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 - 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 - Koeleman, Bobby P.C.
AU - Otte, Willem M.
AU - Braun, Kees P.J.
N1 - Funding Information:
We are grateful to the MING fonds for supporting this project, a generous donation by parents of children with epilepsy, which provided funding for doctoral studies of RS. We would like to thank Dr. Giovanni Falcicchio (Department of Basic Medical Sciences, Neurosciences and Sense Organs, University Hospital of Bari "A. Moro", Bari, Italy) and Rachel Wales BSc (University of Glasgow) for help with data collection. The European Union Seventh Framework Programme (FP7/2007-2013) supported this work under grant agreement n 279062 , as part of the EpiPGX project. The NIH supported this work through grant NIH K23 NS052468 .
Publisher Copyright:
© 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.
KW - Drug resistance
KW - Individual participant data
KW - JME
KW - Juvenile myoclonic epilepsy
KW - Medication withdrawal
KW - Meta-analysis
KW - Multivariable prediction
KW - Prediction model
KW - Refractory epilepsy
KW - Remission
KW - Seizure recurrence
UR - http://www.scopus.com/inward/record.url?scp=85142818169&partnerID=8YFLogxK
U2 - 10.1016/j.eclinm.2022.101732
DO - 10.1016/j.eclinm.2022.101732
M3 - Article
AN - SCOPUS:85142818169
SN - 2589-5370
VL - 53
JO - EClinicalMedicine
JF - EClinicalMedicine
M1 - 101732
ER -