The question of how to determine what constitutes an adequate period without seizures before a patient can be presumed seizure free after an intervention is frequently encountered in clinical practice and has important implications not only for patient management (e.g. when should patients feel secure returning to high-risk work environments?), but also potentially for public policy (e.g. when can patients safely be allowed to drive?). Current laws require a patient to be seizure free anywhere from three months to one year before they can legally drive, irrespective of the patient’s underlying seizure rate. Specifying an arbitrary duration of seizure freedom without considering the variation in seizure rates or a priori
probability of an intervention’s success rate may require some patients to wait longer than necessary before driving and allow others to return to driving before it is safe to do so. Many physicians counsel newly diagnosed patients with epilepsy based on data regarding a priori
probabilities of rates of seizure remission, e.g. the “rule” that, a priori
, approximately 50-65% of newly diagnosed, medication-naive adult epilepsy patients become seizure free upon starting medication while 35-50% of remain refractory. The corresponding probabilities for patients who have failed 1-2 previous medications are closer to 5-10% (Kwan & Brodie, 2000
); for surgery the probability of success reaches as high as 80% to much lower depending on identifiable risk factors (McIntosh et al, 2001
Beyond these figures, some guidance regarding how long to wait before identifying a patient as belonging to the “seizure free” outcome group comes from the recent recommendation of the ILAE task force, which proposes to declare that seizure freedom has been achieved in when there is “freedom from all types of seizures for 12 months or three times the pre-intervention inter-seizure interval, whichever is longer
” (Kwan et al., 2009
). This definition of seizure freedom represents a practical adaptation of rather than a rigorous consequence of the statistical considerations underlying Classical Rule of Three as formulated in the classic 1983 paper by Hanley and Lippman-Hand (Hanley & Lippman-Hand, 1983
; Jovanovic & Levy, 1997
), strict application of which may require waiting much longer than the recommended interval before patients can be reasonably declared “seizure free”.
Nevertheless, we have also seen how a rule in the spirit of that proposed by the ILAE task force can be placed on a solid theoretical foundation using straightforward considerations from probability theory. More specifically, we have shown how Bayes’ rule can be used to combine data regarding the a priori probability of responding to an intervention with patient-specific data regarding how long a patient has remained seizure free so far after an intervention to estimate the probability that the patient will remain seizure free thereafter. In its simplest form, explored in Model #1, this probabilistic framework suggests adopting a modification of the rule proposed by the ILAE task force, which we call the “Rule of Three-To-Six”: Future seizure freedom can be inferred after an observation period of three times the average pre-intervention interseizure interval so long as the pre-intervention probability of success exceeds 50%, whereas up to six times may be required when the pre-intervention probability of success drops as low as 5%.
As briefly explored in Model #2, by modeling the relevant a priori intervention response probabilities, the Bayesian approach used in this paper can also be adapted to guide determination of response to medication conceptualized in more complex ways (e.g. a “meaningful improvement”, such as >50% reduction of seizure frequency rather than a strictly complete cessation of seizures).
It should be borne in mind that the models explored here contain simplifying assumptions that may not be applicable in certain cases. First, our model treats seizure recurrences as random events. This is a reasonable way to model seizure occurrences where the underlying provocative factors are unknown (i.e. for seizures which seem to occur “out of the clear blue”), or unpredictable (e.g. minor illnesses or other unforeseeable stressors), but probably does not appropriately model systematic effects under a patient’s control, such as medication non-compliance or unfavorable lifestyle choices. Second, some patients with epilepsy exhibit either strongly non-random tendencies, as in epilepsy which varies catamenially. Such patterns violate the basic assumption of our analysis that seizure activity follows a constant-rate (Poisson) random process model. Third, between the random and strictly predictable cases lie cases in which seizures show a greater or lesser degree of clustering. In cases where prominent temporal clustering occurs, our analysis may still be reasonably applied if all seizures within a distinct cluster are considered as a single “event”. In cases of weaker clustering, where the boundaries between clusters are indistinct, the basic analysis may still be reasonably applied, although with minor modifications. More specifically, intermediate levels of clustering lead to modest increases in the period of observation required by the Rule of Three-To-Six (see Supplemental Material
). Fourth, the appropriate a priori
probability of seizure freedom, an essential ingredient in the Rule of Three-To-Six, can be only loosely approximated from the current literature surrounding anti-epileptic drug efficacy, which often treats response to anti-seizure interventions as an “all or none” phenomenon. The development of richer, more patient-specific methods for predicting response to epilepsy interventions thus remains an area where further research is needed.
These limitations notwithstanding, to the extent that – as pointed out by the ILAE task force in quoting Voltaire – “the perfect is the enemy of the good”, the Rule of Three-To-Six proposed herein provides reasonable practical guidance for evaluating seizure freedom in response to pharmacological, surgical, and other interventions.