Introduction: External recorders allow for low-cost, non-invasive 1 to 4 weeks monitoring. However, the first 3 months of monitoring duration are the most effective to detect atrial fibrillation (AF). We show the results of the Thunder registry of patients monitored to detect AF during 90 days from the stroke. Methods: A prospective observational study was conducted with consecutive inclusion of patients with cryptogenic stroke after work up (neuroimaging, echocardiography and 24-hour cardiac monitoring) in 5 Comprenhensive Stroke Centers. Patients were continuously monitored for 90 days with a wearable Holter (Nuubo®) after the first 24 hours of the stroke onset. We analyzed the percentage of AF detection in each period (percentage of AF among those monitored), the quality of the monitoring (monitoring time), the percentage of AF by intention to monitor (detection of AF among patient included). Demographic, clinical and echocardiographic predictors of AF detection beyond one week of cardiac monitoring were assessed. Results: A total of 254 patients were included. The cumulative incidence of AF detection at 90 days was 34.84%. The monitoring time was similar among the 3 months (30 days: 544.9 hours Vs 60 days: 505.9 hours Vs 90 days: 591.25 hours) (p=0.512). The number of patients who abandoned monitoring was 7% (18/254). The cumulative percentage of intention to detect AF was 30.88% (Figure). Patients who completed monitoring beyond 30 days had higher score on the NIHSS basal scale (NIHSS 9 IQR 2-17) VS (NIHSS 3 IQR 1-9) (p=0.024). Patients with left atrial volume greater than 28.5ml/m2 had higher risk of cumulative incidence of AF according to the Kaplan Meyer curve beyond the first week of monitoring OR 2.72 (Log-rank (Mantel-Cox test) (p<0.001). Conclusions: In conclusion, intensive 90-day- Holter monitoring with textile Holter was feasible and detected high percentage of AF. Enlarged left atrial volume predicted AF beyond the first week of monitoring.

Artificial Intelligence (AI) can assist in vessel occlusion (VO) identification in acute stroke patients. We aim to investigate the impact of using an AI-based software for automated VO detection on non-contrast CT (AI-VO) as compared to CT-Aangiograpphy (CTA). Methods: From April to October 2020 all patients admitted with a suspected acute ischemic stroke underwent urgent non-contrast CT / CTA / CTP and were treated accordingly. Hypoperfusion areas defined as Tmax>6s on CTP (RAPID software), congruent with the clinical symptoms and a vascular territory, were considered VO (CTP-VO: ground truth). In addition, two experienced neuroradiologists blinded to CTP but not to clinical symptoms retrospectively evaluated CT and CTA to identify intracranial VO (CTA-VO). AI-VO was automatically determined by an AI-based software (Methinks). Results: Of the 338 patients included, 157 (46.5%) showed a CTP-VO (median Tmax>6s: 73[29-127]ml). Overall sensitivity to detect CTP-VO was 50.3% for CTA-VO and 66.9% for AI-VO; specificity was 97.8% for CTA-VO and 86.2% for AI-VO. EVT was performed in 103 patients (EVT-VO: 65.6% of CTP-VO; Tmax>6s: 102[63-160]ml); sensitivity to detect EVT-VO was 69% for CTA-VO and 79.6% for AI-VO; specificity was 95.3% for CTA-VO and 79.6% for AI-VO. The probability to detect a CTP-VO was higher with AI than with CTA for distal occlusions (figure). Accordingly, AI-VO sensitivity was higher than CTA-VO for angiographically confirmed M2/M3-MCA occlusions (80.7% vs 34.6%; p=0.002) but not for M1-MCA/ICA occlusions (82.1% Vs 88.1%;p=0.467). Conclusion: AI-assisted vessel occlusion identification on non-contrast CT may be a useful tool in acute stroke evaluation, especially for distal VO identification, potentially increasing endovascular treatment in these cases.