Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure

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

Abstract

Aims: Risk changes with the progression of disease and the impact of treatment. We developed a dynamic risk stratification Markov chain model using artificial intelligence in patients with chronic heart failure (CHF). Methods and results: We described the pattern of behaviour among 7496 consecutive patients assessed for suspected HF. The following mutually exclusive health states were defined and assessed every 4months: death, hospitalization, outpatient visit, no event, and leaving the service altogether (defined as no event at any point following assessment). The observed figures at the first transition (4months) weres 427 (6%), 1559 (21%), 2254 (30%), 1414 (19%), and 1842 (25%), respectively. The probabilities derived from the first two transitions (i.e. from baseline to 4months and from 4 to 8months) were used to construct the model. An example of the model's prediction is that at cycle 4, the cumulative probability of death was 14%; leaving the system, 37%; being hospitalized between 12 and 16months, 10%; having an outpatient visit, 8%; and having no event, 31%. The corresponding observed figures were 14%, 41%, 10%, 15%, and 21%, respectively. The model predicted that during the first 2years, a patient had a probability of dying of 0.19, and the observed value was 0.18. Conclusions: A model derived from the first 8months of follow-up is strongly predictive of future events in a population of patients with chronic heart failure. The course of CHF is more linear than is commonly supposed, and thus more predictable.

Citation

Kazmi, S., Kambhampati, C., Cleland, J., Cuthbert, J., Kazmi, K. S., Pellicori, P., Rigby, A. S., & Clark, A. L. (2022). Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure. ESC Heart Failure, https://doi.org/10.1002/ehf2.14028

• Journal Article Type: Article
• Acceptance Date: Jun 3, 2022
• Online Publication Date: Jun 23, 2022
• Publication Date: Jun 23, 2022
• Deposit Date: Jun 23, 2022
• Publicly Available Date: Jun 24, 2022
• Journal: ESC Heart Failure
• Electronic ISSN: 2055-5822
• Publisher: Wiley Open Access
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1002/ehf2.14028
• Keywords: Heart failure; Absorbing Markov chains; Disease trajectory; Artificial intelligence; Machine learning
• Public URL: https://hull-repository.worktribe.com/output/4018667
• Publisher URL: https://onlinelibrary.wiley.com/doi/epdf/10.1002/ehf2.14028