Misdiagnosis in JME: Still a problem after 17 years?

Atakli, Dilek; Şenadım, Songül; Baslo, Sezin Alpaydın; Güveli, Betül Tekin; Daryan, Metin Dedei; Sarı, Hüseyin

doi:10.1016/j.seizure.2016.01.015

Cited by 22 publications

(30 citation statements)

References 25 publications

(35 reference statements)

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“…A remarkably high percentage of our subjects were previously undiagnosed. This is most probably because patients do not mention MJs spontaneously, and in a busy clinical setting, clinicians forget to ask specifically, as highlighted by several authors . More surprisingly, 17% of the subjects previously diagnosed with JME did not fulfill the diagnostic criteria .…”

Section: Discussionmentioning

confidence: 98%

“…In summary, of the older studies reporting prevalence of JME, three were from a time when the diagnosis was little known, and all but one included age groups probably containing large fractions of undiagnosed patients. However, the study including only children probably missed several subjects as well, as their upper age limit for inclusion was 12 years, and mean age at onset of JME is 13–16 years . None of these studies asked subjects with generalized epilepsy specifically about MJs.…”

Section: Discussionmentioning

confidence: 99%

“…Another plausible reason for reporting of a rather low prevalence of JME in previous studies is that people fail to report MJs spontaneously, as they tend to think of it as an unproblematic or a normal phenomenon. 13,14 In the previous studies reporting prevalence of JME, there were large groups of unspecified generalized epilepsy (Table 1). By contacting the subjects in this group, we found that nearly one third had MJs, and >20% fulfilled the diagnostic criteria of JME.…”

Section: Discussionmentioning

confidence: 99%

“…There is reason to believe that the prevalence of JME in these studies is underestimated, as patients tend to not mention their myoclonic jerks (MJs) spontaneously. In addition, many clinicians, even among neurologists, still seem to be unaware of this important clue . Furthermore, JME was not well known prior to the 1980s, and consequently a substantial amount of patients in the older age groups may be undiagnosed.…”

Section: Studies Reporting Prevalence Of Jmementioning

confidence: 99%

“…In addition, many clinicians, even among neurologists, still seem to be unaware of this important clue. 13,14 Furthermore, JME was not well known prior to the 1980s, 15 and consequently a substantial amount of patients in the older age groups may be undiagnosed. In fact, in most of the studies listed in Table 1, there were large groups of subjects with primarily GTCSs without a more specific diagnosis.…”

mentioning

confidence: 99%

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Prevalence of juvenile myoclonic epilepsy in people <30 years of age—A population‐based study in Norway

et al. 2016

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A substantial portion of people with JME seem to go undiagnosed, as was the case for more than one third of the subjects in this study. By investigating subjects diagnosed with unspecified genetic generalized epilepsy or absence epilepsy, we found a prevalence of JME that was considerably higher than previously reported. We conclude that JME may go undiagnosed due to the underrecognition of myoclonic jerks. To make a correct diagnosis, clinicians need to ask specifically about myoclonic jerks.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Studies Reporting Prevalence Of Jmementioning

confidence: 99%

mentioning

confidence: 99%

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Prevalence of juvenile myoclonic epilepsy in people <30 years of age—A population‐based study in Norway

et al. 2016

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Epilepsy classification using artificial intelligence: A web‐based application

et al. 2023

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ObjectiveThe purpose of the current endeavor was to evaluate the feasibility of using easily accessible and applicable clinical information (based on history taking and physical examination) in order to make a reliable differentiation between idiopathic generalized epilepsy (IGE) vs. focal epilepsy using machine learning (ML) methods.MethodsThe first phase of the study was a retrospective study of a prospectively developed and maintained database. All patients with an electro‐clinical diagnosis of IGE or focal epilepsy, at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2022, were included. The first author selected a set of clinical features. Using the stratified random portioning method, the dataset was divided into the train (70%) and test (30%) subsets. Different types of classifiers were assessed and the final classification was made based on their best results using the stacking method.ResultsA total number of 1445 patients were studied; 964 with focal epilepsy and 481 with IGE. The stacking classifier led to better results than the base classifiers in general. This algorithm has the following characteristics: precision: 0.81, sensitivity: 0.81, and specificity: 0.77.SignificanceWe developed a pragmatic algorithm aimed at facilitating epilepsy classification for individuals whose epilepsy begins at age 10 years and older. Also, in order to enable and facilitate future external validation studies by other peers and professionals, the developed and trained ML model was implemented and published via an online web‐based application that is freely available at http://www.epiclass.ir/f-ige.

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