高風險手術患者麻醉中的血液動力學分析 Hemodynamic optimization for high risk surgical patients 三軍總醫院麻醉部 呂忠和醫師

Content Introduction Proven benefits of hemodynamic optimization How to assess volume responsiveness ? Understanding SVV and the passive leg-raising test Goal-directed intraoperative therapy Simulation Based Training: Hemodynamic optimization

Introduction Major surgery is associated with a significant and quantifiable rate of both morbidity and mortality This risk of adverse events is increased in groups of patients with certain clinical criteria, for instance, emergency surgery or surgery in a patient with limited cardiovascular or respiratory reserve Perioperative goal-directed therapy based on flow-related hemodynamic parameters improves patient outcome, particularly in high-risk patients

Proven benefits of hemodynamic optimization ↓ Pneumonia infection ↓ Abdominal infection ↓ Wound infection ↓ Respiratory support ↓ Arrhythmia ↓ Pulmonary Edema ↓ Hypotension ↓ Acute myocardial infarction ↓ Stroke ↓ Bowel obstruction ↓ Anastomotic leak ↓ Renal complications ↓ Postoperative massive hemorrhage

Effects of preemptive hemodynamic intervention on mortality rate 29 trials involved 4805 patients in moderate and high-risk surgical patients A systemic review and meta-analysis, Hamilton MA, et al. Anesth Analg 2011;112:1392-1402.

Effects of preemptive hemodynamic intervention on complication rate Hamilton MA, et al. Anesth Analg 2011;112:1392-1402.

Effects of perioperative goal-directed therapy on mortality rate Corcoran T, et al. Anesth Analg 2012;114:640-651.

Effects of perioperative goal-directed therapy on the risk of pneumonia and renal complications Cocoran T, et al. Anesth Analg 2012;114:640-651.

Cardiac Output Preload Afterload Contractivity Stroke Volume X Heart Rate Oxygen Content Cardiac Output Resistance X X Oxygen Delivery Arterial Blood Pressure

Wet, Dry or Something Else? Too low Circulatory disturbance Shock Pre-renal failure Too high Edema Respiratory insufficiency Acute Coronary Syndrome Curves A: Hypothesized line of risk B: Division between patient groups in a ‘wet vs dry’ study C: Division between patient and groups in an ‘optimized vs non-optimized’ study Source: Bellamy, MC. Br J Anaesth 2006; 97 (6): 755-7. Slide modified by Yoshifumi Kotake, MD, PhD, Toho University, Tokyo, Japan. Used with permission. 10

Frank-Starling Curve: Model for understanding fluid condition Control dependent upon vasoactive agents may cause tachycardia Increased cardiac function Normal cardiac function Volume overload may cause increased edema and prolonged respiratory insufficiency SV Decreased cardiac function Low SV may cause GI tract ischemia Appropriate fluid therapy improves oxygen supply-demand balance and prognosis Preload SV Slide created by Yoshifumi Kotake, MD, PhD, Toho University, Tokyo, Japan. Used with permission. 11

Static markers of cardiac preload None of the measures of cardiac preload enables us to accurately predict fluid responsiveness Central venous pressure (8-12 mmHg) Pulmonary artery occlusion pressure (12-15 mmHg) Global end-diastolic volume (by transpulmonary thermodilution) Right ventricular end-diastolic volume (by modified pulmonary artery catheter) Left ventricular end-diastolic diameter/volume (by echocardiography)

Pressures do not predict fluid responsiveness From a physiological point of view, SVV may be superior to PPV, because the SV changes induce changes in the arterial pressure waveform, and PPV is thought to be more susceptible to vascular influences than SVV Marik PE, et al. Crit Care Med 2009;37:2642-2647.

Functional (dynamic) prediction of fluid responsiveness The respiratory variation of hemodynamic signals Pulse pressure variation (PPV) Stroke volume variation (SVV) The passive leg-raising test The end-expiratory occlusion test Echocardiographic assessment

Understanding SVV

Heart-lung interaction Inspiration during mechanical positive pressure ventilation results in increased intrathoracic pressure and a reduced venous return  preload decreases  stroke volume as well as pulse pressure decrease. During expiration, stroke volume and pulse pressure increase in turn The stroke volume decrease is proportional to the degree of hypovolemia and is transmitted to the left heart after 2-3 beats (pulmonary transmit time)

Stroke Volume Variation Normal ventricular function Analyzes the cardiovascular preload response produced by changes in the intrathoracic pressure induced by mechanical ventilation Stroke Volume ∆VS ∆SV 19% Preload Vt 600 ml

Stroke Volume Variation Decreased cardiac function Stroke Volume ∆SV 6% ∆SV Preload Vt 600 ml

SVV to determine Preload Responsiveness <10% >15% International Panel Point of View Article; 2009

SVV: Stroke Volume Variation Highly sensitive and specific indicator of fluid responsiveness Dynamic: determine responsiveness before giving fluid But limited… Mechanical ventilation No arrhythmias / premature ventricular contractions

detect fluid responsiveness even during PVC’s FloTrac SVVxtra Detection SVVxtra: Able to detect fluid responsiveness even during PVC’s PVC Elimination Reliable waveform This is the simplified version of how the algorithm works. The new advanced algorithm works very differently and it is much more complex but this gives the general essence of how reliable SVV numbers are calculated when patients have PVC’s. The new algorithm has been advanced that it can detect more PVC’s per 20 seconds now. 21

The passive leg-raising test Equivalent to 150 – 300 ml volume Effects < 30 sec.. Not more than 4 minutes Self-volume challenge Completely reversible Clinical studies show that if a patient is responsive to PLR (change in SV), they will be responsive to fluid Monnet X, Teboul JL. Intensive Care Med 2008;34:659-663.

The FloTrac System FloTrac Sensor Vigileo Monitor FloTrac

Final Option for Fluid Optimization: Fluid Challenge Frank-Starling Curve Step 1: Give volume (e.g. 250 – 500 ml bolus) Step 2: Observe change in SV (e.g. after 20 minutes) Stroke Volume No change in stroke volume  Further volume will likely not improve output Change in stroke volume  Further volume may improve output Preload / Volume 24

FloTrac Provides Parameters to Measure Flow Frank-Starling Curve Increased cardiac function Normal cardiac function FloTrac system provides: Stroke Volume Stroke Volume Index Cardiac Output Cardiac Index Stroke Volume Decreased cardiac function Preload 25

FloTrac provides parameters to measure flow Frank-Starling Curve Increased cardiac function Normal cardiac function FloTrac system enables three ways of determining preload responsiveness: Stroke Volume Decreased cardiac function 1. SVV 2. PLR (Passive Leg Raising) 3. Fluid challenge Preload 26

FloTrac System: Fluid Optimization Frank-Starling Curve Increased cardiac function Normal cardiac function BOTH DIMENSIONS ARE NECESSARY TO OPTIMIZE FLUID STATUS Stroke Volume FloTrac system provides: 1. Indications of fluid responsiveness 2. Methods of verifying that fluid is beneficial to the patient’s status Decreased cardiac function Preload 27

International Panel Point of View Article

Using the Intervention Analysis Screen Clinically proven to improve patient outcomes International Panel Point of View Article; 2009

or ephedrine

Publication: Clinically Proven Outcomes in High Risk Surgery

Optimize anesthesia VT:8 ml/kg Ephedrine 5-10 mg Ephedrine 5-10 mg Ephedrine 5-10 mg Dobutamin 1mcg/kg/min Norepinephrine 0.01 mcg/kg/min Dobutamin 1mcg/kg/min***

Simulation Based Training Goal-directed hemodynamic therapy for high risk surgical patients