Cost-effectiveness analysis of nanosecond pulsed field ablation against hemithyroidectomy and total thyroidectomy for benign thyroid nodules

Aim: Benign thyroid nodules (BTNs) are common, and although surgery remains an established treatment option for symptomatic cases, direct ablative therapies are increasingly considered as potential alternatives. This study aimed to evaluate the cost-effectiveness of nanosecond pulsed field ablation (nsPFA) as a first-line treatment for BTNs compared with hemithyroidectomy (HT) and total thyroidectomy (TT) in the US. Materials & methods: A cost-effectiveness model combining a decision tree followed by a Markov state-transition model was developed. The decision tree captured short-term outcomes following initial treatment with either nsPFA, HT or TT, while the Markov component projected long-term outcomes, including repeat treatment after nodule recurrence, hypothyroidism, hypoparathyroidism, surgical complications and death. Costs and health outcomes were estimated from a US commercial payer perspective over a 10-year time horizon. Deterministic and probabilistic sensitivity analyses were performed to explore parameter uncertainty. Results: Over 10 years, nsPFA was dominant over both HT and TT, resulting in cost savings of $23,851 and $69,865 in total direct medical costs, and quality-adjusted life-years gains of 0.32 and 0.88, respectively. Overall, thyroid nodule management costs were lower with nsPFA than with surgery alone, with nsPFA domination across deterministic and probabilistic analyses. Deterministic sensitivity analyses showed that results were most sensitive to assumptions regarding long-term health state costs. Conclusion: The results of this analysis suggest that nsPFA for symptomatic BTNs as a first-line strategy is a cost-effective alternative to HT and TT, associated with improved quality-adjusted life-years and reduced long-term treatment burden. These findings were robust in sensitivity analyses; however, further evidence on long-term outcomes is warranted to reduce uncertainty in the results.