Surgical-decision making in the setting of unsuspected N2 disease: a cost-effectiveness analysis.

Background:Identification of unsuspected nodal metastasis may occur at the time of operation for a stage I non-small cell lung cancer. Guidelines for this scenario are unclear. Our goal was to assess the cost-effectiveness of aborting the operation in an attempt to first provide neoadjuvant systemic therapy compared with upfront resection. Methods:A computer simulation Markov model with a lifetime horizon was constructed to compare the costs and clinical outcomes, as measured by quality-adjusted life-years (QALYs), of upfront resection at the time of identification of unsuspected N2 mediastinal disease vs. aborting initial resection and continuing with neoadjuvant therapy prior to resection. Input parameters for the model were derived from published literature with costs measured from the healthcare perspective. The incremental cost-effectiveness ratio (ICER) was evaluated with a willingness-to-pay (WTP) threshold of $150,000/QALY. Both deterministic (one-, two-, and three-way) and probabilistic sensitivity analysis (PSA) were performed to assess the impact of variation in input parameter values on model results. Results:Aborting initial resection in favor of neoadjuvant therapy resulted in both higher costs ($40,415 vs. $29,873) and more QALYs (3.95 vs. 2.84) relative to upfront resection, yielding an ICER of $9,526/QALY. While variation in overall survival had a significant impact on the ICER, perioperative variables did not. As the annual mortality of best-case therapy in the abort group increased from a base-case estimate of 11% to 15%, the ICER exceeded the WTP threshold of $150,000/QALY. Subsequent one- and two-way sensitivity analyses did not find substantially alter the overall results. PSA resulted in aborting resection to be cost-effective in 99.7% of samples, with 13% of samples dominating upfront resection. Conclusions:Treatment of stage IIIa lung cancer requires the input of a multidisciplinary team who must consider cost, quality of life, and overall survival. As new treatments are developed, further analyses should be performed to determine optimal therapy.