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Related Experiment Video

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Development of a Cohort Analytics Tool for Monitoring Progression Patterns in Cardiovascular Diseases: Advanced

Arindam Brahma1, Samir Chatterjee2, Kala Seal1

  • 1Department of Information Systems and Business Analytics, College of Business, Loyola Marymount University, Los Angeles, CA, United States.

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|September 24, 2024
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Summary

This study models cardiovascular disease (CVD) progression using patient data, revealing transition patterns and creating an analytics tool for clinical decision support. The model can predict chronic disease progression with minimal data.

Keywords:
Markovcardiologycardiovascularcardiovascular diseasecontinuous-time Markov chain modeldecision supportdisease monitoringdisease progression modeleHealthhealthcare analyticsheartmonitoringmyocardialprogressionstochasticstochastic modelstroke

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Area of Science:

  • Cardiovascular disease research
  • Computational epidemiology
  • Health informatics

Background:

  • Cardiovascular diseases (CVDs) are the leading global cause of death, characterized by complex chronic progression.
  • Physicians often use a
  • watchful waiting
  • strategy for chronic conditions, delaying interventions.
  • Longitudinal patient data and stochastic modeling can reveal population-level disease progression patterns.

Purpose of the Study:

  • To apply advanced stochastic modeling to longitudinal CVD patient data to uncover transition probabilities and progression patterns.
  • To develop a computational model and a digital clinical cohort analytics artifact for demonstrating the model's actionability.
  • To provide clinicians with data-driven insights for treatment and intervention strategies.

Main Methods:

  • Utilized longitudinal data from 9 National Heart Lung and Blood Institute epidemiological cohort studies, comprising 1274 patients and 4839 CVD episodes over 16 years.
  • Employed the continuous-time Markov chain method to model time-variant transitions between chronic disease states.
  • Developed a computational artifact for cohort analytics and validated its clinical applications through expert interviews.

Main Results:

  • Presented time-variant transition probabilities and patterns of CVD progression, identifying myocardial infarction (MI) to stroke as the fastest transition and MI to angina as the slowest.
  • Congestive heart failure (44.2%) and stroke (25.7%) were identified as the most probable first CVD episodes.
  • The developed artifact serves as an actionable eHealth tool, offering 9 identified use cases for clinical decision support.

Conclusions:

  • Addressed the limitation of lacking actionable cohort-level decision support tools for CVD by developing a comprehensive 10-state Markov chain model.
  • The stochastic model-embedded artifact facilitates efficient disease monitoring and intervention decisions using real-world patient data.
  • The model's flexibility allows for the analysis of any chronic disease progression with basic patient episode data.