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

• Published in: ESC Heart Failure Vol. 9; no. 5; pp. 3009 - 3018
• Main Authors: Kazmi, Syed, Kambhampati, Chandrasekhar, Cleland, John G.F., Cuthbert, Joe, Kazmi, Khurram Shehzad, Pellicori, Pierpaolo, Rigby, Alan S., Clark, Andrew L.
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.10.2022; John Wiley and Sons Inc; Wiley
• Subjects: Absorbing Markov chains; Artificial Intelligence; Chronic Disease; Disease trajectory; Ejection fraction; Health care; Heart failure; Heart Failure - epidemiology; Heart Failure - therapy; Hospitalization; Hospitals; Humans; Machine learning; Markov analysis; Markov Chains; Mortality; Original; Patients; Population; Probability; Risk Assessment; Heart failure; Disease trajectory; Artificial intelligence; Absorbing Markov chains; Machine learning
• DOI: 10.1002/ehf2.14028
• ISSN: 2055-5822

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 4 months: 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 (4 months) 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 4 months and from 4 to 8 months) 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 16 months, 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 2 years, a patient had a probability of dying of 0.19, and the observed value was 0.18. Conclusions A model derived from the first 8 months 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.