Using Markov-Switching Vector Autoregressions for modelling intraoperative hemodynamics

Using Markov-Switching Vector Autoregressions for modelling intraoperative hemodynamics

van Beek, Rutger (TU Delft Electrical Engineering, Mathematics and Computer Science)

Parolya, N. (mentor)
van Lier, Felix (graduation committee)
Hoeks, Sanne (graduation committee)
van Gijzen, M.B. (graduation committee)

Delft University of Technology

Applied Mathematics

2022-07-05

Cardiac complications after surgery are common irrespective of the underlying condition. The postoperative level of troponin T is a good marker for cardiac complications. Little is known on the pathology of the release of troponin T in the blood, while a better understanding might provide the ability to reduce the complications. The goal of the thesis is to find patterns in intraoperative data that are related to the release of troponin T in the blood during surgery. The states resulting from estimating an MSVAR on intraoperative hemodynamic data were interpreted and related to postoperative troponin T measurements. The MSVAR was estimated in two ways: with the EM algorithm and in Bayesian fashion with the Gibbs sampler. Prior distributions were chosen and a Gibbs sampler was developed for estimating the MSVAR with these priors. The differences between the EM algorithm and the Gibbs sampler are mostly fundamental and not practical. Furthermore, the MSVAR is an appropriate model for modelling intraoperative hemodynamic data. The states of the MSVAR were related to various surgery variables, but did not have any prognostic value for predicting postoperative troponin T. The states related to the external shocks continuously given to the patient during surgery rather than the patient's state.

MSVAR
hemodynamics
troponin
Bayesian
Erasmus MC
anesthesia monitoring

http://resolver.tudelft.nl/uuid:16749cbc-a356-4078-be6a-00b3ddd5d3c4

Student theses

master thesis

© 2022 Rutger van Beek