Related Experiment Video
Updated: Jun 17, 2025
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
Disease progression in chronic heart failure is linear: Insights from multistate modelling
Syed Kazmi1,2, Chandrasekhar Kambhampati2, Alan S Rigby3
1Department of Academic Cardiology, Hull University Teaching Hospital NHS Trust, Castle Hill Hospital, Hull, UK.
Insights
Chronic heart failure (HF) progression is more linear than assumed. A Markov model accurately predicts HF disease trajectory and patient outcomes over time, aiding healthcare planning.
Area of Science:
- Cardiology
- Biostatistics
- Public Health
Background:
- Chronic heart failure (HF) poses significant challenges to patient care and healthcare systems.
- Understanding HF disease progression patterns is crucial for effective management and resource allocation.
Purpose of the Study:
- To describe the disease trajectory of patients with chronic heart failure (HF) using a four-state Markov model.
- To assess the predictability of HF progression at a population level.
Main Methods:
- A multistate Markov model was developed using data from 4918 patients followed for 2 years.
- Model predictions for transitions (e.g., hospitalization to death, rehospitalization) were compared with observed data up to 5 years.
- Covariate inclusion in the model did not enhance predictive accuracy.
Main Results:
- The Markov model accurately predicted HF population behavior in subsequent years.
- Predicted transition probabilities closely matched observed rates, demonstrating model validity.
- The model estimated average life expectancy around 10-13 years, aligning with observed 5-year mortality.
Conclusions:
- A Markov chain model indicates a constant annual transition proportion between states for chronic HF patients.
- HF disease progression at a population level appears more linear and predictable than commonly believed.
- Current patient status is a strong predictor of future HF course.
Aims:
Understanding the pattern of disease progression in chronic heart failure (HF) may inform patient care and healthcare system design. We used a four-state Markov model to describe the disease trajectory of patients with HF.
Methods And Results:
Consecutive patients (n = 4918) were enrolled (median age 75 [67-81] years, 61.3% men, 44% with HF and reduced ejection fraction). We generated a model by observing events during the first 2 years of follow-up. The model yielded surprisingly accurate predictions of how a population with HF will behave during subsequent years. As examples, the predicted transition probability from hospitalization to death was 0.11; the observed probabilities were 0.13, 0.14, and 0.16 at 3, 4, and 5 years, respectively. Similarly, the predicted transition intensity for rehospitalization was 0.35; the observed probabilities were 0.38, 0.34, and 0.35 at 3, 4, and 5 years, respectively. A multivariable model including covariates thought to influence outcome did not improve accuracy. Predicted average life expectancy was approximately 10 years for the unadjusted model and 13 years for the multivariable model, consistent with the observed mortality of 41% at 5 years.
Conclusions:
A multistate Markov chain model for patients with chronic HF suggests that the proportion of patients transitioning each year from a given state to another remains constant. This finding suggests that the course of HF at a population level is more linear than is commonly supposed and predictable based on current patient status.
More Related Videos
Related Concept Videos
01:17Pathophysiology of Heart Failure
02:07Tumor Progression
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...