Is seizure frequency variance a predictable quantity?
Is seizure frequency variance a predictable quantity?
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DOI：
10.1002/acn3.519
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发表时间：
2018-02-01
影响因子：
5.3
通讯作者：
Theodore, William H.
中科院分区：
文献类型：
--
作者：
Goldenholz, Daniel M.;Goldenholz, Shira R.;Theodore, William H.
背景：目前还没有正式的方法来预测一个人的癫痫发作次数的预期范围。获得这样的预测将有利于开发更有效的临床试验，也有利于改善门诊患者的临床护理。方法：使用三个独立收集的患者日记数据集，我们探讨了癫痫发作频率的可预测性。研究了3个独立的癫痫发作日记数据库：SeizureTracker（n = 3016）、Human Epilepsy Project（n = 93）和NeuroVista（n = 15）。首先，评估癫痫发作频率的平均值和标准差之间的关系。利用这种关系，将对可能的癫痫发作频率范围的预测与临床试验中常用的传统预测方案进行比较。从SeizureTracker的单独数据导出中获得验证数据集，以进一步验证预测。结果：在数据集之间观察到一致的数学关系。平均发作次数的对数与标准差的对数呈高度线性相关（R-2 > 0.83）。这三个数据集显示出94%的对数-对数关系的高预测准确度，而传统预测方案的预测准确度为77%。独立验证集显示，双对数预测94%的正确范围，而RR 50预测77%。结论：可靠地预测癫痫发作频率变异性是直接的基础上的平均癫痫发作频率的知识，在几个数据集。随着研究的深入，这可能有助于提高RCT的效力，并指导临床实践。
Background: There is currently no formal method for predicting the range expected in an individual's seizure counts. Having access to such a prediction would be of benefit for developing more efficient clinical trials, but also for improving clinical care in the outpatient setting. Methods: Using three independently collected patient diary datasets, we explored the predictability of seizure frequency. Three independent seizure diary databases were explored: SeizureTracker (n = 3016), Human Epilepsy Project (n = 93), and NeuroVista (n = 15). First, the relationship between mean and standard deviation in seizure frequency was assessed. Using that relationship, a prediction for the range of possible seizure frequencies was compared with a traditional prediction scheme commonly used in clinical trials. A validation dataset was obtained from a separate data export of SeizureTracker to further verify the predictions. Results: A consistent mathematical relationship was observed across datasets. The logarithm of the average seizure count was linearly related to the logarithm of the standard deviation with a high correlation (R-2 > 0.83). The three datasets showed high predictive accuracy for this log-log relationship of 94%, compared with a predictive accuracy of 77% for a traditional prediction scheme. The independent validation set showed that the log-log predicted 94% of the correct ranges while the RR50 predicted 77%. Conclusion: Reliably predicting seizure frequency variability is straightforward based on knowledge of mean seizure frequency, across several datasets. With further study, this may help to increase the power of RCTs, and guide clinical practice.