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Impact of treatment decision algorithms on treatment costs in recurrent glioblastoma: a health economic study

Journal Paper/Review - Dec 2, 2019

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Citation
Panje C, Putora P, Hundsberger T, Hottinger A, Roelcke U, Pesce G, Herrmann E, Matter-Walstra K. Impact of treatment decision algorithms on treatment costs in recurrent glioblastoma: a health economic study. Swiss Med Wkly 2019; 149:w20153.
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Type
Journal Paper/Review (English)
Journal
Swiss Med Wkly 2019; 149
Publication Date
Dec 2, 2019
Issn Print
Issn Electronic
1424-3997
Pages
w20153
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Brief description/objective

AIMS
Recurrent glioblastoma (GBM) is a disease with poor prognosis. Although several therapeutic approaches such as chemotherapy, irradiation or surgery have been investigated, there is no established standard therapy. A recent survey among Swiss neuro-oncology centres has shown considerable controversy in the treatment recommendations for any specific scenario of recurrent GBM. In view of the cost differences of the available treatment alternatives, the aim of our study was assess the financial impact of different institutional therapeutic strategies for recurrent GBM in Switzerland.

METHODS
We created a decision analytic model for each of the eight centres participating in the initial study with a centre-specific treatment algorithm to evaluate the average treatment cost per patient. The probability of decision criteria was varied by univariate and probabilistic sensitivity analysis over a wide range to account for the high level of uncertainty. Treatment costs were calculated from the perspective of the Swiss healthcare payer.

RESULTS
Mean treatment costs per patient calculated on the basis of the institutional treatment algorithms ranged from CHF 13,748 to CHF 22,072 depending on the probability of individual decision criteria. The most influential decision factors for the mean treatment costs were the probability of fit patients and the proportion of patients with resectable tumour recurrences. There was a significant correlation between the complexity of treatment algorithms in a centre and the resulting mean treatment costs.

CONCLUSIONS
Institutional treatment algorithms can be used to estimate the average treatment costs per patient, which are, however, highly sensitive to probability changes of individual decision criteria. Our study demonstrates a high variability in treatment costs for recurrent GBM among eight Swiss neuro-oncology centres based on individual institutional treatment algorithms.