Disease progression in chronic heart failure is linear: Insights from multistate modelling

Kazmi, Syed; Kambhampati, Chandrasekhar; Rigby, Alan S.; Cleland, John G. F.; Kazmi, Khurram S; Cuthbert, Joe; Pellicori, Pierpaolo; Clark, Andrew L.

Abstract

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.

Citation

Kazmi, S., Kambhampati, C., Rigby, A. S., Cleland, J. G. F., Kazmi, K. S., Cuthbert, J., Pellicori, P., & Clark, A. L. (online). Disease progression in chronic heart failure is linear: Insights from multistate modelling. European journal of heart failure, https://doi.org/10.1002/ejhf.3400

• Journal Article Type: Article
• Acceptance Date: Jul 12, 2024
• Online Publication Date: Aug 6, 2024
• Deposit Date: Aug 6, 2024
• Journal: European Journal of Heart Failure
• Print ISSN: 1388-9842
• Electronic ISSN: 1879-0844
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1002/ejhf.3400
• Keywords: Absorbing Markov chains; Artificial intelligence; Disease trajectory; Heart failure; Machine learning; Multistate modelling
• Public URL: https://hull-repository.worktribe.com/output/4784911