A cost-utility study of the use of pregabalin added to usual care in refractory neuropathic pain patients in a Swedish setting.

OBJECTIVES

Patients refractory to older therapies for neuropathic pain (NeP) have few remaining therapeutic options. This study evaluates the cost-utility of pregabalin in the treatment of patients with refractory neuropathic pain in Sweden, from a healthcare and a societal perspective.

STUDY LIMITATIONS

The use of non-randomized (observational) data to determine the effectiveness of treatments for NeP. The use of non-Swedish data for some input parameters in the model.

METHODS

A previously constructed discrete event simulation model was adapted to compare pregabalin combined with usual care to usual care alone in a Swedish setting. Pain profiles were generated using clinical data from five non-randomized pregabalin studies in refractory NeP patients. Utility data were generated from a UK survey of patients with NeP. Cost data were generated from the Swedish Dental and Pharmaceutical Benefits Board (TLV's) product price database, a national NeP register, and a regional registry study. Indirect costs were estimated from published sources. One-way and probabilistic sensitivity analyses evaluated uncertainty in the model's output.

RESULTS

The incremental cost-effectiveness ratio (ICER) for pregabalin plus usual care treatment compared to usual care was 51,616 SEK/€5364 and 123,993 SEK/€12,886 with and without indirect costs, respectively. One-way sensitivity analyses confirmed the clinical input data as the main driver of the model; even considerable changes to all other input parameters had only a modest effect on the ICER. The ICER remained well below a conservative threshold of 347,495 SEK/€36,113/£30,000 in all scenarios modelled.

CONCLUSIONS

This study found pregabalin combined with usual care to be cost-effective compared to usual care in patients with refractory NeP from a Swedish Health Care perspective. Moreover, sensitivity analysis showed pregabalin's cost-effectiveness to be robust in all scenarios modelled.