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Stroke is one of the leading causes of acquired epilepsy in adults. An instrument to predict whether people are at high risk of developing post-stroke seizures is not available. We aimed to develop and validate a prognostic model of late (>7 days) seizures after ischaemic stroke. In this multivariable prediction model development and validation study, we developed the SeLECT score based on five clinical predictors in 1200 participants who had an ischaemic stroke in Switzerland using backward elimination of a multivariable Cox proportional hazards model. We externally validated this score in 1169 participants from three independent international cohorts in Austria, Germany, and Italy, and assessed its performance with the concordance statistic and calibration plots. Data were complete for 99·2% of the predictors (99·2% for Switzerland, 100% for Austria, 97% for Germany, and 99·7% for Italy) and 100% of the outcome parameters. Overall, the risk of late seizures was 4% (95% CI 4–5) 1 year after stroke and 8% (6–9) 5 years after stroke. The final model included five variables and was named SeLECT on the basis of the first letters of the included parameters (severity of stroke, large-artery atherosclerotic aetiology, early seizures, cortical involvement, and territory of middle cerebral artery involvement). The lowest SeLECT value (0 points) was associated with a 0·7% (95% CI 0·4–1·0) risk of late seizures within 1 year after stroke (1·3% [95% CI 0·7–1·8] within 5 years), whereas the highest value (9 points) predicted a 63% (42–77) risk of late seizures within 1 year (83% [62–93] within 5 years). The model had an overall concordance statistic of 0·77 (95% CI 0·71–0·82) in the validation cohorts. Calibration plots indicated high agreement of predicted and observed outcomes. This easily applied instrument was shown to be a good predictor of the risk of late seizures after stroke in three external validation cohorts and is freely available as a smartphone app. The SeLECT score has the potential to identify individuals at high risk of seizures and is a step towards more personalised medicine. It can inform the selection of an enriched population for antiepileptogenic treatment trials and will guide the recruitment for biomarker studies of epileptogenesis. None.

[...]for a model to be relevant, it must provide outcome information satisfying the needs of the user at, or soon after, the event. [...]seizures due to hyponatraemia would not have influenced our results because we only considered spontaneous unprovoked seizures after stroke as late seizure and excluded those potentially provoked by hyponatraemia. [...]only the German validation cohort (n=311) relied exclusively on telephonic follow-up.

Stroke is the leading cause of seizures and epilepsy in older adults. Patients who have larger and more severe strokes involving the cortex, are younger, and have acute symptomatic seizures and intracerebral haemorrhage are at highest risk of developing post-stroke epilepsy. Prognostic models, including the SeLECT and CAVE scores, help gauge the risk of epileptogenesis. Early electroencephalogram and blood-based biomarkers can provide information additional to the clinical risk factors of post-stroke epilepsy. The management of acute versus remote symptomatic seizures after stroke is markedly different. The choice of an ideal antiseizure medication should not only rely on efficacy but also consider adverse effects, altered pharmacodynamics in older adults, and the influence on the underlying vascular co-morbidity. Drug–drug interactions, particularly those between antiseizure medications and anticoagulants or antiplatelets, also influence treatment decisions. In this review, we describe the epidemiology, risk factors, biomarkers, and management of seizures after an ischaemic or haemorrhagic stroke. We discuss the special considerations required for the treatment of post-stroke epilepsy due to the age, co-morbidities, co-medication, and vulnerability of stroke survivors.

BackgroundIn addition to other stroke-related deficits, the risk of seizures may impact driving ability after stroke.MethodsWe analysed data from a multicentre international cohort, including 4452 adults with acute ischaemic stroke and no prior seizures. We calculated the Chance of Occurrence of Seizure in the next Year (COSY) according to the SeLECT2.0 prognostic model. We considered COSY<20% safe for private and <2% for professional driving, aligning with commonly used cut-offs.ResultsSeizure risks in the next year were mainly influenced by the baseline risk-stratified according to the SeLECT2.0 score and, to a lesser extent, by the poststroke seizure-free interval (SFI). Those without acute symptomatic seizures (SeLECT2.0 0–6 points) had low COSY (0.7%–11%) immediately after stroke, not requiring an SFI. In stroke survivors with acute symptomatic seizures (SeLECT2.0 3–13 points), COSY after a 3-month SFI ranged from 2% to 92%, showing substantial interindividual variability. Stroke survivors with acute symptomatic status epilepticus (SeLECT2.0 7–13 points) had the highest risk (14%–92%).ConclusionsPersonalised prognostic models, such as SeLECT2.0, may offer better guidance for poststroke driving decisions than generic SFIs. Our findings provide practical tools, including a smartphone-based or web-based application, to assess seizure risks and determine appropriate SFIs for safe driving.

Thymus-derived natural Foxp3 CD4 regulatory T cells (nTregs) play a key role in maintaining immune tolerance and preventing autoimmune disease. Several studies indicate that dendritic cells (DCs) are critically involved in the maintenance and proliferation of nTregs. However, the mechanisms how DCs manage to keep the peripheral pool at constant levels remain poorly understood. Here, we describe that the NF-κB/Rel family transcription factor RelB controls the frequencies of steady-state migratory DCs (ssmDCs) in peripheral lymph nodes and their numbers control peripheral nTreg homeostasis. DC-specific RelB depletion was investigated in CD11c-Cre × RelB mice (RelB ), which showed normal frequencies of resident DCs in lymph nodes and spleen while the subsets of CD103 Langerin dermal DCs (dDCs) and Langerhans cells but not CD103 Langerin dDC of the ssmDCs in skin-draining lymph nodes were increased. Enhanced frequencies and proliferation rates were also observed for nTregs and a small population of CD4 CD44 CD25 memory-like T cells (Tml). Interestingly, only the Tml but not DCs showed an increase in IL-2-producing capacity in lymph nodes of RelB mice. Blocking of IL-2 reduced the frequency of nTregs but increased the Tml frequencies, followed by a recovery of nTregs. Taken together, by employing RelB mice with increased frequencies of ssmDCs our data indicate a critical role for specific ssmDC subsets for the peripheral nTreg and IL-2 Tml frequencies during homeostasis.