proper fluid management, can optimize the hemodynamics of patients and avoid ineffective or even harmful fluid infusion\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Over the past decades, a large body of evidence has showed that various dynamic parameters (both invasive and non-invasive) have emerged with a high sensitivity and specificity for predicting fluid responsiveness (FR)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Among them, non-invasive pulse pressure variation (PPV) has been suggested as a simple indicator of liquid reactivity, as non-invasive and easy access\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, however, it has also been reported that volume status cannot be accurately reflected in certain cases such as cardiac arrhythmias, increased intrathoracic or abdominal pressure, and reduced lung compliance\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecently, ultrasonic Doppler for measuring blood flow of superficial artery has been reported to be used in operation care settings due to its advantages of convenience, non-invasive, far away from the surgical field, and low technical requirements\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Importantly, carotid artery corrected flow time (FTc) has been validated as an acceptable and regenerative method for the identification of fluid responsiveness in critically ill patients with undifferentiated shock\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Interestingly, after Kim et al.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e confirmed that respirophasic variation in blood flow peak velocity (ΔVpeak) of carotid artery has the similar capability to predict fluid responsiveness in infants undergoing cardiac surgery, a systematic meta-analysis of Yao et al.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e further described that ΔVpeak of carotid artery had more value than brachial artery in predicting fluid Responsiveness in mechanically ventilated patients. However, we still could not know that whether radial artery FTc and ΔVpeak could assess the effect of a fluid challenge and is ideally suited to guide fluid resuscitation in mechanically ventilated patients. Our aim was to compare the ability of ultrasonic measurements of radial artery FTc and ΔVpeak to predict fluid responsiveness with non-invasive PPV in mechanically ventilated patients undergoing gynecological surgery.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePatients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter approved by the Research Ethics Committee of Women\u0026rsquo;s Hospital, Zhejiang University School of Medicine (20200197), this study was registered at the Chinese Clinical Trial Registry (ChiCTR)(www.chictr.org) on 2020/12/01 with the registration number ChiCTR-ICR-2000040941. After receiving informed consents, and was This study was performed, The written were provided by all patients. Eighty patients, American Society of Anesthesiologists (ASA) Class I-II, undergoing gynecological surgery under general anesthesia from 2020/12/14 to 2021/02/28 were recruited into the study. Exclusion criteria were pregnancy, \u0026le;18 years of old, BMI\u0026gt;30 or \u0026lt;15kg/m-2, patients receiving vasoactive or inotropic support before induction of anaesthesia, or with left ventricular ejection fraction (LVEF) less than 45%, pre-existing peripheral arterial occlusive disease, cardiovascular disease, hypertension, pulmonary hypertension, chronic lung disease, abnormal chest wall, atrial or ventricular arrhythmia, diabetes mellitus, cerebrovascular disease and so on.\u003c/p\u003e\n\u003cp\u003einduction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnaesthetic management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon arrival in the operating theatre, a three-lead electrocardiogram (ECG), pulse oximetry (SpO\u003csub\u003e2\u003c/sub\u003e), and non-invasive arterial pressure monitoring were applied. After anesthesia induction of midazolam 0.04 mg/kg cisatracurium 0.2 mg/kg propofol 2 mg/kg sufentanil 0.5 \u0026mu;g/kg, endotracheal intubation was performed. Respiratory setting of anesthesia machine (Aestiva, GE/Datex-Ohmeda) are set as follows: volume-controlled ventilation (VCV), inspiratory-expiratory (I:E) ratio of 1:2, respiratory rate of 8-10 bpm, tidal volume of 8 mL/kg of ideal weight [45.5+0.91x (height in cm-152.4)], and PEEP of 5 cm H\u003csub\u003e2\u003c/sub\u003eO in 50% oxygen with air. Respiratory settings were adjusted to maintain the P\u003csub\u003eET\u003c/sub\u003eCO\u003csub\u003e2\u003c/sub\u003e at less than 50 mmHg. Anesthesia was maintained with continuous infusion of remifentanil (0.05-0.2 \u0026mu;g/kg\u003csup\u003e-1\u003c/sup\u003e min\u003csup\u003e-1\u003c/sup\u003e), propofol (50-100\u0026mu;g/kg\u003csup\u003e-1\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e), and sevoflurane (1.5%\u0026ndash;2.5%) and intermittent injection of cisatracurium 0.05 mg/kg as needed to keep the entropy scale between 40 and 60 and for muscle relaxation. The mean arterial pressure was keep between 60 and 80mmHg.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll measurements including the mean arterial pressure, heart rate, FTc and \u0026Delta;Vpeak of radial\u0026nbsp;artery, non-invasive PPV, and stroke volume index (SVI) were recorded 15min after anesthesia induction and 10min after a fluid loading of 6ml/kg of 6% hydroxyethyl starch 130/0.4. Non-invasive PPV was acquired from the radial artery pressure waveform, using a real-time radial artery blood pressure and hemodynamics monitoring system (TL-400, Zhejiang Shanshi Biological Pharmaceutical Equipment Co., Ltd, Hangzhou, China). Averages of PPV auto over four cycles of 8 s were displayed in real time on the monitor and was calculated by automatic detection algorithms without airway pressure acquisition. The parameters were recorded by an anesthesiologist who was unaware of this study to avoid any personal bias. Rartery FTc and \u0026Delta;Vpeak measured by two independent sonographer who were blinded to each other\u0026rsquo;s Doppler results and haemodynamic variables of the patients, using an 6-13 MHz variable frequency linear probe ((SONIMAGE HS1, Konica Minolta Inc, Shanghai, China) (Figure 2). The optimal long-axis view was gained at the radial artery on the B-mode real-time image. The sampling site was located in the centre of the lumen, adjacent to the radial head and the ultrasonic beams were adjusted to ensure \u0026lt;60\u0026nbsp;of angle from the direction of blood flow. The radial artery blood flow waveforms were stored using pulsed wave Doppler for the measurement of FTc and \u0026Delta;Vpeak. FT is measured from the beginning of the upstroke to the trough of the incisural notch on a pulse waveform analysis. FTc was calculated by using a simplified formula: FTc=FT+[1.29x (HR-60)]\u003csup\u003e13\u003c/sup\u003e as evaluating a single cycle after several consecutive cycles became stable and reached the level of acceptable quality. The maximum and minimum values of the peak velocity during one respiratory cycle were measured automatically and recorded. \u0026Delta;Vpeak was calculated as follows: (max peak velocity-min peak velocity)/[(max peak velocity+min peak velocity)/2]\u0026times;100\u003csup\u003e14\u003c/sup\u003e. SVI was recorded by transthoracic echocardiography with a 1.5-4.5 MHz phased array probe from aortic blood flow. The diameter of the left ventricular outflow tract was determined by using the ultrasound images\u0026nbsp;of the largest opening of the aortic valve on the parasternal long-axis view and the left ventricular outflow tract area was calculated as \u0026pi; x (left ventricular outflow tract diameter/2)\u003csup\u003e15\u003c/sup\u003e. The aortic flow time velocity integral was obtained in the apical five-chamber view at the level of the aortic annulus and from the mean of five consecutive beats of a complete respiratory cycle. SVI and BSA were computed by two formulas as follows: (left ventricular out-flow tract area x aortic flow time velocity integral)/body surface area (BSA), BSA (m2) = 0.0061x body length (cm)+0.0128 x body weight (kg)-0.1529\u003csup\u003e16\u003c/sup\u003e. All values represented the mean of three consecutive measurements and the mean of the two sonographer was employed for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy endpoints\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary endpoint was to determine the predictive value of FTc, \u0026Delta;Vpeak, and non-invasive PPV for fluid responsiveness (\u0026ge;15% increases in SVI after fluid challenge) in mechanically ventilated patients\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS 23 (SPSS Inc., Chicago, IL, USA) and PASS 14.0.5 (NCSS Statistical Software, Kaysville, UT, USA) were applied for statistical analysis and calculating the sample size. It has been reported that the area under the receiver operating characteristic (AUROC) curve of FTc measured in the radial artery to predict fluid responsiveness was 0.84\u003csup\u003e18\u003c/sup\u003e, so we assumed that the AUROC curve of radial artery FTc was 0.75. At least 42 patients were required to detect a difference of 0.25 between the AUROC curves of radial artery FTc (0.75) and non-invasive PPV (0.5), with an 0.9 power and type I error of 0.05, assuming 55% incidence of fluid responsiveness in patients undergoing elective\u0026nbsp;gynecological\u0026nbsp;surgery\u003csup\u003e8\u003c/sup\u003e. We used a sample size of 46 patients considering a possible 10% drop out rate. Fluid responsiveness was determined by a 15% or more increase in SVI after fluid challenge.\u003c/p\u003e\n\u003cp\u003eNormality of the data distribution was assessed using Kolmogorove-Smirnov and Shapiroe-Wilk tests. Continuous variables were expressed as mean (standard deviation) if data were normally distributed or median (interquartile range) if not. Categorical variables were expressed as absolute number (%). Responder and non-responder groups were compared with a paired t-test for normally distributed data, ManneWhiney U-test for non-normally distributed data, and X\u003csup\u003e2\u003c/sup\u003e test or Fisher\u0026rsquo;s exact test, as appropriate, for categorical variables. Pearson correlation coefficient was used to test the relationship between baseline values or the relative changes in haemodynamic variables and the percentage change in SVI after fluid challenge. Multivariate logistic regression analyses were performed to identify multivariate predictors of fluid responsiveness and receiver operating characteristic (ROC) curve was performed to assess the abilities of the two ultrasound-derived parameters, FTc and \u0026Delta;Vpeak, non-invasive PPV to predict fluid responsiveness. The predictive accuracy of the ROC analysis was described as follows: poor [area under the curve (AUC) 0.6\u0026ndash;0.7], fair (AUC 0.7\u0026ndash;0.8), good (AUC 0.8\u0026ndash;0.9), and excellent (AUC 0.9\u0026ndash;1.0). The 95% confidence interval (CI) was calculated, and statistical significance was accepted as p\u0026lt;0.05. The \u0026ldquo;optimal\u0026rdquo; cut-off values were assessed by using maximizing Youden\u0026rsquo;s index (J=Sensitivity+Specificity-1=Sensitivity-False-Positive Rate)\u003csup\u003e19\u003c/sup\u003e. The cut-off values defining the gray area was determined by a correlation value of 90% sensitivity and 90 specificity\u003csup\u003e20\u003c/sup\u003e. Importantly, the intra-observer variability (repeatability) and inter-observer variability (reproducibility) were evaluated in 30 randomly chosen selected sets of assessments of FTc and \u0026Delta;Vpeak. Variability was tested by dividing the absolute difference between the two values by their average value. Accordingly, the inter-observer reproducibility for FTc and \u0026Delta;Vpeak was also recognized in all data sets by calculating a coefficient of variation (CV) and an intraclass correlation coefficient (ICC). BlandeAltman plot was applied to test the inter-observer agreement in estimating FTc and \u0026Delta;Vpeak. A P-value \u0026lt;0.05 (two-tailed).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003ePatients\u003c/h2\u003e\n \u003cp\u003eOf the 85 patients assessed for eligibility, 5 were excluded because of history of cardiovascular disease (n\u0026thinsp;=\u0026thinsp;1), refusal to participate (n\u0026thinsp;=\u0026thinsp;2), and other reasons(n\u0026thinsp;=\u0026thinsp;2). Therefore, 80 subjects were enrolled in the final analysis (Supplementary Fig. 1). The main characteristics of the subjects were comparable between responders (n\u0026thinsp;=\u0026thinsp;44) and non-responders (n\u0026thinsp;=\u0026thinsp;36) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient Characteristics.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponders group (n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-responders group (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASA (I/II)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration of Surgery (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.4\u0026thinsp;\u0026plusmn;\u0026thinsp;60.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.3\u0026thinsp;\u0026plusmn;\u0026thinsp;31.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eValues are numbers or means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 compared with Responders group\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eBMI: Body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e); HR: Heart rate; MAP: Mean arterial pressure.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003eHaemodynamic variables before and after fluid challenge\u003c/h2\u003e\n \u003cp\u003eIn both responders and non-responders, fluid challenge significantly increased FTc and SVI, while significantly decreased \u0026Delta;Vpeak and non-invasive PPV(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Before the fluid challenge, FTc and SVI were significantly lower in responders than in non-responders (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), however, \u0026Delta;Vpeak and non-invasive PPV was significantly higher in responders than in non-responders (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, after fluid challenge, FTc, \u0026Delta;Vpeak, and SVI were all not significantly different between the two groups (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Both MAP and HR were not significantly different between the two groups before and after the fluid challenge (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHemodynamic variables before and after fluid challenge.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eResponders group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNon-responders group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003e(ms)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e315.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e348.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e335.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2#\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e354.7\u0026thinsp;\u0026plusmn;\u0026thinsp;24.5*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.184\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;Vpeak (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9#\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.136\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5#\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.957\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSVI (ml m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026minus;\u0026thinsp;2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8#\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e38.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.545\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAP (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e77.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e75.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e75.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.411\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.301\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(beat min-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e64.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e71.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e60.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.356\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.134\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eData are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 compared with before fluid challenge. #p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 compared with Responders group\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eFT\u003csub\u003eC\u003c/sub\u003e: radial artery corrected flow time;\u0026Delta;Vpeak༚ respirophasic variation in radial artery blood flow peak velocity༛PPV༚pulse pressure variation; SVI༚stroke volume index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003eThe ability of FTc and \u0026Delta;Vpeak to predict fluid responsiveness\u003c/h2\u003e\n \u003cp\u003eFTc, \u0026Delta;Vpeak, and non-invasive PPV were proved to be the independent predictors for fluid responsiveness by multivariate logistic regression, with the odds ratios of 1.152(95% CI 1.045 to 1.270), 0.581 (95% CI 0.403 to 0.839), and 0.361 (95% CI, 0.193 to 0.676), respectively (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The regression equation for predicting fluid responsiveness in pregnant women is logit \u003cem\u003eP\u003c/em\u003e=-28.153\u0026thinsp;+\u0026thinsp;0.142 FTc-0.543\u0026Delta;Vpeak-1.018 PPV. The area under the ROC curve of fluid responsiveness predicted by FTC was 0.802 (95% CI, 0.706\u0026ndash;0.898), and \u0026Delta;Vpeak was 0.812 (95% CI, 0.091\u0026ndash;0.286), which were comparable with non-invasive PPV (0.846, 95%CI, 0.070\u0026ndash;0.238)(Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The sensitivity and specificity for FTc and Vpeak are 75.3%, 75.9% and 88.2%, 67.9% (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Their cut-off values for FTc and Vpeak are 336.6 ms and 14.2% (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariate logistic regression analyses identified the factors that were independently associated with fluid responsiveness.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFTC (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.152 (1.045\u0026ndash;1.270)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;Vpeak (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.581(0.403\u0026ndash;0.839)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePPV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.361(0.193\u0026ndash;0.676)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eFT\u003csub\u003eC\u003c/sub\u003e: radial artery corrected flow time;\u0026Delta;Vpeak༚respirophasic variation in radial artery blood flow peak velocity; PPV༚pulse pressure variation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrediction of fluid responsiveness by receiver operating characteristic curves of the baseline FTc, \u0026Delta;Vpeak and PPV.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUROC curve\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOptimal cut-off\u003c/p\u003e\n \u003cp\u003evalue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGrey zone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients in grey zone (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYouden index (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFTc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.802 (0.706\u0026ndash;0.898)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336.6 ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e313.5-336.6 ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75(0.66\u0026ndash;0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76(0.71-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;Vpeak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.812 (0.714\u0026ndash;0.909)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2\u0026ndash;16.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88(0.79\u0026ndash;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68(0.60\u0026ndash;0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003cp\u003e(0.762\u0026ndash;0.930)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u0026ndash;11.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48(60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74(0.55\u0026ndash;0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54(0.46\u0026ndash;0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eAUROC, area under the receiver operating characteristic; CI, confidence interval; FTC: radial artery corrected flow time;\u0026Delta;Vpeak༚respirophasic variation in radial artery blood flow peak velocity; PPV༚pulse pressure variation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e* Optimal cut-off values were determined by maximising the Youden index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec12\"\u003e\n \u003ch2\u003eThe inter-observer agreement in estimating FTc and \u0026Delta;Vpeak\u003c/h2\u003e\n \u003cp\u003eFor FTc measurements, intra-observer variability and inter-observer variability were 0.5 (0.3)% and 1.1 (0.9)%, respectively. For \u0026Delta;Vpeak measurements, inter-observer variability was 7.0 (9.3)% and 5.6 (2.8)%, respectively. Inter-observer reproducibility for estimating FTc was excellent, with an ICC of 0.97 (95% CI, 0.948\u0026ndash;0.984) and a CV of 5.6%. Inter-observer reproducibility for estimating \u0026Delta;Vpeak was also excellent, with an ICC of 0.98 (95% CI, 0.973\u0026ndash;0.988) and a CV of 28.7%. Using Bland-Altman analysis for evaluating inter-observer agreement in estimating FTc and \u0026Delta;Vpeak, the mean biases were \u0026minus;\u0026thinsp;0.26 ms [with 95% limits of agreement (LOA) between \u0026minus;\u0026thinsp;9.34 and 8.82 ms] and 0.41% (with 95% LOA between \u0026minus;\u0026thinsp;1.19% and 2.00%), respectively (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver the last decade, numerous studies have proved the usefulness of dynamic indices, such as SVV and PPV, based on the observation that changes in respiratory phase of stroke volume in patients on mechanical ventilation are closely related to the position of the Frank-Starling curve guiding volume resuscitation because commonly used static indicators of fluid responsiveness are not accurate predictors of the effects of fluid administration\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In conjunction, efforts are being made to validate the role of these variables as the integral parts of goal-directed therapy to improve patient outcomes\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong the dynamic indices, PPV originated from arterial waveform analysis is the representative indices for fluid responsiveness and reflects the percentage change in pulse pressure attributable to periodic changes in intrathoracic pressure caused by mechanical ventilation\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Based on the premise that non-invasive and readily accessed indices are undoubtedly advantageous, non-invasive PPV has been suggested as a simple indicator of fluid responsiveness and have been shown to be able to track changes in PPV reliably and yield a concordance rate of 91% in comparison to invasive PPV measured by PiCCO technology during major open abdominal surgery\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In general, our results showed that non-invasive PPV were predictive of fluid responsiveness in mechanical ventilated patients undergoing gynecological surgery, with the area under the ROC curve of 0.846 (95%CI, 0.762\u0026ndash;0.930), a cut-off value of 11.5%, and a grey zone between 7.5 and 11.5%, which are in accordance with the above-mentioned study. However, several exiting factors including technical factors, arterial compliance, cardiac arrhythmias, increased intrathoracic pressure by large tidal volume or PEEP, increased abdominal pressure, and reduced lung compliance restrict its widespread use during surgery\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecently, ultrasonic Doppler for measuring blood flow of superficial artery has been proved to be successful in predicting the fluid responsiveness\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. FTc is a complex static index and has been used and evaluated as a preload indication to predict fluid responsiveness in different surgical settings and the use of FTc for intraoperative volume optimization has been reported to reduce the incidence of complications, improve patients\u0026rsquo; recovery, and decrease postoperative hospital stay\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. It is affected by several factors, such as preload afterload and inotropic state and is even inversely related to afterload and systemic vascular resistance\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. However, low FTc does not always correspond to low left ventricular preload and can even represent a volume overload state, which means that simple fluid challenge guided by only FTc could further aggravate deterioration in haemodynamic conditions\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. We therefore tested the predictive accuracy of FTc to discriminate between responders and non-responders according to a volume load during gynecological surgery compared with non-invasive PPV. Based on our findings, FTc and non-invasive PPV accurately predicted FR attributable to both volume-loading manoeuvres, indicating interchangeability of these variables in this specific patient population. In this context, ROC analysis yielded similar levels of AUCs for non-invasive approaches during fluid resuscitation in the OR. With respect to statistical comparison of the ascertained AUC values, we found that FTc can predict fluid response in mechanical ventilated patient and no significant differences between FTc and non-invasive PPV. However, our findings appear to contradict those of two previous studies\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, where FTc was not a predictor of fluid responsiveness. It is possible that patients with haemodynamic conditions that would prevent FTc from predicting fluid responsiveness were not excluded and vasoconstriction by norepinephrine may cause low FTc regardless of left ventricular preload state, which could be why FTc failed to predict fluid responsiveness in both studies. Instead, several recent studies strongly confirmed our results. For example, Lee et al.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e demonstrated that FTc and PPV are better than CVP and LVEDAI in predicting fluid responsiveness in neurosurgical patients. Yang et al.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e both FTc and PPVauto were accurate predictors of fluid responsiveness in patients in the supine position and the prone position using a Wilson frame undergoing lumbar spine surgery. In addition, MAITRA et al.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e addressed that pressure transducer derived radial artery cFT correlated with Doppler derived carotid artery cFT and may be a reasonable predictor of volume responsiveness. In this context, FTc can be used to evaluate the effect of the treatments administered or can be integrated as a limit to optimize CO while avoiding excessive fluid loading. However, there may be no single parameter that can guide fluid therapy under all situations, FTc may be extremely useful when interpreted in conjunction with other clinical information, and measurements such as non-invasive PPV.\u003c/p\u003e \u003cp\u003eUp to now, numerous studies have been conducted to determine the ability of ΔVpeak to predict fluid responsiveness and its cut-off value in discriminating between responders and non-responders to fluid resuscitation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Accordingly, the ΔVpeak of aortic artery, carotid artery and brachial artery have been successively prove to be the accurate method of evaluating preload and the promising variable shown to predict fluid responsiveness in ventilated surgical patients, critically ill patients or different kinds of shock\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Of note, the finding of Neto et al.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e confirmed that ΔVpeak were the most appropriate for prediction of fluid responsiveness when compared with the invasive and noninvasive dynamic variables derived from the arterial pressure and the plethysmographic waveforms in children under general anaesthesia and mechanical ventilation in the operating theatre. Suggested cut-off values of ΔVpeak are 7\u0026ndash;20%\u003csup\u003e33\u003c/sup\u003e, which is possibly attributable to the variations in study population, such as surgical patients without concomitant disease or critically ill patients. In the same context, the radial artery is peripheral artery and also provides easy accessibility\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The radial artery ΔVpeak as determined by ultrasonic Doppler is a non-invasive and practical bedside monitor, there might be a role for radial artery ΔVpeak as a predictor of fluid responsiveness in certain clinical situations\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Based on these theoretical advantages, we investigated the feasibility and predictive power of Doppler-acquired respirophasic radial flow dynamics on fluid responsiveness in mechanically ventilated patients. As our results indicated, the predictability of radial artery ΔVpeak was superior to that of non-invasive PPV with excellent interobserver agreement. Moreover, radial artery ΔVpeak yielded a cut-off value with the highest sensitivity and specificity. Interestingly, we also found that radial artery ΔVpeak also showed a significant increase in non-responders after the fluid challenge, suggesting its strong association with preload, while non-invasive PPV showed a significant decrease after fluid challenge even in non-responders. However, as previous study mentioned, the most commonly accessed radial artery could yield erroneous information regarding systemic vascular resistance and respirophasic variations in stroke volume as well the PPV (or SVV) obtained from the radial artery would yield inconclusive or inaccurate information\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In the present study, we comprehensively evaluated the ability of the radial artery to predict volume responsiveness from both the respiratory variability of the radial artery pressure and blood flow, which can provide more favorable evidence for the clinical use of the radial artery in evaluating volume state. Thus, it remains to be verified through further studies and more clinical experience.\u003c/p\u003e \u003cp\u003eThere are several limitations in our study. First, we did not study the ability of FTc and ΔVpeak of radial artery and non-invasive PPV to predict fluid responsiveness in patients during persistent hypotension, hypothermia, septic shock, heart failure, significant valvular heart diseases, or significant radial artery stenosis, therefore, our results cannot be extrapolated to these patients and the generalization of these results may be limited. Further researches are needed to more clearly identify the confounders in order to determine the limitations and indications of these non-invasive assessments of fluid responsiveness. Second, as other dynamic indices based on heart-lung interactions, FTc and ΔVpeak of radial artery and non-invasive PPV have their limitations and could not be used in patients with cardiac arrhythmias or spontaneous breathing. Nevertheless, as previous studies reported\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, few studies have been conducted to determine the ability of non-invasive indices to predict fluid responsiveness and its cut-off value in discriminating between responders and non-responders to fluid resuscitation in the spontaneously breathing or cardiac arrhythmias patient. Consequently, there is a clear need for a reliable non-invasive method for the assessment of volume status and fluid responsiveness in these patient population and clinical setting. Third, FTc and ΔVpeak might be still reliable in patients with decreased arterial compliance, when the predictive power of non-invasive PPV for fluid responsiveness is reduced. Further studies are needed to test their performance in haemodynamically unstable patients under low perfusion status.\u003c/p\u003e \u003cp\u003eIn conclusion, the principal finding of this study is that the measures of FTc and ΔVpeak in the radial artery assessed by Doppler ulrasound appears to be the highly feasible and reliable methods to predict fluid responsiveness, which are valuable and interchangeable with non-invasive PPV in patients undergoing gynecological surgery. Thereby, these results suggested that a non-invasive approach using the dynamic variables of fluid responsiveness in order to maintain or to achieve euvolaemia is as possible as the invasive approach and could serve as useful indices to guide fluid therapy during gynecological surgery. Nevertheless, there may be no single index that can guide fluid therapy in all cases, so every clinical finding and all haemodynamic data should be applied when needed. Combining FTc, ΔVpeak, and non-invasive PPV can be used to predict fluid responsiveness in pregnant women and the regression equation for predicting fluid responsiveness is logit \u003cem\u003eP\u003c/em\u003e=-28.153\u0026thinsp;+\u0026thinsp;0.142FTc-0.543ΔVpeak-1.018PPV. In the future, more clinical investigations and application experience remained to furtherly illuminate and verify their ability for predicting fluid responsiveness and guiding clinic fluid therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee of Women\u0026rsquo;s Hospital, Zhejiang University School of Medicine (20200197). The written informed consents were provided by all patients.\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication :\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable.\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials :\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eFunding :\u0026nbsp;\u003c/strong\u003eNo.\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLili Xu, Shaobing Dai, and Jianjun Shen were the major contributors in writing the manuscript. Jianjun Shen experimented and collected the patient data. Shaobing Dai analyzed and interpreted the patient data. Xia Tao directed ultrasonic operation and controled ultrasonic quality. Lili Xu and Xinzhong Chen read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThis work was supported by Exploration Project of Zhejiang Natural Science Foundation (LY21H090006), Zhejiang Health Science and Technology Planning Project (2021KY768), Bureau of Chinese Medicine, Zhejiang, China (2018ZB065).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eMohsenin V. Assessment of preload and fluid responsiveness in intensive care unit. How good are we? J Crit Care. 2015;30(3):567\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePinsky MR. Functional haemodynamic monitoring. 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Validation of pulse pressure variation and corrected flow time as predictors of fluid responsiveness in patients in the prone position. Br J Anaesth. 2013;110(5):713\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFeissel M, Michard F, Mangin I, Ruyer O, Faller JP, Teboul JL.Respiratory changes in aortic blood velocity as an indicator of fluid responsiveness in ventilated patients with septic shock. Chest. 2001; 119: 867\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBrennan JM, Blair JEA, Hampole C, et al. Radial artery pulse pressure variation correlates with brachial artery peak velocity variation in ventilated subjects when measured by internal medicine residents using hand-carried ultrasound devices. Chest 2007;131:1301\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePereira de Souza Neto E, Grousson S, Duflo F, Ducreux C, Joly H, Convert J, Mottolese C, Dailler F, Cannesson M. Predicting fluid responsiveness in mechanically ventilated children under general anaesthesia using dynamic parameters and transthoracic echocardiography. Br J Anaesth. 2011;106(6):856\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMonge Garcia MI, Gil Cano A, Diaz Monrove JC. Brachial artery peak velocity variation to predict fluid responsiveness in mechanically ventilated patients. Crit Care 2009;13: R142.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHong SW, Shim JK, Choi YS, et al. Predictors of ineffectual radial arterial pressure monitoring in valvular heart surgery. J Heart Valve Dis 2009; 18: 546\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKim DH, Shin S, Kim N, Choi T, Choi SH, Choi YS. Carotid ultrasound measurements for assessing fluid responsiveness in spontaneously breathing patients: corrected flow time and respirophasic variation in blood flow peak velocity. Br J Anaesth. 2018;121(3):541\u0026ndash;549.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"corrected flow time, respirophasic variation in blood flow peak velocity, radial artery, ultrasonography, fluid responsiveness, gynecological","lastPublishedDoi":"10.21203/rs.3.rs-1689029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1689029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND: \u003c/strong\u003eRecent evidence suggests that ultrasound measurements of carotid and brachial artery corrected flow time (FTc) and respirophasic variation in blood flow peak velocity (ΔVpeak) are valuable for predicting fluid responsiveness in mechanical ventilated patients. We performed the study to reveal the performance of ultrasonic measurements of radial artery FTc, and ΔVpeak for predicting fluid responsiveness in mechanical ventilated patients undergoing gynecological surgery.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMETHODS:\u003c/strong\u003e A total of eighty mechanical ventilated patients were enrolled. Radial\u0026nbsp;artery FTc and ΔVpeak, and non-invasive PPV were measured before and after fluid challenge. Fluid responsiveness was defined as an increase in stroke volume index (SVI) of 15% or more after the fluid challenge. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRESULTS:\u003c/strong\u003e Forty-four (55%) patients were fluid responders. Multivariate logistic regression analysis showed that radial artery FTc and ΔVpeak were the independent predictors of fluid responsiveness, with odds ratios of 1.152 [95% confidence interval (CI) 1.045 to 1.270] and 0.581 (95% CI 0.403 to 0.839). The area under the ROC curve of fluid responsiveness predicted by FTc was 0.802 (95% CI, 0.706-0.898), and ΔVpeak was 0.812 (95% CI, 0.714-0.909). The optimal cut-off values of FTc for fluid responsiveness was 336.6 ms (sensitivity of 75.3%; specificity of 75.9%), ΔVpeak was 14.2% (sensitivity of 88.2%; specificity of 67.9%). The grey zone for FTc was 313.5-336.6 ms, ΔVpeak was 12.2-16.5%.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCONCLUSIONS:\u003c/strong\u003e Ultrasound measurement of radial artery FTc and ΔVpeak are the feasible and reliable methods for predicting fluid responsiveness in mechanically ventilated patients.\u003c/p\u003e\u003cp\u003eThe trial was registered at the Chinese Clinical Trial Registry (ChiCTR)(www.chictr.org), registration number ChiCTR-ICR-2000040941.\u003c/p\u003e\u003cp\u003eThis study was approved by the Research Ethics Committee of Women’s Hospital, Zhejiang University School of Medicine (20200197).\u003c/p\u003e","manuscriptTitle":"Corrected flow time and respirophasic variation in blood flow peak velocity of radial artery predict fluid responsiveness in gynecological surgical patients with mechanical ventilation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-27 14:20:42","doi":"10.21203/rs.3.rs-1689029/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92ccf48c-3eda-4fc8-b387-7207cd663036","owner":[],"postedDate":"May 27th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-05-27T14:20:44+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-27 14:20:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1689029","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1689029","identity":"rs-1689029","version":["v1"]},"buildId":"wLkW0s4AflPzk-lpfg-fK","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}