Samoograniczająca się epilepsja z ogniskowymi wyladowaniami w okolicy środkowej i skroniowej oraz aktywacją iglicowo-falową w trakcie snu: cechy kliniczne i wyniki badań elektrycznych mózgu po 2 latach obserwacji
Self-limited epilepsy with centrotemporal spikes (SeLECTS) with spike-and-wave activation in sleep (SWAS): clinical characteristics and electroencephalogram outcomes at the 2-year follow-up
W skrócie
Badacze analizowali 111 dzieci z specjalnym typem epilepsji, która ma tendencję do samoograniczania się. Odkryli pięć czynników, które pozwalają przewidzieć, czy u dziecka wyladowania elektryczne w mózgu będą się utrzymywać przez dłuższy czas, i opracowali model matematyczny do wczesnego rozpoznawania dzieci wymagających intensywniejszej obserwacji. Narzędzie to może pomóc lekarzom w lepszym planowaniu leczenia i poprawie długoterminowych wyników zdrowotnych.
Oryginalny abstract (angielski)
OBJECTIVE: To identify risk factors for persistent spike-and-wave activation in sleep (SWAS) in children with self-limited epilepsy with centrotemporal spikes (SeLECTS), construct a nomogram prediction model and provides a reliable tool for early risk stratification in high-risk children, thereby facilitating optimized clinical decision-making and improving long-term prognosis. METHODS: This retrospective study included 111 patients with a diagnosis of SeLECTS with spike-and-wave activation in sleep (SWAS) and followed-up at least 2 years. Resolution of the SWAS pattern was defined as the reduction of the spike-wave index (SWI) to < 50%. SWI was calculated using the conventional method, defined as the percentage of 1-second epochs with at least one spike-wave discharge during non-rapid eye movement (NREM) sleep relative to the total number of 1-second epochs in NREM sleep. Risk factors were identified using binary multivariate logistic regression. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). RESULTS: Among the 111 patients, resolution of the SWAS pattern was observed in 57.66% (n = 64). The data demonstrated that risk factors significantly associated with persistent SWAS (P < 0.05) included younger age at seizure onset, bilateral electroencephalographic discharges, higher initial SWI, increased SWI after 6 months of standard treatment, and persistent seizures after 6 months of standard treatment. The nomogram achieved an area under the ROC curve (AUC) of 0.908, showed good calibration, and provided significant net clinical benefit in decision curve analysis (DCA). CONCLUSIONS: In this study, we identified five independent risk factors for persistent SWAS and developed a predictive nomogram. This model demonstrates excellent predictive performance and may serve as a valuable clinical tool for predicting EEG outcomes and guiding clinical management in children with SeLECTS and SWAS.