Case Report: Anesthetic Management and Electrical Cardiometry as Intensive Hemodynamic Monitoring During Cheiloplasty in an Infant With Enzyme-Replaced Pompe Disease and Preserved Preoperative Cardiac Function

Introduction: Pompe disease is caused by deficiency of the lysosomal enzyme acid α-glucosidase, which results in cardiac and muscular complications that can jeopardize perioperative outcomes. We report a 4-month-old infant with Pompe disease receiving cheiloplasty under general anesthesia with the aid of peripheral nerve blocks and intensive hemodynamic monitoring.Case Description: This case report describes a 4-month-old full-term Taiwanese female infant who presented with left unilateral cleft lip and palate in the prenatal examination. She was diagnosed with infantile-onset Pompe disease after acidic α-glucosidase (GAA) gene sequencing. She also received enzyme replacement therapy (ERT) 15 days after birth and regular ERT every other week. Cheiloplasty was performed under general anesthesia uneventfully, and peripheral nerve blocks were adopted for analgesia. Intensive hemodynamic monitoring using electrical cardiometry technology (ICON®) and pulse contour analysis (FloTrac system) were applied during the operation. No adverse effects were observed, and the wound healed well. Therefore, the patient was discharged 4 days after surgery.Conclusion: With the availability of ERT, severe organ dysfunction in infantile-onset Pompe disease patients is no longer common. However, moderate cardiac depression can still occur while increasing inspiratory pressure and deepening the anesthesia level despite a normal preoperative echocardiogram report. Therefore, careful, gradual titration is desirable. Furthermore, electrical cardiometry can detect hemodynamic changes more instantaneously and reliably than pulse contour analysis. In addition, we suggest taking advantage of the peripheral nerve block as a part of balanced anesthesia to alleviate the cardiac suppression caused by general anesthesia.

Model-based approach to investigate equipment-induced error in pressure-waveform derived hemodynamic measurements

Objective: Advanced hemodynamic monitoring systems have provided less invasive methods for estimating pressure-derived measurements such as pressure-derived cardiac output (CO) measurements. These devices apply algorithms to arterial pressure waveforms recorded via pressure recording components that transmit the pressure signal to a pressure monitor. While standards have been developed for pressure monitoring equipment, it’s unclear how the equipment-induced error can affect secondary measurements from pressure waveforms. We propose an approach for modelling different components of a pressure monitoring system and use this model-based approach to investigate the effect of different pressure recording configurations on pressure-derived hemodynamic measurements. Approach: The proposed model-based approach is a three step process. 1) modelling the response of pressure recording components using bench tests; 2) verifying the identified models through nonparametric equivalence tests; and 3) assessing the effects of pressure recording components on pressure-derived measurements. To delineate the application of this approach, we performed a series of model-based analyses to quantify the combined effect of a wide range of tubing configurations with various damping ratios and natural frequencies and monitors with different bandwidths on pressure waveforms and CO measurements by six pulse contour algorithms. Results: Model-based results show the error in pressure-derived CO measurements because of tubing configurations with different natural frequencies and damping ratios. Tubing configurations with low natural frequencies (<23 Hz) altered characteristics of pressure waveforms in a way that affected the CO measurement, some by as much as 20%. Significance: Our method can serve as a tool to quantify the performance of pressure recording systems with different dynamic properties. This approach can be applied to investigate the effects of physiologic signal recording configurations on various pressure-derived hemodynamic measurements.

SBP Is Superior to MAP to Reflect Tissue Perfusion and Hemodynamic Abnormality Perioperatively

Many articles have reported that intraoperative low mean artery pressure (MAP) or low systolic blood pressure (SBP) impacts on organs’ function and patients’ outcomes perioperatively. On the contrary, what type of blood pressure should be obtained still needs to be clarified. In our paper, we compared the influencing factors of MAP and SBP, and mathematical formula, arterial pulse contour calculation, and cardiovascular physiological knowledge were adopted to discuss how blood pressure can effectively reflect tissue perfusion and hemodynamic abnormality perioperatively. We concluded that MAP can reflect cardiac output change sensitively and SBP can reflect stroke volume change sensitively. Moreover, SBP can reflect the early hemodynamic changes, organs’ perfusion, and heart systolic function. Compared with MAP, perioperative monitoring of SBP and timely detection and treatment of abnormal SBP are very important for the early detection of hemodynamic abnormalities.

Bioreactance reliably detects preload responsiveness by the end-expiratory occlusion test when averaging and refresh times are shortened

Background The end-expiratory occlusion (EEXPO) test detects preload responsiveness, but it is 15 s long and induces small changes in cardiac index (CI). It is doubtful whether the Starling bioreactance device, which averages CI over 24 s and refreshes the displayed value every 4 s (Starling-24.4), can detect the EEXPO-induced changes in CI (ΔCI). Our primary goal was to test whether this Starling device version detects preload responsiveness through EEXPO. We also tested whether shortening the averaging and refresh times to 8 s and one second, respectively, (Starling-8.1) improves the accuracy of the device in detecting preload responsiveness using EEXPO. Methods In 42 mechanically ventilated patients, during a 15-s EEXPO, we measured ∆CI through calibrated pulse contour analysis (CIpulse, PiCCO2 device) and using the Starling device. For the latter, we considered both CIStarling-24.4 from the commercial version and CIStarling-8.1 derived from the raw data. For relative ∆CIStarling-24.4 and ∆CIStarling-8.1 during EEXPO, we calculated the area under the receiver operating characteristic curve (AUROC) to detect preload responsiveness, defined as an increase in CIpulse ≥ 10% during passive leg raising (PLR). For both methods, the correlation coefficient vs. ∆CIpulse was calculated. Results Twenty-six patients were preload responders and sixteen non preload-responders. The AUROC for ∆CIStarling-24.4 was significantly lower compared to ∆CIStarling-8.1 (0.680 ± 0.086 vs. 0.899 ± 0.049, respectively; p = 0.027). A significant correlation was observed between ∆CIStarling-8.1 and ∆CIpulse (r = 0.42; p = 0.009), but not between ∆CIStarling-24.4 and ∆CIpulse. During PLR, both ∆CIStarling-24.4 and ∆CIStarling-8.1 reliably detected preload responsiveness. Conclusions Shortening the averaging and refresh times of the bioreactance signal to 8 s and one second, respectively, increases the reliability of the Starling device in detection of EEXPO-induced ∆CI. Trial registration: No. IDRCB:2018-A02825-50. Registered 13 December 2018.

Comparison of accuracy of two uncalibrated pulse contour cardiac output monitors in off-pump coronary artery bypass surgery patients using pulmonary artery catheter-thermodilution as a reference

Background Cardiac output (CO) is a key measure of adequacy of organ and tissue perfusion, especially in critically ill or complex surgical patients. CO monitoring technology continues to evolve. Recently developed CO monitors rely on unique algorithms based on pulse contour analysis of an arterial blood pressure (ABP) waveform. The objective of this investigation was to compare the accuracy of two monitors using different methods of pulse contour analysis – the Retia Argos device and the Edwards Vigileo-FloTrac device – with pulmonary artery catheter (PAC)-thermodilution as a reference. Methods Fifty-eight patients undergoing off-pump coronary artery bypass surgery formed the study cohort. A total of 572 triplets of CO measurements from each device – Argos, Vigileo-FloTrac (third generation), and thermodilution – were available before and after interventions (e.g., vasopressors, fluids, and inotropes). Bland–Altman analysis accounting for repeated measurements per subject and concordance analysis were applied to assess the accuracy of the CO values and intervention-induced CO changes of each pulse contour device against thermodilution. Cluster bootstrapping was employed to statistically compare the root-mean-squared-errors (RMSE = √(μ2 + σ2), where μ and σ are the Bland–Altman bias and precision errors) and concordance rates of the two devices. Results The RMSE (mean (95% confidence intervals)) for CO values was 1.16 (1.00–1.32) L/min for the Argos device and 1.54 (1.33–1.77) L/min for the Vigileo-FloTrac device; the concordance rate for intervention-induced CO changes was 87 (82–92)% for the Argos device and 72 (65–78)% for the Vigileo-FloTrac device; and the RMSE for the CO changes was 17 (15–19)% for the Argos device and 21 (19–23)% for the Vigileo-FloTrac device (p < 0.0167 for all comparisons). Conclusions In comparison with CO measured by the PAC, the Argos device proved to be more accurate than the Vigileo-FloTrac device in CO trending and absolute CO measurement in patients undergoing off-pump coronary artery bypass surgery.

Stroke volume variation is a good predictor of fluid responsiveness in cardiac surgery patients with implanted intra-arortic balloon pump

There is a vast body of evidence in favor of individualising fluid therapy using dynamic hemodynamic indices like stroke volume variation (SVV). Patients with implanted intra-aortic balloon pump (IABP) are excluded from this approach because of pulse contour artifacts caused by the pump. The aim of this work is to test whether SVV can be used for fluid responsiveness prediction in these patients. Materials and methods: Patients after cardiac surgery with implanted IABP were included in this study. SVV was measured after placing the IABP on standy mode for one minute. Cardiac output (CO) measurement was obtained via Swan-Ganz catheter before and after a 6 ml/kg fluid challenge. Fluid responsiveness was defined as increase of CO by at least 10%. Results: SVV above 8.5% showed a good correlation with fuid responsiveness. Sensitivity was 95 (95% CI 85 to 100) and specificity 82 (95% CI 72 to 92). SVV had an area under the ROC curve 0.91 (95% CI 0.81 to 1.0) Conclusion: SVV is a good predictor of fluid responsiveness in patients with IABP. SVV should not be excluded as a fluid therapy guide for these patients. Placing the pump on standby for one minute allows obtaining an accurate measurement of this important variable.

Bioreactance and fourth-generation pulse contour methods in monitoring cardiac index during off-pump coronary artery bypass surgery

The pulmonary artery catheter (PAC) is considered the gold standard for cardiac index monitoring. Recently new and less invasive methods to assess cardiac performance have been developed. The aim of our study was to assess the reliability of a non-invasive monitor utilizing bioreactance (Starling SV) and a non-calibrated mini-invasive pulse contour device (FloTrac/EV1000, fourth-generation software) compared to bolus thermodilution technique with PAC (TDCO) during off-pump coronary artery bypass surgery (OPCAB). In this prospective study, 579 simultaneous intra- and postoperative cardiac index measurements obtained with Starling SV, FloTrac/EV1000 and TDCO were compared in 20 patients undergoing OPCAB. The agreement of data was investigated by Bland–Altman plots, while trending ability was assessed by four-quadrant plots with error grids. In comparison with TDCO, Starling SV was associated with a bias of 0.13 L min−1 m−2 (95% confidence interval, 95% CI, 0.07 to 0.18), wide limits of agreement (LOA, − 1.23 to 1.51 L min−1 m−2), a percentage error (PE) of 60.7%, and poor trending ability. In comparison with TDCO, FloTrac was associated with a bias of 0.01 L min−1 m−2 (95% CI − 0.05 to 0.06), wide LOA (− 1.27 to 1.29 L min−1 m−2), a PE of 56.8% and poor trending ability. Both Starling SV and fourth-generation FloTrac showed acceptable mean bias but imprecision due to wide LOA and high PE, and poor trending ability. These findings indicate limited reliability in monitoring cardiac index in patients undergoing OPCAB.