464265 Mathematical Modeling of White Blood Cell Population Dynamics for Disease Prognosis

Wednesday, November 16, 2016: 1:24 PM
Continental 8 (Hilton San Francisco Union Square)
Anwesha Chaudhury1,2,3 and John Higgins1,2,3, (1)Center for Systems Biology, Massachusetts General Hospital, Boston, MA, (2)Pathology, Massachusetts General Hospital, Boston, MA, (3)Systems Biology, Harvard Medical School, Boston, MA

The complete blood count (CBC) is one of the most frequently ordered clinical tests for diagnosis and disease monitoring. CBCs are measured using automated hematology analyzers, usually based on principles of flow cytometry. Conventionally, the total number of each blood component (e.g., red blood cells, white blood cells) from the CBC informs diagnosis and monitoring. This approach, while broadly useful, discards a variety of additional data such as the distribution of different components that can be obtained from the analyzers and used for more accurate and earlier detection of diseases. The approach proposed in this project focuses on a case study consisting of patients with acute myocardial infarction (heart attack or AMI), the number one cause of deaths in the world. Early detection of AMI can result in appropriate therapeutic intervention, which can prevent fatal outcomes.

There is sufficient evidence in the literature highlighting the correlation of white blood cells (WBCs) and WBC subpopulations with AMI. This evidence motivates us to develop additional prognostic tools for examining the WBC dynamics in patients with AMI. In this framework, we aim to extract information from the raw flow cytometry measurements and develop a dynamic mathematical description of WBC subpopulation behavior. For this purpose we are using two cohorts of clinical datasets containing troponin, the gold standard measure of heart muscle cell death, and CBC measurements of patients suspected with AMI. More broadly, our objective is to use patient data to derive insight into the dynamics of human pathophysiology and to develop related clinical tools. This methodology is associated with no additional laboratory testing costs and has immense potential to advance clinical diagnosis, monitoring, and optimization of treatment.

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