• Epilepsy;
  • Temporal lobe epilepsy;
  • Surgical treatment;
  • Prognosis


  1. Top of page
  2. Methods
  3. Results
  4. Discussion
  5. Acknowledgments
  6. References

Purpose: Although several independent predictors of seizure freedom after temporal lobe epilepsy surgery have been identified, their combined predictive value is largely unknown. Using a large database of operated patients, we assessed the combined predictive value of previously reported predictors included in a single multivariable model.

Methods: The database comprised a cohort of 484 patientswho underwent temporal lobe surgery for drug-resistant epilepsy. Good outcome was defined as Engel class 1, one year after surgery. Previously reported independent predictors were tested in this cohort. To be included in our final prediction model, predictors had to show a multivariable p-value of <0.20.

Results: The final multivariable model included predictors obtained from the patient's history (absence of tonic–clonic seizures, absence of status epilepticus), magnetic resonance imaging [MRI; ipsilateral mesial temporal sclerosis (MTS), space occupying lesion], video electroencephalography (EEG; absence of ictal dystonic posturing, concordance between MRI and ictal EEG), and fluorodeoxyglucose positron emission tomography (FDG-PET; unilateral temporal abnormalities), that were related to seizure freedom in our data. The model showed an expected receiver-operating characteristic curve (ROC) area of 0.63 [95% confidence interval (CI) 0.57–0.68] for new patient populations. Intracranial monitoring and surgery-related parameters (including histology) were not important predictors of seizure freedom. Among patients with a high probability of seizure freedom, 85% were seizure-free one year after surgery; however, among patients with a high risk of not becoming seizure-free, still 40% were seizure-free one year after surgery.

Conclusion: We could only moderately predict seizure freedom after temporal lobe epilepsy surgery. It is particularly difficult to predict who will not become seizure-free after surgery.

Epilepsy surgery is an effective treatment for medically intractable epilepsy, especially in patients with temporal lobe epilepsy (TLE). After TLE surgery, 60% to 70% of patients become seizure-free, and 90% of patients achieve a worthwhile reduction in seizure severity (Engel et al., 1998, 2003). The presurgical work-up for epilepsy surgery is stepwise and complex, and contradictory findings from standard tests [history, seizure semiology, electroencephalography (EEG), and magnetic resonance imaging (MRI)] with regard to lateralization or localization of the seizure focus necessitate additional tests of increasing invasiveness and cost [e.g., ictal single photon emission computed tomography (SPECT), positron emission tomography (PET), intracranial EEG recordings]. To be able to inform candidates for TLE surgery about their chances of postoperative seizure freedom, it is important to define which characteristics are true predictors of seizure freedom after surgery. This requires a multivariable study approach (Harrell et al., 1996). The ultimate goal would be to develop a simple clinical prediction model or rule to predict the chance of seizure freedom after surgery for individual patients undergoing TLE surgery.

Previous studies of predictors of postoperative seizure freedom using multivariable analysis differ in their methodology and results (Armon et al., 1996; Berg et al., 1998; Radhakrishnan et al., 1998; Jeong et al., 1999, 2005; Hennessy et al., 2001a, 2001b; Clusmann et al., 2002, 2004; Tonini et al., 2004; Janszky et al., 2005, 2006; Spencer et al., 2005; Cohen-Gadol et al., 2006; Jeha et al., 2006; Kelemen et al., 2006; Yun et al., 2006; Malmgren et al., 2007). Although potential independent predictors have been identified, the predictive value of combinations of these independent predictors (i.e., the value of these predictors combined in a single prediction model) has been investigated in only one study, which included patients with all types of epilepsy and not only TLE (Armon et al., 1996). The aim of the present study was therefore to use a large homogeneous database of patients who underwent TLE surgery to quantify the predictive accuracy of the combination of previously reported predictors of seizure freedom.


  1. Top of page
  2. Methods
  3. Results
  4. Discussion
  5. Acknowledgments
  6. References


In The Netherlands, all patients referred for epilepsy surgery enter the Dutch Collaborative Epilepsy Surgery Program, a nationwide tertiary referral program, in which each referred patient undergoes the same stepwise presurgical work-up. Decisions are taken by a multidisciplinary team. The present retrospective prognostic cohort study included a consecutive cohort of 484 patients (in 16 years) who underwent temporal lobe resection.

Surgery consisted of temporal lobe resection, tailored by acute electrocorticography including amygdalohippocampectomy (77%) (Leijten et al., 2005), a standard resection (first two to three centimeters from the temporal pole) with amygdalohippocampectomy (17%), or a tailored lesionectomy without amygdalohippocampectomy (6%).

Prognostic predictors

We selected previously reported independent pre- and intraoperative predictors of seizure freedom after TLE surgery (see Table 1) (Armon et al., 1996; Berg et al., 1998; Radhakrishnan et al., 1998; Jeong et al., 1999, 2005; Hennessy et al., 2001a, 2001b; Clusmann et al., 2002, 2004; Tonini et al., 2004; Janszky et al., 2005, 2006; Spencer et al., 2005; Cohen-Gadol et al., 2006; Jeha et al., 2006; Kelemen et al., 2006; Yun et al., 2006; Malmgren et al., 2007). We also included four potential predictors suggested by the members of the Dutch Collaborative Epilepsy Surgery Program, namely, absence of atypical features for TLE in videotaped seizures, defined as a somatosensible aura or a tonic, hypermotoric or atonic seizure; posterior temporal ictal onset during EEG monitoring; (ipsilateral) delayed anterior temporal θ onset in ictal EEG as described by Risinger et al. (1989); and the side of surgery (left versus right). These factors either have not yet been investigated before or were not found predictive in previous studies (Sperling et al., 1994).

Table 1.  Potential predictors of postoperative seizure freedom investigated in our study
HistoryMRIVideo EEGAdditional testsSurgery
  1. aFebrile seizures defined as seizures in infancy and early childhood occuring during a sudden rise in temperature early in the course of an illness in the absence of intracranial infection or a defined etiology (Foldvary-Schaefer & Wyllie, 2007).

  2. bIncluding secondary generalized tonic–clonic seizures.

  3. cTotal IQ used as indicator for mental retardation (Clusmann et al., 2004) or need for special schooling (Hennessy et al., 2001a).

  4. dDefined as diagnosis cortical dysgenesis based on histological analysis of the resected tissue.

Female sex1Abnormal MRI2–6No ictal dystonic posturing7Unilateral temporal abnormalities on FDG-PET2Larger resection size (in cm)3
Febrile seizures3,aMTS ipsilateral to resection side1,3,5,8–11No bilateral interictal spikes6,12,13Intracranial monitoring performed3,6,12Postoperative discharges during acute electrocorticography3
Shorter epilepsy duration (in years)7,14Space occupying lesion ipsilateral to resection side3,4,15No extratemp interictal spikes16 MTS on histology1
Higher age at start epilepsy (in years)17Concordance of MRI and EEG results (both unilateral temporal)3Concordance of interictal and ictal EEG results (both unilateral temporal)11 No cortical dysgenesis on histology13,d
No tonic–clonic seizures5,7,9,16,b 
No status epilepticus4
Higher total IQ score14,17,18,18,c 
Younger age at surgery (in years)1,8,10 

Prognostic outcome

Outcome was classified according to the Engel classification, one year after surgery. The outcome was dichotomized as Engel class I (including all subcategories), which is absence of disabling seizures, versus Engel class II or higher (Engel et al., 1993).

Data collection

Predictors and outcome were retrieved for all 484 patients. Because each step of the presurgical work-up and the postsurgical follow-up is registered, we were able to build a research database in which all information on predictors and outcome was coded as described above. During encoding, κ analyses were performed between the two scoring researchers (S.U. and A.C.) and two independent experts (F.L. and J.A.) to ensure uniformity. Variables included in the study all had reasonable κ values of 0.70 or higher (Landis & Koch, 1977; Altman, 1991; Uijl et al., 2007).

Data analysis

We first assessed whether continuous variables or predictors (such as age) had a linear relationship with the outcome by using restricted cubic splines, or whether they required any transformation (e.g., log or square root) (Harrell, Jr et al., 1996). We then quantified which predictors do contribute to the prediction of postoperative seizure freedom by using multivariable logistic regression modeling. We then followed the chronological order in which these predictors are documented in clinical practice. We started with all potential predictors from patient history, MRI, and video EEG monitoring (overall model). Predictors were excluded from this overall model if the sign of the multivariable regression coefficient was not considered plausible compared to the performance in earlier studies, according to the sign OK method (Harrell et al., 1996; Steyerberg et al., 2000). The model was further reduced by backwards stepwise exclusion of the least contributory predictors (defined as a p-value higher than 0.20, based on the log likelihood ratio test) to determine which predictors truly contributed to the prediction of seizure freedom, and this resulted in reduced model 1. In prediction research, in contrast to etiologic research, it is common to use such lenient p-values (e.g., <0.20) (Steyerberg et al., 1999, 2001; Harrell, 2001).

We then assessed whether subsequent presurgical tests had any incremental or added value to model 1, using a forward approach. Model 2 thus extended model 1 with unilateral temporal abnormalities on fluorodeoxyglucose positron emission tomography (FDG-PET). Model 3 was model 2 plus additional results from intracranial monitoring, and model 4 was model 3 with the perioperative predictors.

The ability of each model to discriminate between postoperative seizure freedom or not was quantified using the area under the receiver-operating characteristic curve (ROC) area. Agreement (calibration) between the predicted and observed rates of seizure freedom was assessed with the Hosmer-Lemeshow statistic (high p-values indicating good calibration) and a calibration plot.

To prevent optimistic predictions in new patient populations, the internal validity of the prognostic models was studied with bootstrapping techniques (100 samples) (Harrell et al., 1996). The average difference in performance between the bootstrap samples and the original data gives an impression of the optimism of the model in new patients. Based on these bootstrap results, the ROC area and regression coefficients (odds ratios) of the predictors were corrected for optimism.

As some values were missing, and missing values usually do not occur at random, we imputed the missing values to prevent bias, using single imputation by linear regression with the addition of a random error term (Greenland & Finkle, 1995; Donders et al., 2006). FDG-PET was not performed in all patients, but was usually performed in patients with inconclusive results after MRI and video EEG monitoring. Imputation of FDG-PET results in patients in whom FDG-PET was actually not performed enabled us to assess the independent value of FDG-PET as described previously (Greenland & Finkle, 1995; Donders et al., 2006; Uijl et al., 2007).

Statistical analyses were performed with S-plus version 6.2 (Insightful Corporation, Seattle, WA, U.S.A.).


  1. Top of page
  2. Methods
  3. Results
  4. Discussion
  5. Acknowledgments
  6. References

Of the 484 patients, 356 patients [incidence 74%, 95% confidence interval (CI) 0.69–0.77] were seizure-free (Engel class I) one year after surgery.

Univariable associations between predictors and outcome are presented in Table 2. Based on restricted cubic splines, two continuous predictors, “age at time of surgery” and “duration of epilepsy,” were not linearly related to the outcome but had to be modeled as square root terms. The other continuous predictors were linearly related to the outcome and could be modeled as such.

Table 2.  Univariable associations of each potential predictor with Engel class 1 (yes/no) as outcome
Outcome: Engel class 1N 356 (74%)Odds ratio95% CIp-value
  1. N = number (percentage) of patients with positive test result, total N = 484; for continuous variables, the mean ± standard deviation is presented.

  2. a339 (70%) patients had either MTS or a space-occupying lesion.

Female sex248 (51%)0.870.58–1.300.48
Febrile seizures180 (37%)0.900.59–1.360.61
Epilepsy duration (in years)21.6 ± 11.40.980.97–1.000.05
Age at start epilepsy (in years)11.8 ± 9.3 0.990.98–1.020.87
No tonic–clonic seizures190 (39%)2.391.52–3.76<0.001 
No status epilepticus427 (88%)1.750.98–3.120.06
Total IQ (score)33.3 ± 11.30.990.99–1.010.74
Age at surgery (in years)103.0 ± 18.4 0.980.96–1.000.03
Left-sided surgery232 (48%)0.890.60–1.340.59
Abnormal MRI443 (92%)1.140.60–2.180.68
MTSa248 (51%)1.120.75–1.670.59
Space occupying lesion on MRIa102 (21%)1.500.88–2.550.13
Concordance MRI and EEG255 (53%)1.370.92–2.060.13
No ictal limb dystonia221 (46%)1.210.81–1.820.36
No extratemporal semiology408 (84%)0.910.52–1.610.76
No bilateral interictal spikes368 (76%)0.960.60–1.550.87
No extratemp interictal spikes439 (91%)1.010.51–2.030.97
No start ictal EEG posterior temporal462 (96%)1.050.40–2.730.93
Ipsilateral delayed rhythmical θ onset132 (27%)0.770.49–1.190.24
Concordance interictal and ictal EEG 93 (19%)0.850.52–1.410.53
FDG-PET unilateral temporal136 (28%)1.420.71–2.810.32
No intracranial monitoring414 (86%)0.990.52–1.930.47
Resection size (in cm)4.3 ±–1.170.96
Postoperative discharges249 (51%)1.180.79–1.770.43
MTS on histology269 (56%)0.810.54–1.220.31
No cortical dysgenesis on histology444 (92%)1.210.60–2.460.60

The overall model included all predictors from history, MRI, and video EEG monitoring. Ipsilateral delayed rhythmical θ onset in ictal EEG was excluded from the model based on the sign OK method. Backwards stepwise exclusion further reduced the starting model to six predictors of seizure freedom (model 1, Table 3): age at time of surgery, absence of tonic–clonic seizures or status epilepticus in the patient's history, presence of ipsilateral MTS or a space occupying lesion on the MRI, and absence of ictal dystonic posturing. None of the extra predictors proposed by the members of the Dutch Collaborative Epilepsy Surgery Program were of added predictive value to this reduced model.

Table 3.  Models including the important predictors of postoperative seizure freedom
 Model 1Model 2Model 3Model 4
Odds ratio (95% CI)p-valueOdds ratio (95% CI)p-valueOdds ratio (95% CI)p-valueOdds ratio (95% CI)p-value
  1. aIncluded as square root, see text.

  2. bMTS, mesiotemporal sclerosis.

  3. cna, not applicable.

  4. Model 1 includes predictors from history, MRI, and video EEG; model 2 includes model 1 plus FDG-PET result; model 3 includes model 2 plus intracranial monitoring; model 4 includes model 3 plus surgical predictors.

No tonic–clonic seizures2.24 (1.40–3.58) 0.0012.31 (1.44–3.70) 0.0012.32 (1.45–3.73)<0.01  2.32 (1.45–3.73)<0.01  
No status epilepticus1.62 (0.88–2.98)0.121.54 (0.83–2.84)0.171.52 (0.82–2.82)0.181.45 (0.78–2.71)0.24
Age at surgerya0.84 (0.68–1.04)0.100.83 (0.67–1.03)0.090.83 (0.67–1.03)0.090.84 (0.68–1.05)0.12
MTSb on MRI1.63 (1.03–2.58)0.041.61 (1.02–2.57)0.041.59 (0.99–2.55)0.061.80 (1.05–3.08)0.03
Space occupying lesion on MRI1.67 (0.92–3.02)0.091.68 (0.93–3.03)0.091.63 (0.89–2.99)0.111.57 (0.85–2.90)0.15
No ictal dystonic posturing1.34 (0.87–2.05)0.191.36 (0.89–2.10)0.161.35 (0.88–2.08)0.181.36 (0.87–2.12)0.17
FDG-PET unilateral temporalnac 1.47 (0.95–2.29)0.091.47 (0.95–2.29)0.091.50 (0.96–2.35)0.07
Intracranial monitoring performedna na 1.14 (0.62–2.07)0.681.14 (0.62–2.13)0.67
Resection sizena na na 1.02 (0.86–1.20)0.86
Postoperative dischargesna na na 1.15 (0.76–1.77)0.51
MTSb on histologyna na na 0.78 (0.46–1.34)0.37
No cortical dysgenesis on histologyna na na 1.17 (0.55–2.49)0.68

FDG-PET abnormalities were an additional predictor of seizure freedom [odds ratio (OR) = 1.47; 95% CI 0.95–2.29; p-value: 0.09] (model 2), whereas intracranial monitoring (OR = 1.14; 95% CI 0.62–2.07; p-value = 0.68) (model 3), and operative predictors (model 4) did not add any predictive information.

The Hosmer-Lemeshow test indicated good calibration, with a p-value of 0.79 for model 1, 0.35 for model 2, 0.47 for model 3, and 0.57 for model 4. This was confirmed by the calibration plots (not shown).

Model 2, based on predictors from the patient's history, and MRI, video EEG, and FDG-PET findings yielded the best prediction model, with an ROC area of 0.66 (95% CI 0.60–0.70). After correction for optimism, based on bootstrapping, this ROC area was reduced to an ROC area of 0.63 (95% CI 0.57–0.68), a value that can be expected if this model is used with other similar patient populations.

Table 4 shows the number of patients with and without seizure freedom after one year across the probability categories predicted by model 2. This table shows how many patients with a certain probability of becoming seizure-free actually did or did not become seizure-free one year after surgery. The observed incidence of seizure freedom (whether patients actually became seizure-free) increased from 40% in the group with the lowest probability according to our model to 85% in the highest probability group. The risk of not becoming seizure-free ranged from 15% in the group with the highest probability of seizure freedom to 60% in the lowest. This means that 40% of patients with the highest risk of not achieving seizure freedom were nevertheless seizure-free one year after surgery.

Table 4.  Number (%) of patients with or without seizure freedom after one year over the probability categories estimated by model 2a
Estimated probability based on model 2 in Table 3Seizure freedom N = 356No seizure freedom N = 128
  1. aSee Table 3; N = 484.

<0.45 (N = 5; 1% of 484) 2 (40%) 3 (60%)
0.45–0.60 (N = 52; 11%)31 (59%)21 (41%)
0.60–0.70 (N = 112; 23%)74 (66%)38 (33%)
0.70–0.80 (N = 161; 33%)118 (73%) 43 (26%)
>0.80 (N = 154; 32%)131 (85%) 23 (15%)


  1. Top of page
  2. Methods
  3. Results
  4. Discussion
  5. Acknowledgments
  6. References

We assessed all 22 predictors found in earlier multivariable studies on seizure freedom after TLE surgery (Armon et al., 1996; Berg et al., 1998; Radhakrishnan et al., 1998; Jeong et al., 1999, 2005; Hennessy et al., 2001a, 2001b; Clusmann et al., 2002, 2004; Tonini et al., 2004; Janszky et al., 2005, 2006; Spencer et al., 2005; Cohen-Gadol et al., 2006; Jeha et al., 2006; Kelemen et al., 2006; Yun et al., 2006; Malmgren et al., 2007) and identified seven predictors of postoperative seizure freedom; namely younger age at time of surgery, a history without tonic–clonic seizures, a history without status epilepticus, MRI with ipsilateral MTS, MRI with space occupying lesion, no dystonic posturing during the seizure, and unilateral temporal abnormalities on FDG-PET. The other predictors from the basic diagnostic work-up, additional diagnostic tests, and operative data did not contribute to the prediction of postoperative seizure freedom. Our final model included all predictors reported by Jeong et al. (2005), Spencer et al. (2005), and Janszky et al. (2006).

We reanalyzed the data on the hypothesis that patients with MTS or a space-occupying lesion might have a more similar predictive value, while patients with other abnormalities have a different (smaller) risk of becoming seizure-free. To this end, we took “MTS and/or space-occupying lesion” as one predictor and “other abnormalities on MRI, other than MTS and space-occupying lesion” as a separate predictor. This analysis did not yield different results: the combined predictor “MTS and/or space-occupying lesion” would be included in the final model, but this model yielded a lower ROC area than in our original analysis.

As model 4 shows, resection size was no additional predictor. As this predictor is only meaningful in patients who underwent a tailored resection, we performed a subgroup analysis in the 400 patients who underwent a tailored resection, which gave the same result.

Our study presents an overall predictive value, that is a measure of how the use of such a model would discriminate between postoperative seizure freedom or not. This overall predictive value of the combination of predictors was moderate, with an ROC area 0.63. This means that we were unable to formulate a simple and stable prediction rule to predict seizure freedom that could be used to inform patients. The model can be used to indicate risk categories for postoperative seizure freedom. However, it performs insufficiently to be used for individual patients to discriminate between becoming and not becoming seizure-free.

Of earlier studies, only the one by Armon et al. (1996) included a measure of the performance of their model in predicting postoperative seizure freedom. Armon et al. (1996) found a Somers' D coefficient of 0.47 or a ROC area of 0.74 without correction for optimism, on the basis of five preoperative predictors: ipsilateral imaging abnormality, ipsilateral EEG localization (ictal and interictal), intracranial EEG recordings, temporal lobe resection, and age (Armon et al., 1996). Since their study involved patients who had undergone temporal or extratemporal resections, their model is not directly comparable to ours. However, the predictors “ipsilateral imaging abnormality” and “age” were also included in our model.

Unfortunately, other studies predicting postoperative seizure freedom did not present the overall accuracy of their model (nor could this be reconstructed with the data provided). The wide variation of preoperative predictors reported in the literature and the moderate overall predictive value of our own model indicate that it is difficult to predict postoperative seizure freedom one year after TLE surgery. In prognostic medical research, as in all areas of life, prediction becomes more difficult the further ahead the outcomes we want to predict (Moons et al., 2002). This means that the presence or absence of particular predictors in an individual patient cannot directly be associated with an increased or decreased chance of becoming seizure-free after surgery.

To appreciate our results, some methodological aspects need to be discussed. First, the study outcome measure was Engel class I, one year after surgery. We reanalyzed the data with the outcome absolute seizure freedom (Engel class IA) one year after surgery, which led to the same results (i.e., the same independent predictors were identified). Secondly, we wanted to include ancillary tests, such as FDG-PET, which were not performed in all patients. FDG-PET was performed in 188 of 484 patients, mostly when MRI and video EEG monitoring results were inconclusive. Imputation of FDG-PET results in patients in whom FDG-PET was not performed, as described earlier, enabled us to assess the independent value of FDG-PET in the complete patient population (Uijl et al., 2007). We reached the same conclusion when we restricted our analysis to the subgroup of 188 patients in whom FDG-PET was performed. Thirdly, the predictors were necessarily reduced to essentials for categorization. Since the number of predictors that can be included in a prognostic model is limited, we only included previously reported predictors of seizure freedom. This obviously does not fully reflect the subtle nuances of interpretation that often arise in clinical practice, and the model cannot comprise all possible information; these complexities necessarily have been obscured.

In conclusion, whereas the results of many preoperative tests in TLE surgery have a statistically significant association with postoperative seizure freedom, in combination they are only moderate predictors of postoperative seizure freedom. It is particularly difficult to predict the absence of postoperative seizure freedom. Therefore, currently available data do not yet allow the development of a robust prediction rule for postoperative seizure freedom. More refined (software) analysis of existing tests, new diagnostic tests such as EEG-functional MRI (EEG-fMRI), and even genetic analysis, may provide future opportunities to improve the prediction of postoperative seizure freedom.


  1. Top of page
  2. Methods
  3. Results
  4. Discussion
  5. Acknowledgments
  6. References

The authors would like to thank Anja J. Couperus, EEG technician, for her help in coding the data from video-EEG monitoring into our research database. This study was made possible by grants from the National Board of Academic Hospitals and the College of Health Insurers (VAZ/CVZ grant 001134) and the Dutch National Epilepsy Foundation (NEF grant 04–05).

Conflict of interest: We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. The authors declare no conflicts of interest.


  1. Top of page
  2. Methods
  3. Results
  4. Discussion
  5. Acknowledgments
  6. References
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