Applied physiology for the clinician: Fluid responsiveness prediction with PPV and SVV in the perioperative setting

Applied physiology for the clinician: Fluid responsiveness prediction with PPV and SVV in the perioperative setting

  • issue 77

Simon Tilma Vistisen, Thomas W L Scheeren
vistisen@clin.au.dk

A decade ago, most of us were probably encouraged by the impressive ability of pulse pressure variation (PPV) and stroke volume variation (SVV) to predict fluid responsiveness.1 The holy grail ofvolume management monitoring had finally been found! Unfortunately, many limitations have been identified since, so that PPV and SVV are not broadly applicable in the ICU but still have a role to play in the peri-operative setting when used and interpreted in the right way, i.e., when cyclic respiratory changes in preload induced by mechanical ventilation adequately and interpretably induce PPV or SVV (see Fig. 1 depicting PPV and SVV of a fluid responder and a fluid non-responder).

 

Fig. 1: Frank-Starling curve. Horizontal bi-directional arrows depict respiratory preload variations. Vertical arrows depict SVV or PPV for a fluid responder (left portion) and non-responder (right portion).

A few years ago, the fathers of PPV published an overview of the PPV LIMITS,2an acronym identifying circumstances where the respiratory-induced fluctuations in preload may not be ‘adequately’ induced or even interpretable for various reasons. For the last half of this acronym, ITS, we currently cannot interpret PPV and trust its ability to predict fluid responsiveness:

I: Irregular heart rhythm such as atrial fibrillation or frequent ectopic beats – the measured PPV is not interpretable because it originates from preload variations caused by chaotic heart rate rather than cyclic respiratory preload variations (unlike Fig. 1).

T: When the Thorax is open, the respiratory changes in pleural pressure causing PPV (see Fig. 1) are nearly abolished.

S: During Spontaneous breathing, respiratory physiology is very different from the physiology during controlled mechanical ventilation depicted in Fig. 1, hampering PPV interpretability (obviously, a limitation related mostly to the ICU and regional but not general anaesthesia).

However, for the LIM-part of the acronym, we may have a chance to interpret PPV:

L: Low heart-rate-to-respiratory-rate ratio (HR/RR): When HR becomes lower than approx. 3.6 times the RR, the two overall mechanisms causing left ventricular preload variations – and in turn PPV – namely the respiratory effects on eachside of the heart, start counteracting each other and PPV diminishes, which is excellently explained by De Backer et al.3 An HR/RR of 3.6 is, for example, equivalent to a RR of 18/min in combination with an HR of 65, so low HR/RR can happen during surgery, more often when patients are on beta-blockers. If HR/RR goes slightly below 3.6, it may be possible, at least temporarily, to decrease RR slightly for a few minutes to overcome this limitation and be able to interpret PPV.

I: Intra-abdominal hypertension is commonly encountered for the peri-operative setting when inducing pneumoperitoneum for laparoscopic surgery. PPV may be less reliable in this case due to the altered hemodynamic conditions. Most experimental studies do not reflect this condition (rather abdominal compartment syndrome), but a few clinical studies performed during laparoscopic surgery suggest that SVV still predicts fluid responsiveness with acceptable accuracy and that the optimal thresholds may be higher, i.e., in the range of 12-14%.4-6

M: Mechanical ventilation with low tidal volume (Vt): De Backer et al. highlighted this limitation in 2005 in their landmark paper.7Unfortunately, this study has some problems with the data presentation for, particularly, its high-Vt group:

  1. a) There are only 10 non-responders ‘present’ in the classification curve for the high-Vt group, whereas there should (reportedly) have been 12. This is evident as each horizontal ‘specificity step’ on the classification curve is 10 percentage-points.
    b) The reported sensitivity and specificity values for the high-Vt group (88% and 89%, respectively) are statistically impossible in a group with (reportedly) 15 responders and 12 non-responders – also not possible if there were 10 non-responders.
    c) The study’s scatterplot reports a negative PPV value, which is mathematically impossible according to the PPV definition (max value minus min value).

Whether any of these problems impair the study conclusions is difficult to say, but since this landmark study,7we have definitely seen studies where PPV does not predict fluid responsiveness well in low-Vt cohorts,8 but also studies showing excellent prediction despite low Vt.9Also, we know that, at least with healthy lungs, PPV is nearly proportional to the applied Vt (responsible for preload fluctuations, see Fig. 1), so PPV at Vt of 6 ml/kg may predict PPV at Vt of 8 ml/kg.10
Therefore, the Vt limitation should perhaps not be interpreted as black/white (valid/invalid above/below a certain Vt), at least not in the peri-operative setting, i.e., in patients with ‘healthy’ lungs. Some anaesthesiologists target a Vt of around 6 ml/kg, whereas others target it around 8 ml/kg. In the first case, it may be possible, at least temporarily, to increase Vt to 8 ml/kg for, e.g., one minute, to enable interpreting PPV at 8 ml/kg and its change from 6 ml/kg to 8 ml/kg8(perhaps in combination with reducing RR, if the HR/RR is close to 3.6).

In summary, the LIMITS acronym coined by Michard et al.2 comprises in some cases limitations to PPV that we simply cannot ‘overcome’, but in other cases, it may be possible to slightly adjust ventilator settings to get valid fluid responsiveness prediction with PPV and/or SVV in the peri-operative setting. For laparoscopic surgery, there are compelling results reported for SVV, but further studies are needed to confirm SVV validity and its optimal threshold in this setting.

References

  1. Marik PE, Cavallazzi R, Vasu T,et al. Crit Care Med2009;37:2642-7.
  2. Michard F, Chemla D, Teboul JL. Crit Care2015;19:144-015-0869-x.
  3. De Backer D, Taccone FS, Holsten R, et al. Anesthesiology2009;110:1092-7.
  4. Hoiseth LO, Hoff IE, Myre K, et al. Acta Anaesthesiol Scand2012;56:777-86.
  5. Guinot PG, de Broca B, Bernard E, et al. Br J Anaesth2014;112:660-4.
  6. Zlicar M, Novak-Jankovic V, Blagus R, et al. J Clin Monit Comput2018;32:825-32.
  7. De Backer D, Heenen S, Piagnerelli M, et al. Intensive Care Med2005;31:517-23.
  8. Myatra SN, Prabu NR, Divatia JV,et al. Crit Care Med2017;45:415-21.
  9. Freitas FG, Bafi AT, Nascente AP, et al. Br J Anaesth2013;110:402-8.
  10. Liu Y, Lou JS, Mi WD,et al. BMC Anesthesiol2016;16:75-016-0233-y.