Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Dynamic Modeling of Coagulation Advances the Bloody Fight to Improve Patient Outcomes

Pressly, Michelle (2020) Dynamic Modeling of Coagulation Advances the Bloody Fight to Improve Patient Outcomes. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

[img] PDF (Updated Draft)
Draft Version
Restricted to University of Pittsburgh users only until 28 September 2021.

Download (38MB) | Request a Copy

Abstract

Treating hospitalized patients with coagulation problems can prove complex. In trauma or obstetrics, complex coagulation events present a unique challenge to clinicians. These challenges are amplified by interpatient variability, dynamic changes, transfusion decisions, and source of the event. When the coagulation process is abnormal (insufficient or excessive), the risk of hemorrhage or venous thromboembolism increases. Dynamic modeling has the potential as a tool for identifying a patient's coagulation state. Thereby, dynamic modeling provides a method for the improvement in clinical response. Multiple published models capture the coagulation cascade in varying levels of complexity. However, one significant void demonstrated in the literature is the lack of direct application of a coagulation model to clinically available data to improve medical protocols.

To address this void, simplified coagulation models are proposed to capture blood coagulation data from the Thromboelastogram, or TEG, to subtype coagulopathy. The combination of dynamic and statistical models with the initial minutes of data provides actionable data to clinicians much sooner than the TEG, which usually takes over an hour. Providing outputs to clinicians sooner in trauma and obstetrics settings can be the difference between a good and a bad outcome. Focusing on the obstetric population, other efforts of this work aim to determine the physiological dynamics that could drive venous thromboembolism with a mechanistic model assessing coagulation challenges faced during and after birth. In parallel, to broadly understand risk factors that could lead to adverse coagulation events, statistical models of a large obstetric database are constructed in order to analyze both insufficient and excessive clotting challenges faced by obstetric patients. Developed models and methods have expedited understanding and treatment decisions around patient coagulation.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Pressly, Michellemichellepressly@gmail.commap3120000-0003-3667-9003
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairParker, Robert S.rparker@pitt.edurparker
Committee CoChairClermont, Gillescler@pitt.educler
Committee MemberNeal, Matthew D.nealm@pitt.edunealm
Committee MemberWilmer, Christopher E.wilmer@pitt.eduwilmer
Date: 28 September 2020
Date Type: Publication
Defense Date: 14 July 2020
Approval Date: 28 September 2020
Submission Date: 27 July 2020
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 188
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Chemical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Blood, Dynamic Modeling, Statistics, Trauma, Obstetrics
Date Deposited: 28 Sep 2020 18:51
Last Modified: 28 Sep 2020 18:51
URI: http://d-scholarship.pitt.edu/id/eprint/39467

Metrics

Monthly Views for the past 3 years

Plum Analytics


Actions (login required)

View Item View Item