Perioperative Hemodynamic Monitoring: An Overview of Current Methods

Tidal Volume Challenge to Assess Volume Responsiveness with Dynamic Preload Indices During Non-Cardiac Surgery: A Prospective Study

Background/Objectives: The aim of this study is to assess whether changes in Pulse Pressure Variation (PPV) and Stroke Volume Variation (SVV) following a VtC can predict the response to fluid administration in patients undergoing surgery under general anesthesia with protective mechanical ventilation. Methods: A total of 40 patients undergoing general surgery or vascular surgery without clamping the aorta were enrolled. Protective mechanical ventilation was applied, and the radial artery was catheterized in all patients. The protocol began one hour after the induction of general anesthesia and the stabilization of hemodynamic parameters. The parameters PPV6 and SVV6 were recorded during ventilation with a Vt of 6 mL/kg Ideal Body Weight (IBW) (T1). Then, the Vt was increased to 8 mL/kg IBW for 3 min without changing other respiratory parameters. After the VtC, the parameters PPV8 and SVV8 (T2) were recorded. After the stabilization of hemodynamic parameters, volume expansion (VE) was administered with colloid fluid of 6 mL/kg IBW. Parameters before (T3) and 5 min after fluid challenge (T4) were recorded. The change in the Stroke Volume Index (SVI) before and after VE was used to indicate fluid responsiveness. Patients were classified as fluid responders (SVI ≥ 10%) or non-responders (SVI < 10%). Results: The parameter ΔPPV(6-8) demonstrated good predictive ability to predict fluid responsiveness, evidenced by an Area Under the Curve (AUC) of 0.86 [95% Confidence Interval (CI) 0.74 to 0.95, p < 0.0001]. The threshold of ΔPPV(6-8) exceeding 2% identified responders with a sensitivity of 83% (95% CI 0.45 to 1.0, p < 0.0001) and a specificity of 73% (95% CI 0.48 to 1.0, p < 0.0001). The parameter ΔSVV(6-8) also revealed good predictive ability, reflected by an AUC of 0.82 (95% CI 0.67 to 0.94, p < 0.0001). The criterion ΔSVV(6-8) greater than 2% pinpointed responders with a sensitivity of 83% (95% CI 0.71 to 1.0, p < 0.001) and a specificity of 77% (95% CI 0.44 to 1.0, p < 0.001). Conclusions: This study demonstrates that VtC possesses good predictive ability for fluid responsiveness in patients undergoing general surgery.

Using generalized additive models to decompose time series and waveforms, and dissect heart-lung interaction physiology

Common physiological time series and waveforms are composed of repeating cardiac and respiratory cycles. Often, the cardiac effect is the primary interest, but for, e.g., fluid responsiveness prediction, the respiratory effect on arterial blood pressure also convey important information. In either case, it is relevant to disentangle the two effects. Generalized additive models (GAMs) allow estimating the effect of predictors as nonlinear, smooth functions. These smooth functions can represent the cardiac and respiratory cycles' effects on a physiological signal. We demonstrate how GAMs allow a decomposition of physiological signals from mechanically ventilated subjects into separate effects of the cardiac and respiratory cycles. Two examples are presented. The first is a model of the respiratory variation in pulse pressure. The second demonstrates how a central venous pressure waveform can be decomposed into a cardiac effect, a respiratory effect and the interaction between the two cycles. Generalized additive models provide an intuitive and flexible approach to modelling the repeating, smooth, patterns common in medical monitoring data.

Anesthesia Management for the Patient with Chronic Decompensated Heart Failure and Low Cardiac Output Undergoing CABG with Advanced Cardiac Monitoring: A Case Report

Introduction

Heart failure (HF) is a complex clinical syndrome caused by a structural or functional heart disorder. One of the most important challenges for anesthesiologists is the management of anesthesia in patients with severe heart failure, which has been facilitated by advanced monitoring systems.

Case Presentation

The patient was a 42-year-old man with a history of hypertension (HTN) and HF with involvement of the three coronary arteries (3VD) with ejection fraction (EF) 15%. He was also a candidate for elective CABG. In addition to the insertion of arterial line in the left radial artery and the Swan-Ganz catheter in the pulmonary artery, the patient was also monitored by the Edwards Lifesciences Vigilance II for cardiac index (CI) and intravenous mixed blood oxygenation (ScvO₂). Hemodynamic changes during and after surgery, as well as during inotrope infusion, were controlled, and the amount of fluid therapy was calculated by gold direct therapy (GDT) method.

Conclusions

Using PA catheter with advanced monitoring and GDT-based fluid therapy guaranteed a safe anesthesia in this patient with severe heart failure and EF < 20%. Moreover, the postoperative complications and duration of ICU stays were significantly reduced.

Relationship of Effective Circulating Volume with Sublingual Red Blood Cell Velocity and Microvessel Pressure Difference: A Clinical Investigation and Computational Fluid Dynamics Modeling

The characteristics of physiologic hemodynamic coherence are not well-investigated. We examined the physiological relationship between circulating blood volume, sublingual microcirculatory perfusion, and tissue oxygenation in anesthetized individuals with steady-state physiology. We assessed the correlation of mean circulatory filling pressure analogue (Pmca) with sublingual microcirculatory perfusion and red blood cell (RBC) velocity using SDF+ imaging and a modified optical flow-based algorithm. We also reconstructed the 2D microvessels and applied computational fluid dynamics (CFD) to evaluate the correlation of Pmca and RBC velocity with the obtained pressure and velocity fields in microvessels from CFD (pressure difference, (Δp)). Twenty adults with a median age of 39.5 years (IQR 35.5−44.5) were included in the study. Sublingual velocity distributions were similar and followed a log-normal distribution. A constant Pmca value of 14 mmHg was observed in all individuals with sublingual RBC velocity 6−24 μm s−1, while a Pmca < 14 mmHg was observed in those with RBC velocity > 24 μm s−1. When Pmca ranged between 11 mmHg and 15 mmHg, Δp fluctuated between 0.02 Pa and 0.1 Pa. In conclusion, the intact regulatory mechanisms maintain a physiological coupling between systemic hemodynamics, sublingual microcirculatory perfusion, and tissue oxygenation when Pmca is 14 mmHg.