Usefulness of microsimulation to translate valve performance into patient outcome: Patient prognosis after aortic valve replacement with the Carpentier–Edwards supra-annular valve

How microsimulation translates outcome estimates to patient lifetime event occurrence in the setting of heart valve disease

Treatment decisions in healthcare often carry lifelong consequences that can be challenging to foresee. As such, tools that visualize and estimate outcome after different lifetime treatment strategies are lacking and urgently needed to support clinical decision-making in the setting of rapidly evolving healthcare systems, with increasingly numerous potential treatments. In this regard, microsimulation models may prove to be valuable additions to current risk-prediction models. Notable advantages of microsimulation encompass input from multiple data sources, the ability to move beyond time-to-first-event analysis, accounting for multiple types of events and generating projections of lifelong outcomes. This review aims to clarify the concept of microsimulation, also known as individualized state-transition models, and help clinicians better understand its potential in clinical decision-making. A practical example of a patient with heart valve disease is used to illustrate key components of microsimulation models, such as health states, transition probabilities, input parameters (e.g. evidence-based risks of events) and various aspects of mortality. Finally, this review focuses on future efforts needed in microsimulation to allow for increasing patient-tailoring of the models by extending the general structure with patient-specific prediction models and translating them to meaningful, user-friendly tools that may be used by both clinician and patient to support clinical decision-making.

Aggressive tissue aortic valve replacement in younger patients and the risk of re-replacement: Implications from microsimulation analysis

OBJECTIVE

Advances in transcatheter aortic valve replacement have led to the consideration of tissue aortic valve replacement in younger patients. Part of this enthusiasm is the presumption that younger patients would have more flexibility in future treatment options, such as a primary surgical aortic valve replacement followed later by transcatheter aortic valve replacement(s) (valve-in-valve), vice versa, or other permutations. We created a microsimulation model using published longevity of tissue valves to predict the outcomes of patients after primary tissue surgical aortic valve replacement.

METHODS

The model calculated survival by incorporating annual mortality (Social Security Administration) and mortality from re-replacements (Society of Thoracic Surgeons) in patients with surgical aortic valve replacement. Freedom from reoperation for structural valve degeneration incorporated best published data to determine the annual risk of re-replacement for structural valve degeneration based on implant duration and stratified by patient age. A constant rate of re-replacement for nonstructural valve degeneration indications was also incorporated. Each simulation was performed for 50,000 individuals. Kaplan-Meier curves were generated to represent survival. All simulations were run within the MATLAB environment (The MathWorks, Inc, Natick, Mass).

RESULTS

Earlier decades of life at primary surgical aortic valve replacement were associated with higher incidences of re-replacements and especially multiple re-replacements. For those patients receiving a primary tissue surgical aortic valve replacement at age 50 years, 57.2% will require a second valve, 18.0% will require a third valve, and 1.6% will require a fourth valve with average operative mortalities of 2.9%, 4.8%, and 7.3%, respectively. A 50-year-old patient at primary surgical aortic valve replacement has a 13.1% chance of re-replacement before turning 60 years of age.

CONCLUSIONS

Microsimulation incorporates changing hazards to estimate the risk of aortic valve re-replacement in patients undergoing tissue surgical aortic valve replacement and may be a starting point for patient education and health care economic planning.

Congenital heart defect repair with ADAPT tissue engineered pericardium scaffold: An early-stage health economic model

OBJECTIVE

The objective of this study was to evaluate the cost effectiveness of tissue engineered bovine tissue pericardium scaffold (CardioCel) for the repair of congenital heart defects in comparison with surgery using xenogeneic, autologous, and synthetic patches over a 40-year time horizon from the perspective of the UK National Health Service.

METHODS

A six-state Markov state-transition model to model natural history of disease and difference in the interventional effect of surgeries depending on patch type implanted. Patches differed regarding their probability of re-operation due to patch calcification, based on a systematic literature review. Transition probabilities were based on the published literature, other clinical inputs were based on UK registry data, and cost data were based on UK sources and the published literature. Incremental cost-effectiveness ratio (ICER) was determined as incremental costs per quality adjusted life years (QALY) gained. We used a 40-year analytic time-horizon and adopted the payer perspective. Comprehensive sensitivity analyses were performed.

RESULTS

According to the model predictions, CardioCel was associated with reduced incidence of re-operation, increased QALY, and costs savings compared to all other patches. Cost savings were greatest compared to synthetic patches. Estimated cost savings associated with CardioCel were greatest within atrioventricular septal defect repair and lowest for ventricular septal defect repair. Based on our model, CardioCel relative risk for re-operations is 0.938, 0.956and 0.902 relative to xenogeneic, autologous, and synthetic patches, respectively.

CONCLUSION

CardioCel was estimated to increase health benefits and save cost when used during surgery for congenital heart defects instead of other patches.

Influence of the characteristics of Japanese patients on the long-term outcomes after aortic valve replacement: results of a microsimulation

Objectives

The aim of this study was to explore the influence of the characteristics of Japanese patients on the long-term outcomes after aortic valve replacement with either mechanical or biological prostheses by means of a microsimulation.

Methods

A microsimulation model was used to simulate the lives of patients living in Japan and in the United States after aortic valve replacement. The background mortality data taken from the 21st complete life table of Japan and the bleeding hazard determined from a meta-analysis of long-term results in Japanese institutions were used to simulate Japanese patients. The life expectancy, event-free life expectancy, and lifetime risk of structural valve degeneration were calculated. A sensitivity analysis for various bleeding hazards was performed.

Results

Regarding the event-free life expectancy, the age crossover points between the two valve types were 64–65 and 57–58 years for Japanese and American patients, respectively. Regarding the life expectancy, the age crossover points were 88–89 and 64–65 years, respectively, for Japanese and American patients. The lifetime risk of structural valve degeneration was higher in Japanese patients than in American patients. The sensitivity analysis showed that the age crossover points were sensitive to the hazard of bleeding complications.

Conclusions

The long-term clinical outcomes after aortic valve replacement were simulated with a microsimulation model. The results indicated that the age crossover points in the advantages and disadvantages between mechanical valves and bioprostheses may be higher in Japanese patients than in American subjects.