Data-Augmented Modeling of Intracranial Pressure
Precise management of patients with cerebral diseases often requires intracranial pressure (ICP) monitoring, which is highly invasive and requires a specialized ICU setting. The ability to noninvasively estimate ICP is highly compelling as an alternative to, or screening for, invasive ICP measurement. Most existing approaches for noninvasive ICP estimation aim to build a regression function that maps noninvasive measurements to an ICP estimate using statistical learning techniques. These data-based approaches have met limited success, likely because the amount of training data needed is onerous for this complex applications. In this work, we discuss an alternative strategy that aims to better utilize noninvasive measurement data by leveraging mechanistic understanding of physiology. Specifically, we developed a Bayesian framework that combines a multiscale model of intracranial physiology with noninvasive measurements of cerebral blood flow using transcranial Doppler. Virtual experiments with synthetic data are conducted to verify and analyze the proposed framework. A preliminary clinical application study on two patients is also performed in which we demonstrate the ability of this method to improve ICP prediction.
KeywordsCerebrovascular dynamics Data assimilation Patient-specific modeling Transcranial Doppler
JXW would like to thank J. Pyne and J. Wu for helpful discussions. The authors also thank the anonymous reviewers for their comments and suggestions, which helped improve the quality and clarity of the manuscript.
- 1.Andrews, P. J., G. Citerio, L. Longhi, K. Polderman, J. Sahuquillo, P. Vajkoczy, N.-I. Care, E. M. N. S. of the European Society of Intensive Care Medicine et al. NICEM consensus on neurological monitoring in acute neurological disease. Intensive Care Med. 34:1362–1370, 2008.Google Scholar
- 4.Asiedu, D. P., K.-J. Lee, G. Mills, and E. E. Kaufmann. A review of non-invasive methods of monitoring intracranial pressure. J. Neurol. Res. 4:1–6, 2014.Google Scholar
- 5.Bertoglio, C., P. Moireau, and J.-F. Gerbeau. Sequential parameter estimation for fluid–structure problems: Application to hemodynamics. Int. J. Numer. Methods Biomed. Eng. 28:434–455, 2012.Google Scholar
- 12.Hickman, K., B. Mayer, and M. Muwaswes. Intracranial pressure monitoring: review of risk factors associated with infection. Heart & Lung: The J. Criti. Care 19:84–90, 1990.Google Scholar
- 20.Iman, R. L. Latin hypercube sampling. Encyclopedia of quantitative risk analysis and assessment , 2008.Google Scholar
- 21.Itu, L., P. Sharma, C. Suciu, F. Moldoveanu, and D. Comaniciu. Personalized blood flow computations: A hierarchical parameter estimation framework for tuning boundary conditions. Int. J. Numer Methods Biomed. Eng. 33:e02803, 2017.Google Scholar
- 22.Kashif, F. M., G. C. Verghese, V. Novak, M. Czosnyka, and T. Heldt. Model-based noninvasive estimation of intracranial pressure from cerebral blood flow velocity and arterial pressure. Sci. Transl. Med. 4:129ra44–129ra44, 2012.Google Scholar
- 30.Moré, J. J. The Levenberg-Marquardt algorithm: implementation and theory. In: Numerical Analysis, edited by G. A. Watson, Berlin: Springer, pp. 105–116, 1978.Google Scholar
- 36.Serban, R. and A. C. Hindmarsh. CVODES: the sensitivity-enabled ODE solver in SUNDIALS. In: ASME 2005 international design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers, pp. 257–269, 2005.Google Scholar
- 38.Stevens, S. A., W. D. Lakin, and P. L. Penar. Modeling steady-state intracranial pressures in supine, head-down tilt and microgravity conditions. Aviation Space Environ. Med. 76:329–338, 2005.Google Scholar
- 39.Tiago, J., T. Guerra, and A. Sequeira. A velocity tracking approach for the data assimilation problem in blood flow simulations. Int. J. Numer. Methods Biomed. Eng. 33:e2856, 2017.Google Scholar