Clinical score system in the treatment of Cushing's disease: failure to identify discriminative variables from the German Cushing's Registry

Remission in Cushing's disease is predicted by cortisol burden and its withdrawal following pituitary surgery

AIM

To ascertain the predictors of remission and relapse in patients of Cushing's disease (CD) undergoing pituitary transsphenoidal surgery (TSS).

METHODS

Patients with CD subjected to TSS over 35 years at a tertiary care center were included. Patients were grouped into remission and persistent disease at 1 year after surgery, and were further followed up for relapse. Demographic, clinical, biochemical, histological, radiological and post-operative follow-up parameters were analyzed.

RESULTS

Of the 152 patients of CD, 145 underwent TSS. Remission was achieved in 95 (65.5%) patients at 1 year. Patients in remission had shorter duration of symptoms prior to presentation (p = 0.009), more frequent presence of proximal myopathy (p = 0.038) and a tumor size of < 2.05 cm (p = 0.016) in comparison to those with persistent disease. Post-TSS, immediate post-operative 0800-h cortisol (< 159.85 nmol/L; p = 0.001), histological confirmation of tumor (p = 0.045), duration of glucocorticoid replacement (median 90 days; p = 0.001), non-visualization of tumor on MRI (p = 0.003), new-onset hypogonadism (p = 0.001), 3-month 0800-h cortisol (< 384.9 nmol/L; p = 0.001), resolution of diabetes (p = 0.001) and hypertension (p = 0.001), and recovery of hypothalamic-pituitary-adrenal axis (p = 0.018) favored remission. In logistic regression model, requirement of glucocorticoid replacement (p = 0.033), and resolution of hypertension post-TSS (p = 0.003) predicted remission. None of the parameters could predict relapse.

CONCLUSION

The study could ascertain the predictors of remission in CD. Apart from the tumor characteristics, surgical aspects and low post-operative 0800-h cortisol, the results suggest that baseline clinical parameters, longer glucocorticoid replacement, and resolution of metabolic complications post-TSS predict remission in CD. Long-term follow-up is essential to look for relapse.

The application of multi-criteria decision analysis to inform in resource allocation

Background: There is a perception held by payers that orphan products are expensive. As a result, the current health technology assessment systems might be too restrictive for orphan drugs, therefore potentially denying patients access to life-saving medicines. While price is important, it should be considered in relation to a broader range of disease-related product attributes that are not necessarily considered by many health technology assessment agencies. To overcome these challenges, multi-criteria decision analysis has been proposed as an alternative to evaluate technologies. Methods: A targeted literature review was conducted to identify the most frequently cited attributes in multi-criteria decision analysis (MCDA) in rare diseases. From the leading attributes identified, we developed a multi-criteria decision analysis framework with which to aggregate the orphan drug values. We subsequently reviewed and plotted the relationship between single attributes and the average annual treatment costs for 8 drugs used in the treatment of rare endocrine diseases. The annual treatment costs were based on UK list prices for the average daily dose per patient. Results: The five most frequently mentioned attributes in the literature were as follows: Disease severity, Unmet need (or availability of therapeutic alternatives), Comparative effectiveness or efficacy, Quality of evidence and Safety & tolerability. Results from the multi-criteria decision analysis framework indicate a wide range of average annual per-patients costs for drugs intended for the same diseases, and likewise for diseases with a similar level of Disease severity. Conclusions: Multi-criteria decision analysis may offer a viable alternative to support discussion in reimbursement decisions for orphan drugs. The analyses can be used to inform investigations on the application of MCDAs in rare diseases.