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White blood cell population dynamics for risk stratification of acute coronary syndrome

  1. John M. Higginsa,b,c,1
  1. aCenter for Systems Biology, Massachusetts General Hospital, Boston, MA 02114;
  2. bDepartment of Pathology, Massachusetts General Hospital, Boston, MA 02114;
  3. cDepartment of Systems Biology, Harvard Medical School, Boston, MA 02115
  1. Edited by Shu Chien, University of California, San Diego, La Jolla, CA, and approved September 28, 2017 (received for review June 3, 2017)

Significance

Medical doctors use blood counts to help diagnose and monitor almost all diseases. In the process of counting blood cells, most hematology analyzers actually measure features of thousands of individual blood cells, but this single-cell information is rarely utilized, and only the derived total cell counts are used to guide clinical care. This single-cell information helps further characterize each patient’s inflammatory or immunologic state. Using a mathematical model, we show how this routinely available single-cell information can help distinguish healthy and sick patients in general and those with acute coronary syndrome in particular. More broadly, our study shows how mathematical modeling of existing routine clinical laboratory data can help realize the vision of precision medicine today.

Abstract

The complete blood count (CBC) provides a high-level assessment of a patient’s immunologic state and guides the diagnosis and treatment of almost all diseases. Hematology analyzers evaluate CBCs by making high-dimensional single-cell measurements of size and cytoplasmic and nuclear morphology in high throughput, but only the final cell counts are commonly used for clinical decisions. Here, we utilize the underlying single-cell measurements from conventional clinical instruments to develop a mathematical model guided by cellular mechanisms that quantifies the population dynamics of neutrophil, lymphocyte, and monocyte characteristics. The dynamic model tracks the evolution of the morphology of WBC subpopulations as a patient transitions from a healthy to a diseased state. We show how healthy individuals and hospitalized patients with similar WBC counts can be robustly classified based on their WBC population dynamics. We combine the model with supervised learning techniques to risk-stratify patients under evaluation for acute coronary syndrome. In particular, the model can identify more than 70% of patients in our study population with initially negative screening tests who will be diagnosed with acute coronary syndrome in the subsequent 48 hours. More generally, our study shows how mechanistic modeling of existing clinical data can help realize the vision of precision medicine.

Footnotes

  • ?1To whom correspondence should be addressed. Email: john_higgins{at}hms.harvard.edu.
  • Author contributions: A.C. and J.M.H. designed research; A.C. performed research; A.C., L.N., and J.M.H. analyzed data; and A.C., L.N., and J.M.H. wrote the paper.

  • Conflict of interest statement: A.C. and J.M.H. are listed as inventors on a patent application filed by their institution.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.danielhellerman.com/lookup/suppl/doi:10.1073/pnas.1709228114/-/DCSupplemental.

Published under the PNAS license.

Online Impact

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