Modeling Early Warning Systems: Construction and Validation of a Discrete Event Simulation Model for Heart Failure

Area of Science:

• Cardiology
• Health Economics
• Computational Biology

Background:

• Heart failure management requires effective strategies to improve patient outcomes and resource utilization.
• Individual patient characteristics significantly influence health outcomes in heart failure.
• Existing models may not fully capture the nuances of different management strategies and patient profiles.

Purpose of the Study:

• To develop and validate a discrete event simulation model for heart failure patients.
• To compare usual care with an early warning system, with and without a diagnostic algorithm.
• To account for individual patient characteristics and their impact on health outcomes.

Main Methods:

• A patient-level discrete event simulation model was developed using data from the Trans-European Network - Home-Care Management System study.
• The model was coded in RStudio and validated using the Assessment of the Validation Status of Health-Economic decision models tool.
• It incorporates 20 patient/disease characteristics and generates 8 distinct outcomes.

Main Results:

• Early warning systems increased outpatient visits by 2.99/year and life years by 0.81 (0.45 QALYs) compared to usual care, with higher costs (€11,249).
• Adding a diagnostic algorithm to the early warning system yielded a 0.92 life year gain (0.57 QALYs), reduced hospitalizations by 0.65/year, and increased costs by €9680.
• The model demonstrated robustness and validity when compared with external data and other models.

Conclusions:

• A validated patient-level simulation model for heart failure has been developed.
• The model can simulate diverse outcomes for various patient subgroups and interventions.
• It offers valuable insights for guiding research and developing novel heart failure treatment options.