Abstracts

Rationale: People with epilepsy have variable and dynamic trajectories in response to antiseizure medications. Accurately modeling long-term treatment response will aid prognostication at the individual level and health resource planning at the societal level. Unfortunately, robust models are lacking. We aimed to develop a Markov model to predict the probability of future seizure-freedom based on the current seizure state and number of antiseizure medication regimens trialed.

Methods: We included people with newly diagnosed epilepsy who attended a specialist clinic in Glasgow, Scotland, between July 1982 and October 2012. They were followed up until October 2014 or death. We developed a simple Markov model, based on the current seizure state only (Fig. 1), and a more detailed model, based on both current seizure state and number of antiseizure medication regimens trialed (not shown).

Figure 1 legend: The simple model uses three health states: seizure-free, not seizure-free, and dead. Allowed transitions are represented by arrows, and transition probabilities p from state i to state j are denoted p_i,j.

Results: Our models suggested that once seizure-freedom was achieved, it was likely to persist, regardless of the number of antiseizure medications trialed to reach that point. The likelihood of achieving long-term seizure-freedom was highest with the first antiseizure medication regimen, at approximately 50%. The chance of achieving seizure-freedom fell with subsequent regimens. Fluctuations between seizure-free and not seizure-free states were highest earlier on, but decreased with chronicity of epilepsy. Seizure-freedom/seizure recurrence risk tables were constructed with these probability data, similar to cardiovascular risk tables (Fig. 2).

Figure 2 legend: An example of five-year transition probabilities (from 0 to 1) characterizing seizure control, using the simple model. Rows correspond to the initial states and columns correspond to the next or subsequent states. States in the simple model include ‘not seizure-free’ (‘Sz’), ‘seizure-free’ (‘SF’), and dead (‘D’).

Conclusions: Quantitative models, as used in this study, can provide pertinent clinical insights that are not apparent from simple statistical analysis alone. Attaining seizure freedom at any time in a patient’s epilepsy journey will confer durable benefit. Seizure-freedom risk tables may be used to individualize the prediction of future seizure control trajectory.

Funding: Please list any funding that was received in support of this abstract.: No specific funding was received towards this work.