Prognostic factors predicting an unprovoked seizure recurrence in children and adults following a first unprovoked seizure.

backgroundAssessing the risk of seizure recurrence after a first unprovoked seizure remains a clinical challenge but is essential for counselling, especially given its impact on driving, employment, and treatment decisions. The International League Against Epilepsy now allows for an 'operational' diagnosis of epilepsy after a single unprovoked seizure, based on an individual's recurrence risk. This shift highlights the need for more precise tools to predict seizure risk and guide accurate diagnosis and management.

objectivesTo identify which prognostic factors predict the risk of subsequent unprovoked seizures and the development of epilepsy at any time following a first unprovoked seizure, a cluster of seizures within 24 hours, or a first episode of status epilepticus - regardless of seizure type. SEARCH

methodsWe searched the following databases between 12 and 15 December 2022, with no language restrictions: CENTRAL, MEDLINE, SCOPUS, ClinicalTrials.gov and the World Health Organization (WHO) International Clinical Trials Registry Platform. SELECTION CRITERIA: We included retrospective and prospective cohort studies and randomised controlled trials (RCTs) that assessed prognostic factors for seizure recurrence following a first unprovoked seizure. Eligible studies reported on at least one index prognostic factor (including demographic variables such as age and sex, clinical features such as seizure type, and investigation results such as electroencephalogram (EEG) and neuroimaging) and assessed seizure recurrence outcomes. Prognostication began at the time of the initial seizure, with outcomes assessed at a minimum of six months' follow-up. DATA COLLECTION AND ANALYSIS: Two review authors screened titles and abstracts identified through searches and removed irrelevant articles. We extracted data using a data extraction form. We conducted separate meta-analyses to pool odds ratios (ORs), risk ratios (RRs), and hazard ratios (HRs) reported from univariable regression analyses in the included studies. We conducted meta-analyses using a random-effects, generic inverse-variance model, which accounted for any between-study heterogeneity in the prognostic effect. We summarised the meta-analysis by the pooled estimate (the average prognostic factor effect), its 95% confidence interval (CI), the I² (heterogeneity) estimates, and a 95% prediction interval for the predictive effect in a single population. Two review authors independently extracted data and assessed risk of bias using the QUality In Prognosis Studies (QUIPS) tool. We adapted the GRADE framework to assess the certainty of evidence for each prognostic factor-outcome association. We rated evidence certainty as high, moderate, low, or very low, based on study phase, internal validity, effect size and precision, heterogeneity, generalisability, and reporting bias. MAIN

resultsWe included 23 studies (5918 participants). Cohort sizes varied from 50 to 1885 participants (median 134). Most studies were cohort designs (15 prospective, seven retrospective), with one RCT. Median follow-up was 35 months (range six to 283 months). Seven studies included participants on anti-seizure medication (ASM) after their first seizure; 16 did not. Eight studies were adult-only, 12 paediatric-only, and three included both age groups. Using the QUIPS tool, nine studies (39%) had a low risk of bias, 11 (48%) unclear, and three (13%) high. Abnormal EEG (RR 1.90, 95% CI 1.60 to 2.25; P = 0.022, I² = 55.4%; 9 studies, 1904 participants; HR 1.45, 95% CI 1.17 to 1.79; P = 0.018, I² = 75%; 3 studies, 939 participants) probably increases the risk of seizure recurrence (moderate-certainty evidence for RR; low-certainty for HR). Low-certainty evidence suggests abnormal brain imaging (RR 2.19, 95% CI 1.74 to 2.76; P = 0.085, I² = 54.6%; 4 studies, 890 participants), nocturnal seizures (HR 1.41, 95% CI 1.13 to 1.75; P = 0.674, I² = 0%; 3 studies, 967 participants; RR 1.23, 95% CI 1.04 to 1.47; P = 0.017, I² = 70.7%; 4 studies, 1248 participants), family history of epilepsy (RR 1.47, 95% CI 1.16 to 1.85; P = 0.423, I² = 0%; 6 studies, 1290 participants), and Todd's paresis (RR 1.48, 95% CI 1.02 to 2.13; P = 0.102, I² = 56.2%; 3 studies, 836 participants) may increase seizure recurrence, but findings remain uncertain due to limitations in study quality, consistency, and precision. Very low-certainty evidence means we are uncertain about associations of febrile seizures (RR 1.02, 95% CI 0.82 to 1.28; P = 0.516, I² = 0%; 3 studies, 841 participants), focal neurological deficit (HR 1.21, 95% CI 0.92 to 1.60; P = 0.028, I² = 67%; 4 studies, 981 participants), status epilepticus (RR 1.05, 95% CI 0.81 to 1.36; P = 0.0507, I² = 56.4%; 5 studies, 1456 participants), male sex (RR 1.14, 95% CI 0.94 to 1.39; P = 0.190, I² = 39.8%; 3 studies, 738 participants), initial focal seizures (OR 1.19, 95% CI 0.77 to 1.85; P = 0.004, I² = 88.1%; 2 studies, 473 participants), and age under 16 years (OR 1.80, 95% CI 1.16 to 2.79; I² = 0%; 5 cohorts, 522 participants; low-certainty evidence but RR 0.69, 95% CI 0.47 to 1.01; I² = 37.9%; 3 studies, 480 participants; very low-certainty evidence) with seizure recurrence, due to significant inconsistency, imprecision, indirectness, and risk of bias across studies. AUTHORS'

conclusionsWe aimed to identify prognostic factors predicting seizure recurrence after a first unprovoked seizure. Considerable heterogeneity and inconsistency in how the included studies defined, measured, and reported prognostic factors significantly limited our analyses and led to downgrading of the evidence due to imprecision and methodological variability. These limitations highlight the need for standardisation. Future studies would benefit from adherence to an international core outcome set currently under development, which will standardise reporting and collection of prognostic factor data, enhancing comparability and reliability. Identifying high-risk cohorts remains critical for guiding clinical decisions, shaping healthcare policy, and enabling recruitment into trials of disease-modifying treatments.