A pseudo-Bayesian model-based approach for noninvasive intracranial pressure estimation

Abstract

A noninvasive intracranial pressure (ICP) estimation method is proposed that incorporates model-based estimation within a probabilistic framework. A first-order subject-specific model of the cerebral vasculature relates arterial blood pressure with cerebral blood flow velocity. The model is solved for a range of physiologically plausible ICP values, and the resulting residual errors are transformed into likelihoods for each candidate ICP. The likelihoods are combined with a imulti-modal prior distribution of the ICP to yield an a posteriori distribution whose mode is taken as the final ICP estimate. An extension to this method is proposed to harness the temporal evolution of past ICP estimates for reducing dependence on the multi-modal prior distribution. This approach combines ICP estimates computed with a uniform prior belief with predictions from a single-state model of cerebral autoregulatory dynamics. This method was tested on data from thirteen patients from Boston Children's Hospital and yielded an ICP estimation bias (mean error or accuracy) of 0.3 nrmmHg and a root-mean-squared error (or precision) of 5.2 minHg. These performance characteristics are well within the acceptable range for clinical decision making. The method proposed here therefore constitutes a significant step towards robust, continuous, patient-specific noninvasive ICP determination.