1. 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

2. 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

3. 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

4. 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

5. 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:

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

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

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

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

10. 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):
Slide modified by Yoshifumi Kotake, MD, PhD, Toho University, Tokyo, Japan. Used with permission. 10

11. 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

12. 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)

13. 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:

14. 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

15. Understanding SVV

16. 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)

17. 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

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

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

20. 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

21. 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

22. 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:

23. The FloTrac System
FloTrac Sensor
Vigileo Monitor
FloTrac

24. 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

25. 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

26. 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

27. 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

28. International Panel Point of View Article

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

30. or ephedrine

32. Publication: Clinically Proven Outcomes in High Risk Surgery

33. 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***

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