RESUMO
PURPOSE: This study investigates the performance of a cardiac-based seizure detection algorithm (CBSDA) that automatically triggers VNS (NCT01325623). METHODS: Thirty-one patients with drug resistant epilepsy were evaluated in an epilepsy monitoring unit (EMU) to assess algorithm performance and near-term clinical benefit. Long-term efficacy and safety were evaluated with combined open and closed-loop VNS. RESULTS: Sixty-six seizures (n=16 patients) were available from the EMU for analysis. In 37 seizures (n=14 patients) a ≥ 20% heart rate increase was found and 11 (n=5 patients) were associated with ictal tachycardia (iTC, 55% or 35 bpm heart rate increase, minimum of 100 bpm). Multiple CBSDA settings achieved a sensitivity of ≥ 80%. False positives ranged from 0.5 to 7.2/h. 27/66 seizures were stimulated within ± 2 min of seizure onset. In 10/17 of these seizures, where triggered VNS overlapped with ongoing seizure activity, seizure activity stopped during stimulation. Physician-scored seizure severity (NHS3-scale) showed significant improvement for complex partial seizures (CPS) at EMU discharge and through 12 months (p<0.05). Patient-scored seizure severity (total SSQ score) showed significant improvement at 3 and 6 months. Quality of life (total QOLIE-31-P score) showed significant improvement at 12 months. The responder rate (≥ 50% reduction in seizure frequency) at 12 months was 29.6% (n=8/27). Safety profiles were comparable to prior VNS trials. CONCLUSIONS: The investigated CBSDA has a high sensitivity and an acceptable specificity for triggering VNS. Despite the moderate effects on seizure frequency, combined open- and closed-loop VNS may provide valuable improvements in seizure severity and QOL in refractory epilepsy patients.