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An Algorithm for Management of Deep Brain Stimulation Battery Replacements: Devising a Web-Based Battery Estimator and Clinical Symptom Approach
Article first published online: 30 MAY 2012
© 2012 International Neuromodulation Society
Neuromodulation: Technology at the Neural Interface
Volume 16, Issue 2, pages 147–153, March/April 2013
How to Cite
Montuno, M. A., Kohner, A. B., Foote, K. D. and Okun, M. S. (2013), An Algorithm for Management of Deep Brain Stimulation Battery Replacements: Devising a Web-Based Battery Estimator and Clinical Symptom Approach. Neuromodulation: Technology at the Neural Interface, 16: 147–153. doi: 10.1111/j.1525-1403.2012.00457.x
Conflict of Interest: Mr. Montuno, Mr. Kohner, and Dr. Foote report no disclosures. Dr. Okun serves as a consultant for the National Parkinson Foundation and has received research grants from the National Institutes of Health (NIH), National Parkinson Foundation, Michael J. Fox Foundation, Parkinson Alliance, Smallwood Foundation, and UF Foundation. Dr. Okun has not received honoraria from industry or travel expenses for greater than 24 months. Dr. Okun has received royalties for publications with Demos, Manson, and Cambridge (movement disorders books). Dr. Okun has participated in CME activities on movement disorders sponsored by the University of South Florida CME office, PeerView, Prime, and Vanderbilt University. The institution receives training grants from Medtronic and ANS/St. Jude, and the PI has no financial interest in these grants. Dr. Okun has participated as a site principal investigator and/or co-investigator for several NIH, foundation, and industry sponsored trials over the years but has not received honoraria or speaking fees.
- Issue published online: 1 APR 2013
- Article first published online: 30 MAY 2012
- Received: December 4, 2011 Revised: February 21, 2012 Accepted: March 27, 2012
Objective: 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.
Background: 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.
Methods: 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.
Results: 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.
Conclusion: 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.