Literature DB >> 21423075

A computational model to compare different investment scenarios for mini-stereotactic frame approach to deep brain stimulation surgery.

M Lanotte1, M Cavallo, A Franzini, M Grifi, E Marchese, M Pantaleoni, M Piacentino, D Servello.   

Abstract

UNLABELLED: Deep brain stimulation (DBS) alleviates symptoms of many neurological disorders by applying electrical impulses to the brain by means of implanted electrodes, generally put in place using a conventional stereotactic frame. A new image guided disposable mini-stereotactic system has been designed to help shorten and simplify DBS procedures when compared to standard stereotaxy. A small number of studies have been conducted which demonstrate localization accuracies of the system similar to those achievable by the conventional frame. However no data are available to date on the economic impact of this new frame. AIM: The aim of this paper was to develop a computational model to evaluate the investment required to introduce the image guided mini-stereotactic technology for stereotactic DBS neurosurgery.
METHODS: A standard DBS patient care pathway was developed and related costs were analyzed. A differential analysis was conducted to capture the impact of introducing the image guided system on the procedure workflow. The analysis was carried out in five Italian neurosurgical centers.
RESULTS: A computational model was developed to estimate upfront investments and surgery costs leading to a definition of the best financial option to introduce the new frame. Investments may vary from Euro 1.900 (purchasing of Image Guided [IG] mini-stereotactic frame only) to Euro 158.000.000. Moreover the model demonstrates how the introduction of the IG mini-stereotactic frame doesn't substantially affect the DBS procedure costs.

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Year:  2010        PMID: 21423075

Source DB:  PubMed          Journal:  J Neurosurg Sci        ISSN: 0390-5616            Impact factor:   2.279


  1 in total

1.  Integration of the OpenIGTLink network protocol for image-guided therapy with the medical platform MeVisLab.

Authors:  Jan Egger; Junichi Tokuda; Laurent Chauvin; Bernd Freisleben; Christopher Nimsky; Tina Kapur; William Wells
Journal:  Int J Med Robot       Date:  2012-02-28       Impact factor: 2.547

  1 in total

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