Meta-analysis of time to seizure relapse after a post-operative epilepsy surgery.
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Date
2020
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Abstract
Epilepsy is a common disease world-wide whose suggested treatment vary from
surgical to non-surgical. Although surgical treatments may be commonly recommended
and successful in pharmacoresistant epilepsy for patients with refractory
epilepsy, there are some patients who experience a seizure relapse. It is of interest to
medical practitioners to determine how long patients survive epilepsy after a successful
surgery. Survival analysis methods have been used to model time-to-event
data. Hence we attempt to determine the time to a seizure relapse in epilepsy patients
after a surgery. However, single study have some limitations, such as lack of
accommodation of spatial factors, different research approaches to mention a few.
Most of the time, single studies are under-powered to detect the factors of covariates.
Meta-analysis methods have been developed to overcome this problem, where
a number of studies are amalgamated and a common conclusion is drawn. This thesis
aims is to determine the long-term seizure outcome after an epilepsy surgery of
refractory epilepsy without focusing on the types of refractory but rather in resective
surgery. In the current study a systematic review was done using Google Scholar,
Medline, and PubMed. The event of interest is seizure relapse and our interest is to
pool the time to first seizure relapse after surgical treatment. To measure the seizure
freedom the clinical method call Engel class I was used. The univariate and metasurvival
of fixed and random effect model were used to measure the proportion of
seizure freedom. Our focus was only in single arm treatment (surgical treatment) .
There were a total of 18 studies that satisfy the inclusion criteria with observations
at 6 time points measured in months after post-operative (6, 12, 24, 36, 60 and 120
months). In the univariate analysis, the probabilities of seizure freedom of the fixed
effects models were systematically larger than the random effect results. There was
evidence of significance of heterogeneity between studies, and the true variation
between studies test was large. The result that we got in univariate random effect
model were for time-points 6, 12, 24, 36, 60 and 120 months were 0.74 95% confidence
interval (CI)(0.66- 0.82), 0.69 95% CI (0.61- 0.77), 0.64 95% CI (0.56- 0.71), 0.60 95% CI
(0.52- 0.68), 0.56 95% CI (0.48- 0.63) and 0.47 95% CI (0.38- 0.56) respectively. The
meta-survival analysis also systematically showed that, the seizure free probability
were larger in a fixed effects model than in a random effects model. The summary
survival estimates of the random effect model that were pooled in the following time
points 6, 12, 24, 36, 60 and 120 were 0.7655 95% CI (0.6808- 0.8613), 0.7140 95% CI
(0.6246- 0.8163), 0.6462 95% (0.5614, 0.7438), 0.6105 95% (0.5225, 0.7133), 0.5700 95%
(0.4892, 0.6641) and 0.4755 (0.4078, 0.5545) respectively. The median time to relapse
was found using the meta-survival analysis in random effects model to be 104.46
months (8.87 years). We can conclude that the meta-survival analysis may be the
method to pool the time-to-event data in one-arm treatment..
Description
Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.