Cost-effectiveness analysis in cardiac surgery: A review of its concepts and methodologies

Abstract

Cost-effectiveness analysis (CEA) in cardiac surgery continues to grow in relevance with increasing health care expenditures, a greater emphasis on value-based care, the continuing development of costly surgical and noninvasive technologies, advances in cardiac devices, and changes in eligibility criteria over the past two decades. Although the rapidly evolving surgical technologies pose challenges to CEA, improvements in gathering and leveraging long-term economic and clinical data alongside trials and in cardiac surgery registries represent future opportunities for the field. As such, it is important for cardiac surgeons to understand CEA with respect to existing and future surgical therapies. Herein, we review the fundamental principles of cost-effectiveness analysis theory and discuss recent cost-effectiveness studies on cardiac surgery.

FIGURE E1.. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram of study inclusion

Central Picture.. Publication trends in cardiac surgery cost-effectiveness analysis since January 2000.

FIGURE 1.. Cost-effectiveness analyses in cardiac surgery published since January 2000.

LVAD, left ventricular assist device; DT, destination therapy; DES, drug-eluting stent; TAVR, transcatheter aortic valve replacement; CF, continuous-flow; ACA, Affordable Care Act; BTT, bridge to transplant; AS, aortic stenosis; MR, mitral regurgitation.

FIGURE 2:. Hypothetical individual patient’s follow-up duration adjusted for quality-of-life.

The patient’s health state is longitudinally measured using a health state classification instrument at preoperative and several postoperative time points. The health states are then converted into utilities using HRQoL weights based on societal preferences. Quality-adjusted life years (QALYs) are represented by the area under the curve, i.e. the sum of each period multiplied by the HRQoL/utility during that period. Zero indicates death while 1 indicates perfect health. HRQoL, health-related quality-of-life; QALY, quality-adjusted life year.

FIGURE 3:. Hypothetical probabilistic sensitivity analysis (PSA) plotted on a cost-effectiveness plane.

When comparing two competing surgeries, A versus B, a scatterplot of the difference in average costs and QALYs per PSA iteration can be created with a diagonal representing the cost-effectiveness threshold. The percentage of points lying to the right of a given threshold line indicates the probability that the intervention is cost-effective relative to the competing intervention. Multiple cost-effectiveness thresholds can be plotted to determine the impact on the probability of cost-effectiveness. The lower right quadrant represents iterations where the intervention A is “dominant” due to having lower incremental costs and higher incremental QALYs than B. The upper left quadrant represents iterations where A is “dominated” due to higher incremental costs and lower incremental QALYs. The upper right and lower left quadrants represent tradeoffs between higher and lower incremental costs and QALYs, respectively. QALY, quality-adjusted life year.

FIGURE 4:. Cost-effectiveness acceptability curves.

This graph shows the probability of each intervention being cost-effective given a range for society’s willingness to pay to gain one QALY. As the cost-effectiveness threshold increases, the probability that surgery A is cost-effective increases while that of B decreases (equal to 100% - probability A is cost-effective). The vertical lines represent just two of the cost-effectiveness thresholds and correspond directly to the diagonals on the cost-effectiveness plane. QALY, quality-adjusted life year.

FIGURE 5:. One-way deterministic sensitivity analyses across several model inputs.

The base case scenario represents the incremental cost-effectiveness ratio (ICER) point estimate when comparing two surgeries. In deterministic sensitivity analysis, a given input, e.g. the HRQoL weight, is varied in the model to determine how upper and lower bound assumptions impact outcomes. For example, when comparing surgery A versus B, assuming a higher HRQoL following A lowers the ICER, as incremental QALYs increase. QALY, quality-adjusted life year; HRQoL, health-related quality-of-life.