Thursday, November 8, 2018

A prediction model to determine childhood epilepsy

van Diessen E, Lamberink HJ, Otte WM, Doornebal N, Brouwer OF, Jansen FE, Braun KPJ. A Prediction Model to Determine Childhood Epilepsy After 1 or More Paroxysmal Events. Pediatrics. 2018 Nov 2. pii: e20180931. doi:10.1542/peds.2018-0931. [Epub ahead of print]


The clinical profile of children who had possible seizures is heterogeneous, and accuracy of diagnostic testing is limited. We aimed to develop and validate a prediction model that determines the risk of childhood epilepsy by combining available information at first consultation.

We retrospectively collected data of 451 children who visited our outpatient department for diagnostic workup related to 1 or more paroxysmal event(s). At least 1 year of follow-up was available for all children who were diagnosed with epilepsy or in whom diagnosis remained inconclusive. Clinical characteristics (sex, age of first seizure, event description, medical history) and EEG report were used as predictor variables for building a multivariate logistic regression model. Performance was validated in an external cohort (n = 187).

Model discrimination was excellent, with an area under the receiver operating characteristic curve of 0.86 (95% confidence interval [CI]; 0.80-0.92), a positive predictive value of 0.93 (95% CI 0.83-0.97) and a negative predictive value of 0.76 (95% CI 0.70-0.80). Model discrimination in a selective subpopulation of children with uncertain diagnosis after initial clinical workup was good, with an area under the receiver operating characteristic curve of 0.73 (95% CI 0.58-0.87).

This model may prove to be valuable because predictor variables together with a first interictal EEG can be available at first consultation. A Web application is provided ( to facilitate the diagnostic process for clinicians who are confronted with children with paroxysmal events, suspected of having an epileptic origin.

From the article 

Age at consultation and at first event were truncated at month level. Medical history of a child was categorized into 3 predictor variables, namely “neurologic history” in the presence of a history of perinatal asphyxia, congenital or acquired brain lesions, head trauma, central nervous system infections, or migraine; “psychiatric history” in the presence of a diagnoses of autism, attention-deficit/hyperactivity disorder or other psychiatric disorders; and “known metabolic or genetic syndrome.” Relevant family history was categorized into the presence or absence of epilepsy, febrile seizures, migraine, and seizures in first- or second-degree relatives; or as “other” when consanguinity of parents, cerebrovascular accidents, or developmental delay was present in first- or second-degree relatives. Psychomotor development was categorized as normal or delayed on the basis of IQ (IQ of ≤70 was considered as intellectual disability) and type of schooling. Categorization was not mutually exclusive in these clinical categorical variables. EEG registrations were evaluated by 2 experienced neurophysiologists, and EEG reports were categorized as the presence (or absence) of focal epileptiform abnormalities if (multi)focal spikes or spike-wave-complexes were reported, generalized epileptiform abnormalities when generalized spikes or spike-wave complexes were reported, and aspecific nonepileptiform abnormalities if no clear epileptiform activity was stated but focal or generalized slowing or other aspecific abnormalities were reported.

Clinical symptoms extracted from the event descriptions provided by caregivers and children were categorized into multiple predictor variables, namely the presence or absence of (1) staring or nonattendance, (2) bilateral jerking or shaking, (3) stiffening or tonic posturing, (4) lateralizing motor symptoms, (5) weakness or loss of muscle tone, (6) sensory signs (such as a sensation of tingling and visual or auditory symptoms), (7) automatism, or (8) autonomic symptoms. Standard seizure classification, as recently proposed by the International League Against Epilepsy (ILAE),21 could not be applied to categorize event description because the majority of the events were eventually nonepileptic in nature….

Early and accurate diagnosis (or exclusion) of epilepsy after a suspected event will diminish patients’ ignorance but also wrongly applied stigmata, will improve self-management, and prevent unnecessary antiepileptic drug treatment and the burden of noncontributing ancillary investigations in children who are wrongly diagnosed. Our model provides a rational approach to assist clinicians during the diagnostic process by combining routinely available clinical information in a multivariate way. More specifically, we expect our model to be useful as an “independent” screening tool to assess the likelihood of a possible seizure to be epileptic in origin and to help the clinician decide on the need for ancillary investigations or refer to an epileptologist.

We consciously do not propose a single cutoff value for clinical decision-making because this is a decision rather than a diagnostic model. Our prediction tool can help the clinician to decide whether ancillary investigations or referral to an epileptologist are necessary, which is especially preferable for children where the risk is neither high nor low. Additionally, high-risk cases are identified quickly, and appropriate actions can be taken early in the process. Therefore, the merit of the model lies mainly with the general (pediatric) neurologist or pediatrician with access to EEG. In addition, it could be of use for the epileptologist to add in the prognosis of clinically more complicated cases in which diagnosis remains unclear after first consultation.

Predictor variables obtained from the EEG report strongly contributed to the discriminative power of the full multivariate model, which reflects daily practice. History taking is without doubt an important diagnostic instrument because it provides a coherent sequence of all the information provided. However, subjective reporting might obscure its value for clinical decision-making. The submodel based on EEG predictors alone showed a superior performance as compared with the submodel with only clinical predictor variables. Nevertheless, after assessing the most important aspect, whether the event is epileptic in origin, a treating physician aims to classify the specific seizure type and epilepsy syndrome to initiate adequate treatment and predict prognosis. Here, clinical information is indispensable, and a standard EEG recording alone will be insufficient. Conversely, EEG predictors showed an inferior performance compared with clinical predictors when tested on children in whom diagnosis remained uncertain after first consultation. Again, this reflects the actual clinical situation in which incongruent EEG findings might obfuscate clinical diagnosis.

Courtesy of Neurologist Connect

No comments:

Post a Comment