Background and aims of the study: Several different biological, bioprosthetic and mechanical devices are available for heart valve replacement. We present a quantitative methodology to estimate the (event-free) life-expectancy and lifetime risk of valve-related events for individual patients after implantation of any one of these valve types.

Methods: We modelled the age-dependent prognosis of a patient after aortic valve replacement with a discrete-time Markov model and Monte-Carlo simulation to estimate (event-free) life-expectancy and life-time risk of valve-related events, respectively. Quantitative estimates to parameterize these models used hypothetical devices and presumed data were based on a limited review of published literature.

Results: This decision-analytical approach allowed an estimation of the overall and event-free life-expectancy as well as the lifetime risk of valve-related events after implantation of different types of prosthetic heart valve in the aortic position. In the current, hypothetical model, one valve type excelled for all age groups in terms of life expectancy and life-time risk of valve-related events. The choice of the second-best alternative varied according to patient age and comorbidity. Sensitivity analyses showed results to be especially dependent on the durability of the replacement valve and surgical risk.

Conclusions: This methodological approach is very flexible, and its quantitative results may guide decision making, if increasing quantitative information on heart valve prosthesis performance becomes available in future. Markov models and Monte Carlo simulation may be used to obtain a better understanding of the effect that different types of prosthetic heart valves have on patient prognosis, while quantitative results may help cardiologists and cardiac surgeons to choose a specific valve type for an individual patient.

How to cite: de Kruyk, A. R., van der Meulen, J. H., van Herwerden, L. A., Bekkers, J. A., Steyerberg, E. W., Dekker, R., & Habbema, J. D. (1998). Use of Markov series and Monte Carlo simulation in predicting replacement valve performances. The Journal of heart valve disease, 7(1), 4–12.