Cost-effectiveness of surgery for drug-resistant temporal lobe epilepsy in the US

Abstract

Objective: Surgery is an effective but costly treatment for many patients with drug-resistant temporal lobe epilepsy (DR-TLE). We aim to evaluate whether, in the United States, surgery is cost-effective compared to medical management for patients deemed surgical candidates and whether surgical evaluation is cost-effective for patients with DR-TLE in general.

Methods: We use a semi-Markov model to assess the cost-effectiveness of surgery and surgical evaluation over a lifetime horizon. We use second-order Monte Carlo simulations to conduct probabilistic sensitivity analyses to estimate variation in model output. We adopt both health care and societal perspectives, including direct health care costs (e.g., surgery, antiepileptic drugs) and indirect costs (e.g., lost earnings by patients and care providers.) We compare the incremental cost-effectiveness ratio to societal willingness to pay (∼$100,000 per quality-adjusted life-year [QALY]) to determine whether surgery is cost-effective.

Results: Epilepsy surgery is cost-effective compared to medical management in surgically eligible patients by virtue of being cost-saving ($328,000 vs $423,000) and more effective (16.6 vs 13.6 QALY) than medical management in the long run. Surgical evaluation is cost-effective in patients with DR-TLE even if the probability of being deemed a surgical candidate is only 5%. From a societal perspective, surgery becomes cost-effective within 3 years, and 89% of simulations favor surgery over the lifetime horizon.

Conclusion: For surgically eligible patients with DR-TLE, surgery is cost-effective. For patients with DR-TLE in general, referral for surgical evaluation (and possible subsequent surgery) is cost-effective. Patients with DR-TLE should be referred for surgical evaluation without hesitation on cost-effectiveness grounds.

Figure 1. Diagram of Markov decision-analytic model used for primary analysis (model 1)

Illustration of the primary and secondary models developed to study cost-effectiveness. (A) Diagram shows the structure of the decision-analytic model used for cost-effectiveness analysis in patients with DR-TLE deemed to be surgical candidates. The M nodes represent Markov nodes that are built into the decision tree, allowing the creation of iterative loops (1-year cycles) during which patients can transition between health states. The 3 health states are seizure-free, having seizures, and dead (not shown). Each branch of the tree is annotated with an average estimate and a probability distribution, allowing probabilistic sensitivity analysis (Monte Carlo simulations). (B) Diagram of the decision-analytic model used for secondary analysis (model 2). The decision-analytic model was restructured as shown in this diagram in order to conduct the secondary analysis (evaluating whether referral for surgical evaluation is a cost-effective strategy). DRE = drug-resistant epilepsy; ED = emergency department.

Figure 2. ICER vs time plot for surgery and referral for surgery

(A) Curves show how the incremental cost-effectiveness ratio (ICER) varies with time from surgery in patients found eligible for surgery. (B) Curves show how the ICER varies with time from surgical referral (and possible subsequent surgery) in patients with drug-resistance temporal lobe epilepsy (DR-TLE). Results of the analysis from the societal (orange) and health care (gray) perspective are shown. Negative values of ICER occur when surgery becomes cost-saving compared to medical management. QALY = quality-adjusted life-year.

Figure 3. Results of probabilistic sensitivity analysis

Scatterplots (A–D) show the results of 10,000 Monte Carlo simulations (probabilistic sensitivity analysis) of the decision-analytic models. Each blue dot represents the results of an individual simulation. Whenever a dot falls to the right of (below) the willingness to pay (WTP) line, the model would find surgery (model 1) or referral for surgical evaluation (model 2) to be cost-effective. QALY = quality-adjusted life-year; USD = US dollars.

Figure 4. Acceptability curves for surgery and referral for surgery strategies

Acceptability curves are generated by running a set of 10,000 simulations for a range of willingness to pay (WTP) and charting the proportion of simulations at each threshold that favor a surgical or medical strategy. For any 1 value of WTP on the horizontal axis, the proportion of simulation recommending surgery and medical management adds up to 1. (A) Acceptability curves for surgery in surgically eligible patients. (B) Acceptability curves for surgical evaluation in patients with drug-resistant epilepsy of the temporal lobe in general.