Przewidywanie ryzyka powrotu napadów epilepsji u dzieci po odstawieniu leków przeciwpadaczkowych: spersonalizowany model wspomagający decyzje kliniczne dostępny online
Predicting the risk of seizure recurrence after antiseizure medication withdrawal in children with epilepsy: An individualized and web-based clinical decision support model
W skrócie
Badanie analizowało, u ilu dzieci z epilepsją powracają napady po przerwaniu leczenia lekami przeciwpadaczkowymi. Okazało się, że napady wracają u około 26% pacjentów, a ryzyko zależy od poprzednich napadów wysokiej klasy (status epilepticus), gorączki towarzyszącej napadom oraz czasu trwania choroby. Naukowcy stworzyli internetowe narzędzie, które pomaga lekarzom ocenić indywidualne ryzyko powrotu napadów u konkretnego dziecka na podstawie jego historii medycznej.
Oryginalny abstract (angielski)
BACKGROUND: To determine seizure recurrence rates and independent risk factors following antiseizure medication (ASM) withdrawal in pediatric epilepsy, and integrate these findings into a personalized web-based clinical decision support system (CDSS). METHODS: In this prospective cohort study, 318 pediatric epilepsy patients with ≥24 months of clinical seizure freedom and completely normal pre-withdrawal EEGs were evaluated during ASM withdrawal. Independent risk factors were identified using a multivariable Cox proportional hazards model and embedded into a dynamic web tool. RESULTS: Seizure recurrence occurred in 25.8% (n = 82) of patients (mean time: 4.70 ± 13.96 months). The Cox model demonstrated acceptable discrimination (C-index = 0.808, optimism-corrected C-index = 0.769). Independent predictors of recurrence were status epilepticus (SE; HR = 2.718, 95% CI: 1.193-6.194; p = 0.017), febrile seizures (FS; HR = 2.300, 95% CI: 1.214-4.356; p = 0.011), and active epilepsy duration (HR = 1.022 per month, 95% CI: 1.016-1.027; p < 0.001). Compared to ≤24 months, pre-withdrawal seizure freedom of 25-35 months (HR = 0.231, p = 0.001) and ≥36 months (HR = 0.232, p = 0.002) both conferred an approximate 77% risk reduction. Mandatory pre-withdrawal EEG normalization served as a controlled methodological feature isolating clinical predictors in EEG-normal children. Among 35 patients undergoing a second withdrawal attempt, 11 (31.4%) experienced recurrence. CONCLUSIONS: SE, FS, active epilepsy duration, and pre-withdrawal seizure-free duration independently predict recurrence in children with normal pre-withdrawal EEGs. Embedding this model into a web tool enables dynamic, evidence-based risk stratification in pediatric practice.