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一种深部脑刺激电池更换管理算法:设计基于网络的电池估算器和临床症状方法。

An algorithm for management of deep brain stimulation battery replacements: devising a web-based battery estimator and clinical symptom approach.

机构信息

Department of Neurology, Center for Movement Disorders & Neurorestoration, University of Florida, Gainesville, FL 32607, USA.

出版信息

Neuromodulation. 2013 Mar-Apr;16(2):147-53. doi: 10.1111/j.1525-1403.2012.00457.x. Epub 2012 May 30.

Abstract

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.

摘要

目的

深部脑刺激(DBS)是一种有效的技术,已被用于治疗晚期和药物难治性运动和精神障碍。为了避免植入脉冲发生器(IPG)故障和随之而来的不良症状,需要更好地了解 IPG 电池的寿命和管理。

背景

现有的电池估计方法缺乏临床应用所需的特异性。技术挑战阻碍了更高精度的寿命估计,需要更好的方法来管理 DBS 电池寿命的结束。

方法

作者回顾了文献,并构建了 DBS 电池估算器,并在 http://mdc.mbi.ufl.edu/surgery/dbs-battery-estimator 上提供。构建了一种用于管理 DBS 电池寿命的临床算法。该算法考虑了电池估算和临床症状。

结果

现有的 DBS 电池寿命估计方法利用平均电流消耗的插值来计算电池的剩余寿命。不幸的是,这种技术只能提供大致的估计。该技术存在固有误差,并且这些误差会随着电池估算的每次迭代而累积。这些误差中的一些无法在估算过程中得到解释,而另一些则源于设备变化、电池电压依赖性、电池使用、电池化学、阻抗波动、插值误差、使用模式和自放电。我们提供了基于网络的电池估算器以及用于临床管理的算法。我们讨论了在不考虑临床情况的情况下使用电池估算器的危险。

结论

未来需要进一步努力,以更可靠地管理植入设备电池;然而,实施一种同时考虑电池估计寿命和患者症状的临床算法,应该可以改善 DBS 患者的护理。

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