Critical closing pressure during experimental intracranial hypertension: comparison of three calculation methods

Katarzyna Kaczmarska1,2, Agnieszka Uryga3, Michał M Placek3,4

  • 1Department of Neurosurgery, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland.

Neurological Research
|March 14, 2020
PubMed

Insights

The pulsatile flow forward (PFF) model is recommended for estimating critical closing pressure (CrCP) more accurately than Aaslid's formula. Both impedance methods showed limitations compared to PFF, especially at lower blood pressures.

Area of Science:

  • Neuroscience
  • Physiology
  • Biomedical Engineering

Background:

  • Critical closing pressure (CrCP) is vital for assessing cerebrovascular tone.
  • Accurate CrCP estimation methods are lacking, hindering clinical application.
  • Existing methods include Aaslid's formula and cerebrovascular impedance techniques.

Purpose of the Study:

  • Compare three CrCP estimation methods: Aaslid's formula, continuous flow forward (CFF), and pulsatile flow forward (PFF).
  • Evaluate method accuracy under varying physiological conditions (intracranial hypertension, hypotension, hypocapnia, hypercapnia).

Main Methods:

  • Retrospective experimental study in New Zealand white rabbits.
  • Physiological maneuvers included induced intracranial hypertension, sympathetic blockade, and respiratory modulation.
  • CrCP estimation using Aaslid's formula, CFF, and PFF models.

Main Results:

  • All CrCP estimates increased with intracranial hypertension and hypocapnia.
  • Hypercapnia decreased CrCP estimates, with Aaslid's formula yielding non-physiological values in 16% of cases.
  • Bland-Altman analysis showed agreement between methods deteriorated at lower estimated CrCP values.

Conclusions:

  • Aaslid's formula has limited clinical utility for CrCP calculation.
  • The pulsatile flow forward (PFF) model provides a more accurate hemodynamic description and is recommended for CrCP estimation.
  • Cerebrovascular impedance methods, particularly PFF, offer improved accuracy over conventional formulas.

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