An Algorithm for Management of Deep Brain Stimulation Battery Replacements: Devising a Web‐Based Battery Estimator and Clinical Symptom Approach. Issue 2 (30th May 2012)

• Record Type: Journal Article
• Authors: Montuno, Michael A.; Kohner, Andrew B.; Foote, Kelly D.; Okun, Michael S.
• Is Part Of: Neuromodulaton. Volume 16:Issue 2(2013)
• Page Start: 147
• Page End: 153
• Publication Date: 2012-05-30
• Subjects: Central nervous system -- Physiology -- Periodicals; Central nervous system -- Diseases -- Periodicals; 616.8
• DOI: 10.1111/j.1525-1403.2012.00457.x
• Languages: English
• ISSNs: 1094-7159

Abstract

<abstract abstract-type="main" xml:lang="en"> <title> <x xml:space="preserve">Abstract</x> </title> <p> <bold>Objective: </bold> Deep brain stimulation (DBS) is an effective technique that has been utilized to treat advanced and medication‐refractory movement and psychiatric disorders. In order to avoid implanted pulse generator (IPG) failure and consequent adverse symptoms, a better understanding of IPG battery longevity and management is necessary.</p> <p> <bold>Background: </bold> Existing methods for battery estimation lack the specificity required for clinical incorporation. Technical challenges prevent higher accuracy longevity estimations, and a better approach to managing end of DBS battery life is needed.</p> <p> <bold>Methods: </bold> The literature was reviewed and DBS battery estimators were constructed by the authors and made available on the web at http://mdc.mbi.ufl.edu/surgery/dbs-battery-estimator. A clinical algorithm for management of DBS battery life was constructed. The algorithm takes into account battery estimations and clinical symptoms.</p> <p> <bold>Results: </bold> Existing methods of DBS battery life estimation utilize an interpolation of averaged current drains to calculate how long a battery will last. Unfortunately, this technique can only provide general approximations. There are inherent errors in this technique, and these errors compound with each iteration of the battery estimation. Some of these errors cannot be accounted for in the estimation process, and some of the errors stem from device variation, battery voltage dependence, battery usage, battery chemistry, impedance fluctuations, interpolation error, usage patterns, and self‐discharge. We present web‐based battery estimators along with an algorithm for clinical management. We discuss the perils of using a battery estimator without taking into account the clinical picture.</p> <p> <bold>Conclusion: </bold> Future work will be needed to provide more reliable management of implanted device batteries; however, implementation of a clinical algorithm that accounts for both estimated battery life and for patient symptoms should improve the care of DBS patients.</p> </abstract>