TY - JOUR
T1 - Individualised prediction of drug resistance and seizure recurrence after medication withdrawal in people with juvenile myoclonic epilepsy
T2 - A systematic review and individual participant data meta-analysis
AU - Stevelink, Remi
AU - Al-Toma, Dania
AU - Jansen, Floor E
AU - Lamberink, Herm J
AU - Asadi-Pooya, Ali A
AU - Farazdaghi, Mohsen
AU - Cação, Gonçalo
AU - Jayalakshmi, Sita
AU - Patil, Anuja
AU - Özkara, Çiğdem
AU - Aydın, Şenay
AU - Gesche, Joanna
AU - Beier, Christoph P
AU - Stephen, Linda J
AU - Brodie, Martin J
AU - Unnithan, Gopeekrishnan
AU - Radhakrishnan, Ashalatha
AU - Höfler, Julia
AU - Trinka, Eugen
AU - Krause, Roland
AU - EpiPGX Consortium
AU - Irelli, Emanuele Cerulli
AU - Di Bonaventura, Carlo
AU - Szaflarski, Jerzy P
AU - Hernández-Vanegas, Laura E
AU - Moya-Alfaro, Monica L
AU - Zhang, Yingying
AU - Zhou, Dong
AU - Pietrafusa, Nicola
AU - Specchio, Nicola
AU - Japaridze, Giorgi
AU - Beniczky, Sándor
AU - Janmohamed, Mubeen
AU - Kwan, Patrick
AU - Syvertsen, Marte
AU - Selmer, Kaja K
AU - Vorderwülbecke, Bernd J
AU - Holtkamp, Martin
AU - Viswanathan, Lakshminarayanapuram G
AU - Sinha, Sanjib
AU - Baykan, Betül
AU - Altindag, Ebru
AU - von Podewils, Felix
AU - Schulz, Juliane
AU - Seneviratne, Udaya
AU - Viloria-Alebesque, Alejandro
AU - Karakis, Ioannis
AU - D'Souza, Wendyl J
AU - Sander, Josemir W
AU - Koeleman, Bobby P C
AU - Otte, Willem M
AU - Braun, Kees P. J.
N1 - © 2022 The Author(s).
PY - 2022/11
Y1 - 2022/11
N2 - BACKGROUND: A third of people with juvenile myoclonic epilepsy (JME) are drug-resistant. Three-quarters have a seizure relapse when attempting to withdraw anti-seizure medication (ASM) after achieving seizure-freedom. It is currently impossible to predict who is likely to become drug-resistant and safely withdraw treatment. We aimed to identify predictors of drug resistance and seizure recurrence to allow for individualised prediction of treatment outcomes in people with JME.METHODS: We performed an individual participant data (IPD) meta-analysis based on a systematic search in EMBASE and PubMed - last updated on March 11, 2021 - including prospective and retrospective observational studies reporting on treatment outcomes of people diagnosed with JME and available seizure outcome data after a minimum one-year follow-up. We invited authors to share standardised IPD to identify predictors of drug resistance using multivariable logistic regression. We excluded pseudo-resistant individuals. A subset who attempted to withdraw ASM was included in a multivariable proportional hazards analysis on seizure recurrence after ASM withdrawal. The study was registered at the Open Science Framework (OSF; https://osf.io/b9zjc/).FINDINGS: Our search yielded 1641 articles; 53 were eligible, of which the authors of 24 studies agreed to collaborate by sharing IPD. Using data from 2518 people with JME, we found nine independent predictors of drug resistance: three seizure types, psychiatric comorbidities, catamenial epilepsy, epileptiform focality, ethnicity, history of CAE, family history of epilepsy, status epilepticus, and febrile seizures. Internal-external cross-validation of our multivariable model showed an area under the receiver operating characteristic curve of 0·70 (95%CI 0·68-0·72). Recurrence of seizures after ASM withdrawal (n = 368) was predicted by an earlier age at the start of withdrawal, shorter seizure-free interval and more currently used ASMs, resulting in an average internal-external cross-validation concordance-statistic of 0·70 (95%CI 0·68-0·73).INTERPRETATION: We were able to predict and validate clinically relevant personalised treatment outcomes for people with JME. Individualised predictions are accessible as nomograms and web-based tools.FUNDING: MING fonds.
AB - BACKGROUND: A third of people with juvenile myoclonic epilepsy (JME) are drug-resistant. Three-quarters have a seizure relapse when attempting to withdraw anti-seizure medication (ASM) after achieving seizure-freedom. It is currently impossible to predict who is likely to become drug-resistant and safely withdraw treatment. We aimed to identify predictors of drug resistance and seizure recurrence to allow for individualised prediction of treatment outcomes in people with JME.METHODS: We performed an individual participant data (IPD) meta-analysis based on a systematic search in EMBASE and PubMed - last updated on March 11, 2021 - including prospective and retrospective observational studies reporting on treatment outcomes of people diagnosed with JME and available seizure outcome data after a minimum one-year follow-up. We invited authors to share standardised IPD to identify predictors of drug resistance using multivariable logistic regression. We excluded pseudo-resistant individuals. A subset who attempted to withdraw ASM was included in a multivariable proportional hazards analysis on seizure recurrence after ASM withdrawal. The study was registered at the Open Science Framework (OSF; https://osf.io/b9zjc/).FINDINGS: Our search yielded 1641 articles; 53 were eligible, of which the authors of 24 studies agreed to collaborate by sharing IPD. Using data from 2518 people with JME, we found nine independent predictors of drug resistance: three seizure types, psychiatric comorbidities, catamenial epilepsy, epileptiform focality, ethnicity, history of CAE, family history of epilepsy, status epilepticus, and febrile seizures. Internal-external cross-validation of our multivariable model showed an area under the receiver operating characteristic curve of 0·70 (95%CI 0·68-0·72). Recurrence of seizures after ASM withdrawal (n = 368) was predicted by an earlier age at the start of withdrawal, shorter seizure-free interval and more currently used ASMs, resulting in an average internal-external cross-validation concordance-statistic of 0·70 (95%CI 0·68-0·73).INTERPRETATION: We were able to predict and validate clinically relevant personalised treatment outcomes for people with JME. Individualised predictions are accessible as nomograms and web-based tools.FUNDING: MING fonds.
U2 - 10.1016/j.eclinm.2022.101732
DO - 10.1016/j.eclinm.2022.101732
M3 - Review
C2 - 36467455
SN - 2589-5370
VL - 53
JO - EClinicalMedicine
JF - EClinicalMedicine
M1 - 101732
ER -