Predictors of relapse risk and treatment response in AQP4-IgG positive and seronegative NMOSD : a multicentre study

Background Neuromyelitis optica spectrum disorder (NMOSD) can be categorised into aquaporin-4 antibody (AQP4-IgG) NMOSD or seronegative NMOSD. While our knowledge of AQP4-IgG NMOSD has evolved significantly in the past decade, seronegative NMOSD remains less understood. This study aimed to evaluate the predictors of relapses and treatment responses in AQP4-IgG NMOSD and seronegative NMOSD. Methods This was a multicentre, international, retrospective cohort study using the MSBase registry. Recurrent relapse risk was assessed using an Andersen-Gill model and risk of first relapse was evaluated using a Cox proportional hazards model. Covariates that putatively influence relapse risk included demographic factors, clinical characteristics and immunosuppressive therapies; the latter was assessed as a time-varying covariate. Results A total of 398 patients (246 AQP4-IgG NMOSD and 152 seronegative NMOSD) were included. The AQP4-IgG NMOSD and seronegative NMOSD patients did not significantly differ by age at disease onset, ethnicity or annualised relapse rate. Both low-efficacy and high-efficacy immunosuppressive therapies were associated with significant reductions in recurrent relapse risk, with notably greater protection conferred by high-efficacy therapies in both AQP4-IgG NMOSD (HR 0.27, 95% CI 0.15 to 0.49, p<0.001) and seronegative NMOSD (HR 0.21, 95% CI 0.08 to 0.51, p<0.001). Longer disease duration (HR 0.97, 95% CI 0.95 to 0.99, p<0.001) and male sex (HR 0.52, 95% CI 0.34 to 0.84, p=0.007) were additional protective variables in reducing the recurrent relapse risk for the AQP4-IgG NMOSD group. Conclusion Although further studies are needed to improve our understanding of seronegative NMOSD, our findings underscore the importance of aggressive treatment with high-efficacy immunotherapies in both NMOSD subtypes, regardless of serostatus

Predictive models for starting antiseizure medication withdrawal following epilepsy surgery in adults

More than half of adults with epilepsy undergoing resective epilepsy surgery achieve long-term seizure freedom and might consider withdrawing antiseizure medications. We aimed to identify predictors of seizure recurrence after starting postoperative antiseizure medication withdrawal and develop and validate predictive models. We performed an international multicentre observational cohort study in nine tertiary epilepsy referral centres. We included 850 adults who started antiseizure medication withdrawal following resective epilepsy surgery and were free of seizures other than focal non-motor aware seizures before starting antiseizure medication withdrawal. We developed a model predicting recurrent seizures, other than focal non-motor aware seizures, using Cox proportional hazards regression in a derivation cohort (n = 231). Independent predictors of seizure recurrence, other than focal non-motor aware seizures, following the start of antiseizure medication withdrawal were focal non-motor aware seizures after surgery and before withdrawal [adjusted hazard ratio (aHR) 5.5, 95% confidence interval (CI) 2.7-11.1], history of focal to bilateral tonic-clonic seizures before surgery (aHR 1.6, 95% CI 0.9-2.8), time from surgery to the start of antiseizure medication withdrawal (aHR 0.9, 95% CI 0.8-0.9) and number of antiseizure medications at time of surgery (aHR 1.2, 95% CI 0.9-1.6). Model discrimination showed a concordance statistic of 0.67 (95% CI 0.63-0.71) in the external validation cohorts (n = 500). A secondary model predicting recurrence of any seizures (including focal non-motor aware seizures) was developed and validated in a subgroup that did not have focal non-motor aware seizures before withdrawal (n = 639), showing a concordance statistic of 0.68 (95% CI 0.64-0.72). Calibration plots indicated high agreement of predicted and observed outcomes for both models. We show that simple algorithms, available as graphical nomograms and online tools (predictepilepsy.github.io), can provide probabilities of seizure outcomes after starting postoperative antiseizure medication withdrawal. These multicentre-validated models may assist clinicians when discussing antiseizure medication withdrawal after surgery with their patients