1. Predicting Fluid Responsiveness, an Update GHAZI ALDEHAYAT MD

2. Fluid Management and Optimization
The primary goal of hemodynamic therapy is the prevention of:
Inadequate tissue perfusion.
Inadequate oxygenation.

3. Fluid Management and Optimization
It is crucial that optimal volaemic status is achieved, thus preventing
The deleterious effects of inadequate tissue blood flow
The harmful effects of fluid overload.

4. Goal Directed Therapy
Multiple studies have demonstrated the favorable outcome achieved by goal-directed fluid management during the intraoperative period.
To aim for normal numbers of key hemodynamic parameters such as co, cI, O2 delivery ,svr

5. Goal Directed Therapy A recent meta-analysis.
Twenty nine studies involving patients.
Rational fluid management based on flow-directed haemodynamic goals significantly reduced perioperative mortality and morbidity.
Hamilton MA, Cecconi M, Rhodes A. A systematic review and
meta-analysis on the use of preemptive hemodynamic intervention
to improve postoperative outcomes in moderate and high risk surgical patients. Anesth Analg 2011; 112: 1392–402

6. Fluid Management and Optimization
Administration of excess fluid can cause several problems including:
Increase in demand in cardiac function as a result
of extreme shift of the right on the Starling
myocardial performance curve.
Fluid accumulation in the lungs predisposes to
pneumonia and respiratory failure.
Inhibition of gastric motility and poor wound
healing.

7. Fluid Management and Optimization
Advanced cardiovascular monitoring is a prerequisite to optimize hemodynamic treatment in critically ill patients prone to cardiocirculatory failure.
It is not enough to measure the parameter e.g. if the stroke volume or cardiac output, it is important to know what sort of intervention is more suitable

8. Preload Responsiveness
The expected haemodynamic response to volume expansion is an increase in stroke volume and therefore cardiac output.
Only a proportion of critically ill patients show a response to volume expansion by a significant increase in stroke volume because the subsequent increase in stroke volume also depends on ventricular function.
Which patient needs volume and which patient needs other interventions e.g inotropes

9. Frank Starling Curve
The Frank Sterling Curve or cardiac function curve is a graph illustrating the relationship between the preload (plotted along the x-axis) and the cardiac output or stroke volume (plotted along the y-axis) . Preload is defined physiologically as the stretch of cardiac muscle fibres before contraction. Right arterial pressure (RAP) or pulmonary capillary wedge pressure (PCWP) are often used as a surrogate measure. From the above mentioned, it should be clear that preload is better represented by mean systemic filling pressure (Pms) subtracted by (RAP):
Preload=Pms -RAP

10. Frank Starling Curve
From the Frank–Starling law of the heart, an increase in preload will significantly increase stroke volume only if both ventricles are on the ascending portion of the curve. If one or both ventricles lie on the flat portion, then the patient will be regarded as a non-responder; that is, cardiac output will not increase significantly in response to volume expansion.
Michard F, Teboul J-L. Using heart–lung interactions to assess fluid responsiveness during mechanical ventilation. Crit Care 2000; 4: 282–9

11. Preload Dependence
If the heart is working on the steep portion (low preload,) an increase in preload (induced by volume expansion) will induce a significant increase in stroke volume (here the heart is said to be preload dependent, and the patient is a responder to volume expansion).
If the heart is working on the steep portion (low preload), then an increase in preload (induced by volume expansion) will induce a significant increase in stroke volume (here the heart is said to be preload dependent, and the patient is a responder to
volume expansion

12. Preload Dependence
If the heart is working on the plateau (elevated preload), then an increase in preload (induced by volume expansion) will not induce any significant increase in stroke volume (here the heart is said to be preload independent and the patient is a non responder to volume expansion).

13. Preload Dependence Or “is my patient preload dependent or not?”
“will my patient’s cardiac output increase after volume expansion?” Or
“is my patient preload dependent or not?”

14. Preload Dependence
Therefore, it is of major importance for the anesthesiologist to be able to predict the effects of volume expansion before he/she performs volume expansion

15. Monitoring Reliable, Which monitor?
continuous, noninvasive, operator-independent and
cost-effective and should have a fast response time.

16. Preload Monitoring
Preload measurement, by whatever technique, is still commonly used to guide fluid therapy but can fail to estimate the response to fluids in one half of patients, thus rendering them exposed to the hazards of unnecessary fluid therapy.
Preload measurement, by whatever technique, is still commonly used to guide fluid therapy but can fail to estimate the response to fluids in one half of patients, thus rendering them exposed to the hazards of unnecessary fluid therapy

17. Static Parameters
Central venous pressure (CVP) has been traditionally used to guide fluid administration within the operating theatre, but neither CVP nor pulmonary artery occlusion pressure (PAOP) have been shown to be accurate markers of RV and LV end-diastolic volumes, respectively.

18. Central Venous Pressure
CO is, for an important part, determined by the venous return to the heart.
Venous return itself is determined by:
CVP.
resistance to venous return.
systemic vascular compliance.
venous capacitance.
stressed and unstressed blood volume.
and mean systemic filling pressure.
CVP is often used to guide fluid therapy in both adults and children and is considered to reflect cardiac preload. However, CO is, for an important part, determined by the venous return to the heart. Venous return itself is determined by CVP, resistance to venous return, systemic vascular compliance, venous capacitance, stressed and unstressed blood volume, and mean systemic filling pressure

19. Central Venous Pressure
Except for CVP, the other variables are difficult or even impossible to measure.
Furthermore, the value of CVP is influenced by the.
diastolic compliance of the right ventricle.
intra-abdominal pressure.
positive end expiratory pressure.
forced expiration.
Except for CVP, the other variables are difficult or even impossible to measure. Furthermore, the value of CVP is influenced by the diastolic compliance of the right ventricle, intra-abdominal pressure, positive end expiratory pressure, and forced expiration. Indeed, CVP does not predict fluid responsiveness and only poorly reflects preload in adults, children, and pediatric animal models.

20. Nolen-Walston RD, Norton JL, de Solis C, Underwood C, Boston R, Slack J, Dallap BL: The effects of hypohydration on central venous pressure and splenic volume in adult horses. J Vet Intern Med 2010.
Magdesian KG, Fielding CL, Rhodes DM, Ruby RE: Changes in central venous pressure and blood lactate concentration in response to acute blood loss in horses. J Am Vet Med Assoc 2006, 229:

21. Central Venous Pressure
This systematic review demonstrated a very poor relationship between CVP and blood volume as well as the inability of CVP/CVP to predict the hemodynamic response to a fluid challenge. CVP should not be used to make clinical decisions regarding fluid management. Marik PE, Baram M, Vahid B Does central venous pressure predict fluid responsiveness? A systematic review of the literature and the tale of seven mares. Chest. 2008;134(1):172–178

22. Static Parameters
Consequently, for a given preload value or central venous pressure, it is not possible to predict the effects of an increase in preload on stroke volume.
Thus, preload or its surrogates are not accurate predictors of
fluid responsiveness.
Michard F, Teboul JL: Predicting fluid responsiveness in ICU
patients: A critical analysis of the evidence. Chest 121: ,
2002
Muller L, Louart G, Bengler C, et al: The intrathoracic blood
volume index as an indicator of fluid responsiveness in critically ill
patients with acute circulatory failure: A comparison with central
venous pressure. Anesth Analg 107: , 2008
Osman D, Ridel C, Ray P, et al: Cardiac filling pressures are not
appropriate to predict hemodynamic response to volume challenge. Crit
Care Med 35:64-68, 2007

23. Static Parameters Issues: CVP and PAOP poor predictors of fluid status
Kumar CCM 2004

24. Static Parameters
Similarly, in patients receiving a fluid challenge, changes in CVP and PAOP do not reflect changes in ventricular end-diastolic volumes.
Pinsky M. Assessment of indices of preload and volume responsiveness.
Curr Opin Crit Care 2005; 11: 235–9

25. Static parameters,
Fluid challenge.
Passive leg raising.

26. Dynamic Response to Preload

27. Pulsus Paradoxus: The Origin of SVV
• Pulsus Paradoxus is the origin of SVV value.
- Occurs with spontaneously breathing patients.
• Reverse Pulsus Paradoxus
– Occurs during positive pressure ventilation.
Exaggerated in hypovolemic patients due to compression of empty cava

28. Cyclic Changes in LV Stroke Volume
Consequently, mechanically ventilated patients under
general anesthesia present with cyclic changes in left ventricular stroke volume. When the heart is working on the steep portion of the Frank-Starling relationship, these respiratory variations are important because slight changes in right ventricular preload induced by mechanical ventilation will induce significant changes in stroke volume (Frank-Starling) whereas when the heart is working on the plateau of this relationship, respiratory variations are small because changes in right ventricular preload induced by mechanical ventilation have no impact on stroke volume .After filling the compression of the svc and inferior vena cava decreases, the variations decreases.

29. In patients undergoing positive pressure ventilation, heart–lung interactions can be used to identify fluid responsiveness.
Most dynamic parameters can be measured using widely available devices for continuous beat-to-beat cardiac output monitoring

30. Cardiopulmonary Interactions
“The more sensitive a ventricle is to preload, the more the stroke volume will be impacted by changes in preload due to positive pressure ventilation.” AP AP
Preload Independent
Preload Dependent
ΔSV =
SVmax – SV min
(SVmax + SV min)/2

31. Dynamic Parameters
Dynamic indicators can be derived from
Single arterial pressure waveform (systolic pressure variations [SPVs], pulse-pressure variations [PPVs] and stroke volume variations [SVV].
Plethysmographic waveform (respiratory variations in the plethysmographic waveform amplitude [POP] and plethysmographic variability index [PVI]).
Desebbe O, Cannesson M: Using ventilation induced plethysmographic
variations to optimize patient fluid status. Curr Opin Anaesthesiol
21: , 2008
These dynamic indicators can be derived from a
single arterial pressure waveform (systolic pressure variations [SPVs] and pulse-pressure variations [PPVs]) or from the plethysmographic waveform (respiratory variations in the plethysmographic waveform amplitude [POP] and plethysmographic variability index [PVI]).
Desebbe O, Cannesson M: Using ventilation induced plethysmographic
variations to optimize patient fluid status. Curr Opin Anaesthesiol
21: , 2008

32. Dynamic parameters
The respiratory changes in aortic flow velocity and stroke volume can be assessed by Doppler echocardiography.
Distensibility index of the inferior vena cava
Collapsibility index of the superior vena cava

33. Dynamic Parameters
It is clear now that dynamic parameters of fluid responsiveness, based on cardiopulmonary interactions in patients under general anesthesia and mechanical ventilation, are superior to static indicators (such as central venous pressure).
Michard F: Changes in arterial pressure during mechanical ventilation.
Anesthesiology 103: , 2005
Marik PE, Cavallazzi R, Vasu T, et al: Dynamic changes in
arterial waveform derived variables and fluid responsiveness in mechanically
ventilated patients: A systematic review of the literature.
Crit Care Med 37: , 2009

34. Stroke Volume Variation

35. Stroke Volume Variation
Stroke Volume Variation (SVV) represents the variation of stroke volume (SV) over the ventilatory cycle.
SVmax
SVmin
SVmean
SVmax – SVmin
SVV =
SVmean
Stroke volume variation (SVV) is the change in stroke volume during the respiratory cycle, and is calculated by the formula SVV (%) ¼ (SVmax 2 SVmin)/SVmean. SVV can be assessed continuously by any beat-to-beat cardiac output monitor. Many studies have shown this to be a reliable predictor of fluid responsiveness.
Hofer C, Senn A, Weibel L. Assessment of SVV for predicting fluid
responsiveness using the modified FloTrac and PiCCOplus systems. CritCare 2008; 12: R82
SVV is...
... measured over last 30s window
… only applicable in controlled mechanically ventilated patients with regular heart rhythm

37. Stroke Volume Variation
J Anesth. 2011 Dec;25(6): Epub 2011 Sep 4.
Accuracy of stroke volume variation in predicting fluid responsiveness: a systematic review and meta-analysis.
Zhang Z, Lu B, Sheng X, Jin N.
Source
Department of Critical Care Medicine, Jinhua Central Hospital, 351# Mingyue Road, Jinhua, , Zhejiang, People's Republic of China.
Abstract
PURPOSE:
Stroke volume variation (SVV) appears to be a good predictor of fluid responsiveness in critically ill patients. However, a wide range of its predictive values has been reported in recent years. We therefore undertook a systematic review and meta-analysis of clinical trials that investigated the diagnostic value of SVV in predicting fluid responsiveness.

38. Plethysmographic Variability Index (PVI)

39. Stroke Volume Variation
METHODS:
Clinical investigations were identified from several sources, including MEDLINE, EMBASE, WANFANG, and CENTRAL. Original articles investigating the diagnostic value of SVV in predicting fluid responsiveness were considered to be eligible. Participants included critically ill patients in the intensive care unit (ICU) or operating room (OR) who require hemodynamic monitoring.
RESULTS:
A total of 568 patients from 23 studies were included in our final analysis. Baseline SVV was correlated to fluid responsiveness with a pooled correlation coefficient of Across all settings, we found a diagnostic odds ratio of 18.4 for SVV to predict fluid responsiveness at a sensitivity of 0.81 and specificity of The SVV was of diagnostic value for fluid responsiveness in OR or ICU patients monitored with the PiCCO or the FloTrac/Vigileo system, and in patients ventilated with tidal volume greater than 8 ml/kg.
CONCLUSIONS:
SVV is of diagnostic value in predicting fluid responsiveness in various settings.
J Anesth. 2011 Dec;25(6): Epub 2011 Sep 4.
Accuracy of stroke volume variation in predicting fluid responsiveness: a systematic review and meta-analysis.
Zhang Z, Lu B, Sheng X, Jin N.

40. PVI Calculation Automated measurement
Changes in plethysmographic waveform amplitude over the respiratory cycle
PVI is a percentage from 1 to 100%:
1 - no pleth variability
100 - maximum pleth variability

41. Higher PVI = More likely to respond to fluid administration
10 %
Lower PVI = Less likely to respond
to fluid administration
24 %
Higher PVI = More likely to respond to fluid administration
Preload
Maxime Cannesson, MD, PhD

42. Cannesson et al. Anesthesiology 2007

43. PVI

44. Pleth Variability Index to Monitor the Respiratory Variations in the Pulse Oximeter Plethysmographic Waveform Amplitude and Predict Fluid Responsiveness in the Operating Room Cannesson et al. BJA 2008

45. Ten studies in 233 patients were included in this meta-analysis
Ten studies in 233 patients were included in this meta-analysis. All patients were in normal sinus rhythm.
CONCLUSIONS:
Based on our meta-analysis, we conclude that ∆POP and PVI are equally effective for predicting fluid responsiveness in ventilated adult patients in sinus rhythm. Prediction is more accurate when a large fluid bolus is administered
Intensive Care Med. 2012 Sep;38(9): doi: /s Epub 2012 Jun 26.
Accuracy of plethysmographic indices as predictors of fluid responsiveness in mechanically ventilated adults: a systematic review and meta-analysis.
Sandroni C, Cavallaro F, Marano C, Falcone C, De Santis P, Antonelli M.

47. Mechanical Ventilation General Anesthesia Tidal volume > 8 mL/Kg
Major Limitations of Dynamic Parameters for Fluid Responsiveness Assessment
Mechanical Ventilation
General Anesthesia
Tidal volume > 8 mL/Kg
No arrhythmia
HR/RR < 3.6
SVV
PPV
SPV
ΔPOP
ΔPOP

48. SuConclusionmmaryCoionsnclus
There are many parameters to use.
Static measurements are not accurate.
We need less invasive and more dynamic parameters.
SVV is good parameters to use.

49. THANK YOU

51. Global End-diastolic Volume and Intrathoracic Blood Volume
Transpulmonary thermodilution using a commercially available device (PiCCO, Pulsion Medical Systems) can be used to assess the global end-diastolic volume (GEDV), the largest volume of blood contained within the four heart chambers, and intrathoracic blood volume (ITBV), which comprises GEDV and pulmonary blood volume. Both parameters are readily available using the PiCCO system and GEDV has been validated as an indicator of cardiac preload. GEDV may also be useful in predicting preload response, but there are insufficient data to support this.

52. TOE Static measurement of LV end-diastolic area
(LVEDA), measured by transoesophageal echo (TOE), correlates well with LVEDV, and as such has been examined as a parameter of LV preload Although LVEDA performs well in determining an endpoint for fluid administration, it is less useful in predicting
those patients who would benefit from volume expansion. LVEDA correlates better with stroke volume than PAOP does, but neither correlates strongly

53. LVEDA
Estimation of the LVEDA may not accurately represent LV end-diastolic volume, which in turn relates little to diastolic chamber compliance. LVEDA is limited by underlying cardiac conditions, which may cause dilatation or poor LV systolic function, and there is considerable overlap in baseline LVEDA values in patients who do respond to a fluid challenge and patients who do not.