Objectives: To identify direct relationships between health states in two heart failure treatments Markov chains. After that, this study seeks to observe if the identified direct relationships between health states for the studied treatments are the same.

Methods: The Markov chains generated by Thokala et al. (2020) to evaluate the use of telemonitoring versus the usual care for heart failure were used to identify the direct relationships between the following health states: stable disease; severity degree #1; severity degree #2; severity degree #3; and death. The Weighted Influence Non-linear Gauge System (WINGS) multi-criteria decision analysis (MCDA) approach was applied in the telemonitoring and usual care Markov chains separately. To apply WINGS, each health state was considered an element, the probability to remain in a health state was the strength degree of each health state in the chain, the probability transition from a health state to another was the influence degree that a health state exerts over another.

Results: It was identified eight direct relationships in both chains: remain in stable disease; remain in severity degree #1; remain in severity degree #2; remain in severity degree #3; remain in the death; from severity degree #2 to severity degree #1; from severity degree #3 to severity degree #2; from severity degree #3 to death. In addition, it was identified the direct relationship from severity degree #2 to death only for usual care.

Conclusions: The MCDA approach here presented showed a way to identify direct relationships between health states in Markov chains. Thus, it was possible to note that there is a direct relationship from severity degree #2 to death only in the usual care Markov chain.