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

Panje, Cédric; Putora, Paul M.; Hundsberger, Thomas; Hottinger, Andreas F; Roelcke, Ulrich; Pesce, Gianfranco; Herrmann, Evelyn; Matter-Walstra, Klazien (2019). Impact of treatment decision algorithms on treatment costs in recurrent glioblastoma: a health economic study. Swiss medical weekly, 149, w20153. EMH Media 10.4414/smw.2019.20153

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Radiation Oncology

UniBE Contributor:

Panje, Cédric; Putora, Paul Martin and Herrmann, Evelyn

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1424-3997

Publisher:

EMH Media

Language:

English

Submitter:

Beatrice Scheidegger

Date Deposited:

18 Dec 2019 15:55

Last Modified:

07 Sep 2020 10:30

Publisher DOI:

10.4414/smw.2019.20153

PubMed ID:

31800087

BORIS DOI:

10.7892/boris.136373

URI:

https://boris.unibe.ch/id/eprint/136373

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