Individualised prediction of drug resistance and seizure recurrence after medication withdrawal in people with juvenile myoclonic epilepsy: A systematic review and individual participant data meta-analysis

Summary 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  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

Stents Revestidos com Fármacos Versus Stents Metálicos nos Doentes com Enfarte Agudo do Miocárdio com Supradesnivelamento de ST.

MedicinaMedicin

Atraso no diagnóstico de esclerose múltipla numa população portuguesa

Introduction: Multiple sclerosis is a chronic inflammatory disease, in which a diagnostic delay could reduce the available therapeutic options. Therefore, it is important to monitor the time to diagnosis and understand factors that may potentially reduce it. The objective of this study was to determine the time between the first symptoms and the diagnosis of multiple sclerosis and which factors may contribute to a diagnostic delay. Material and methods: Cross-sectional multicenter study, with retrospective data analysis, conducted in five tertiary Portuguese hospitals. Patients were consecutively selected from each local multiple sclerosis patients´ database. Sociodemographic and initial clinical data were collected through a questionnaire. Date of final diagnosis and multiple sclerosis classification was obtained from clinical files. Results: A total of 285 patients were included with mean age at diagnosis of 36 years. The median time between first clinical manifestation and multiple sclerosis diagnosis was nine months (IQR 2 - 38). Diagnostic delay was associated with an older age (p < 0.001; r = 0.35), motor deficit at onset [26.5 months (IQR 4.5 - 56.5); p = 0.0005], higher number of relapses before diagnosis (p < 0.001; r = 0,626), first observation by other medical specialty [11 months (IQR 2 - 48); p < 0.001], prior alternative diagnosis [20 months (IQR 4 - 67.5); p < 0.001] and primary progressive subtype [37 months (IQR 25 - 64.5); p < 0.001]. The most significant delay occurred between the initial symptom and neurological observation. Discussion: A significant delay occurred between initial symptoms and the diagnosis of multiple sclerosis, reflecting the need toincrease awareness of this entity and its diverse symptom presentation.Introdução: A esclerose múltipla é uma doença inflamatória crónica na qual um atraso no diagnóstico poderá reduzir as opções terapêuticas, sendo importante monitorizar o tempo até ao diagnóstico e compreender os fatores que potencialmente o reduzam. Foi objetivo deste estudo determinar o tempo entre os primeiros sintomas e o diagnóstico de esclerose múltipla e quais os fatores que podem contribuir para o atraso no diagnóstico. Material e Métodos: Estudo multicêntrico transversal retrospetivo, realizado em cinco hospitais portugueses. Os doentes foram selecionados, consecutivamente, a partir de bases de dados locais. Os dados sociodemográficos e clínicos iniciais foram adquiridos através de questionário individual. A data do diagnóstico final e a classificação da esclerose múltipla foram obtidas por consulta do processo clínico. Resultados: Foram incluídos 285 doentes com média de idade ao diagnóstico de 36 anos. A mediana do tempo entre a primeira manifestação clínica e o diagnóstico foi de nove meses (IQR 2 - 38). O atraso no diagnóstico foi associado a idade avançada (p < 0,001; r = 0,35), défice motor inicial [26,5 meses (IQR 4,5 - 56,5), p = 0,0005], maior número de surtos previamente ao diagnóstico (p < 0,001; r = 0,626), primeira observação por outra especialidade médica [11 meses (IQR 2 - 48); p < 0,001], diagnóstico prévio alternativo [20 meses (IQR 4 - 67,5); p < 0,001] e esclerose múltipla primária progressiva [37 meses (IQR 25 - 64,5), p < 0,001]. O atraso mais significativo ocorreu entre o primeiro sintoma e a observação por neurologista. Discussão: Ocorreu um atraso significativo entre o primeiro sintoma e o diagnóstico de esclerose múltipla, refletindo uma necessidade de maior acuidade na identificação dos seus principais sintomas.This research had a financial support of Biogen Idec,Inc

Individualised prediction of drug resistance and seizure recurrence after medication withdrawal in people with juvenile myoclonic epilepsy: A systematic review and individual participant data meta-analysis

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

Clinical and genetic characteristics of late-onset Huntington's disease

Background: The frequency of late-onset Huntington's disease (&gt;59 years) is assumed to be low and the clinical course milder. However, previous literature on late-onset disease is scarce and inconclusive. Objective: Our aim is to study clinical characteristics of late-onset compared to common-onset HD patients in a large cohort of HD patients from the Registry database. Methods: Participants with late- and common-onset (30–50 years)were compared for first clinical symptoms, disease progression, CAG repeat size and family history. Participants with a missing CAG repeat size, a repeat size of ≤35 or a UHDRS motor score of ≤5 were excluded. Results: Of 6007 eligible participants, 687 had late-onset (11.4%) and 3216 (53.5%) common-onset HD. Late-onset (n = 577) had significantly more gait and balance problems as first symptom compared to common-onset (n = 2408) (P &lt;.001). Overall motor and cognitive performance (P &lt;.001) were worse, however only disease motor progression was slower (coefficient, −0.58; SE 0.16; P &lt;.001) compared to the common-onset group. Repeat size was significantly lower in the late-onset (n = 40.8; SD 1.6) compared to common-onset (n = 44.4; SD 2.8) (P &lt;.001). Fewer late-onset patients (n = 451) had a positive family history compared to common-onset (n = 2940) (P &lt;.001). Conclusions: Late-onset patients present more frequently with gait and balance problems as first symptom, and disease progression is not milder compared to common-onset HD patients apart from motor progression. The family history is likely to be negative, which might make diagnosing HD more difficult in this population. However, the balance and gait problems might be helpful in diagnosing HD in elderly patients