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. 2024 Feb 1;26(2):euae031.
doi: 10.1093/europace/euae031.

Economic analyses in cardiac electrophysiology: from clinical efficacy to cost utility

Maria Hee Jung Park Frausing  1   2 , Jens Cosedis Nielsen  1   2 , Caroline Louise Westergaard  3 , Christian Gerdes  1 , Jakob Kjellberg  4 , Giuseppe Boriani  3 , Mads Brix Kronborg  1   2
Affiliations

Economic analyses in cardiac electrophysiology: from clinical efficacy to cost utility

Maria Hee Jung Park Frausing et al. Europace. .

Abstract

Cardiac electrophysiology is an evolving field that relies heavily on costly device- and catheter-based technologies. An increasing number of patients with heart rhythm disorders are becoming eligible for cardiac interventions, not least due to the rising prevalence of atrial fibrillation and increased longevity in the population. Meanwhile, the expansive costs of healthcare face finite societal resources, and a cost-conscious approach to new technologies is critical. Cost-effectiveness analyses support rational decision-making in healthcare by evaluating the ratio of healthcare costs to health benefits for competing therapies. They may, however, be subject to significant uncertainty and bias. This paper aims to introduce the basic concepts, framework, and limitations of cost-effectiveness analyses to clinicians including recent examples from clinical electrophysiology and device therapy.

Keywords: Antibacterial envelope; Cardiac electrophysiology; Cardiac implantable electronic devices; Cost utility; Cost-effectiveness analysis; Early rhythm control; Economy; Quality-adjusted life year.

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Conflict of interest statement

Conflict of interest: M.H.J.P.F. received consulting fees from Medtronic outside the submitted work. G.B. reports speaker’s fees from Bayer, Boston, BMS, Daiichi Sankyo, and Janssen outside the submitted work. M.B.K. reports speaker’s fees from Bayer, Novartis, and Abbott outside the submitted work. J.C.N. was supported by a grant from the Novo Nordisk Foundation (NNF16OC0018658). Remaining authors declare no conflicts of interest. J.C.N. and G.B. are Editors of Europace and were not involved in the peer review process or publication decision.

Figures

Graphical Abstract
Graphical Abstract
Figure 1
Figure 1
Example of a decision tree (left) and a simplified three-state Markov model (right). An important distinction between these two models is that decision trees are unidirectional, whereas Markov models are cyclic. The different disease states in the models should represent significant events both clinically and economically. Squares indicate decision nodes. Triangles indicate terminal nodes. P’s indicate transition probabilities, which are usually estimated from literature. P7 is an absorptive state (death) for which the transition probability will always be 1.
Figure 2
Figure 2
Opportunity costs are the potential profits forgone by choosing one alternative over the other.
Figure 3
Figure 3
Two simplified schematic overviews of calculation of QALYs with usual care vs. the intervention under investigation. Quality-adjusted life years are estimated by multiplying utility weights associated with a given health condition with the time spent in this state. ΔQALY indicates QALY gained. A utility value of 0 corresponds to the utility of being dead. QALYs, quality-adjusted life years; QoL, quality of life.
Figure 4
Figure 4
(A) The cost-effectiveness plane. The Y-axis indicates incremental cost. The X-axis indicates incremental health benefits of the new intervention. (B) The CEAC. Note that the CEAC also provides information about the error rate (1-p). Solid lines indicates an intervention. As shown, the probability of cost-effective is 60% at the given cost-effectiveness threshold, and accordingly, the probability of not being cost-effective is 100% − 60% = 40%. CEAC, cost-effectiveness acceptability curve.
Figure 5
Figure 5
Example of tornado diagram. Tornado diagrams are used to depict results from one-way sensitivity analyses. The parameters in a tornado diagram are sorted from the highest (top) to the lowest range. The bars visually display which parameters have the strongest influence on the outcome; as shown, a change in Parameters 1 and 2 could potentially influence the conclusions of the analyses. Red and turquoise colours indicate the highest vs. the lowest values of each range.
See this image and copyright information in PMC

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