TY  - JOUR
ID  - enlighten272593
UR  - https://eprints.gla.ac.uk/272593/
IS  - 5
A1  - Kazmi, Syed
A1  - Kambhampati, Chandrasekhar
A1  - Cleland, John G.F.
A1  - Cuthbert, Joe
A1  - Kazmi, Khurram Shehzad
A1  - Pellicori, Pierpaolo
A1  - Rigby, Alan S.
A1  - Clark, Andrew L.
Y1  - 2022/10//
N2  - 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.
PB  - Wiley
JF  - ESC Heart Failure
VL  - 9
SN  - 2055-5822
TI  - Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure
SP  - 3009
AV  - public
EP  - 3018
ER  -