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Hemodynamic Indices and Shape-Based Models of Left Atrial Appendage to Enhance Stroke Prediction in Atrial Fibrillation

Sanatkhani, Soroosh (2022) Hemodynamic Indices and Shape-Based Models of Left Atrial Appendage to Enhance Stroke Prediction in Atrial Fibrillation. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Atrial fibrillation (AF) is the most common arrhythmia that leads to thrombus formation, mostly in the left atrial appendage (LAA). The current standard of stratifying stroke risk, based on the CHA2DS2-VASc score, does not consider LAA morphology/hemodynamics. The aim of this study was to determine whether LAA morphology and hemodynamics-based indices can stratify stroke risk independent of CHA2DS2-VASc score, left atrium size, and AF type. In a retrospective matched case-control study, patient-specific measurements in 128 AF patients included left atrial (LA) and LAA 3D geometry obtained by cardiac computed tomography, heart rate, cardiac output, and hematocrit. We quantified patient-specific 3D LAA morphology in terms of a novel LAA appearance complexity index (LAA-ACI) and employed computational fluid dynamics (CFD) analysis to quantify LAA mean residence time, tm and asymptotic concentration, C∞ of blood-borne particles.

Effects of confounding variables were examined to optimize the CFD analysis. cardiac output, but not by the temporal pattern of pulmonary vein inlet flow, significantly affected LAA tm. Both the hematocrit level and the blood rheology model (Newtonian vs. non-Newtonian) also significantly affected LAA tm. Finally, 10,000 s was found to be a sufficient length of CFD simulation to calculate LAA tm in a consistent and reliable manner.

LAA tm varied significantly within a given LAA morphology as defined by the current subjective method, and it was not simply a reflection of LAA geometry/appearance. In addition, LAA-ACI and tm varied significantly for a given CHA2DS2-VASc score, indicating that these two indices of stasis are not simply a reflection of the subjects’ clinical status. Using multiple logistic regression, we observed that ACI, tm, and C∞ had a modest, but statistically insignificant performance in predicting stroke (area under the ROC curve = 0.56–0.61). The temporal dissociation between adverse changes in LAA shape and hemodynamics-based indices and the actual stroke event can contribute to the negative result; a longitudinal study is necessary to address this issue. In addition, it is possible that a multiscale model that combines CFD-based hemodynamics simulation and biology-based thrombus formation can yield indices that can better stratify stroke risk in AF patients.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Sanatkhani, Sorooshsorooshsanatkhani@gmail.comsos510000-0003-4745-0669
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee MemberBorovetz, Harveyborovetz@pitt.eduborovetz
Committee MemberFederspiel, Williamfederspielwj@pitt.edufederspielwj
Committee MemberJain, Sandeepjainsk@upmc.edu
Committee MemberMukkamala, Ramakrishnarmukkamala@pitt.edurmukkamala
Committee MemberSaba, Samirsabas@upmc.edu
Committee CoChairMenon, Prahladprm44@pitt.eduprm44
Committee ChairShroff, Sanjeevsshroff@pitt.edusshroff0000-0003-1868-3826
Date: 16 January 2022
Date Type: Publication
Defense Date: 18 August 2021
Approval Date: 16 January 2022
Submission Date: 17 August 2021
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 164
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Bioengineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: mean residence time, asymptotic concentration, appearance complexity index, computational fluid dynamics, confounding variables, principal component analysis
Date Deposited: 16 Jan 2022 15:04
Last Modified: 16 Jan 2022 15:04
URI: http://d-scholarship.pitt.edu/id/eprint/41689

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