ABSTRACT
BACKGROUND AND AIMS: Two or more National Institutes of Health Stroke Scale (NIHSS) points on each motor items (A2L2 score) have shown good accuracy in predicting large vessel occlusion (LVO) in the prehospital setting of acute ischemic stroke (AIS) care. We aimed to study this score for LVO prediction in our stroke network and predictors of poor outcome (PO) after mechanical thrombectomy (MT). METHODS: From our Safe Implementation of Thrombolysis in Stroke (SITS) registry including patients receiving reperfusion therapy for AIS, we retrospectively computed the A2L2 score from the admission NIHSS to test the diagnostic accuracy for LVO prediction. Multivariable analysis for independent predictors of LVO on the entire cohort and PO from patients with LVO were performed. RESULTS: From the 853 patients with AIS (67% LVO), A2L2 was positive in 52%. A2L2 score (Odds ratio [OR] 4.6;95%CI 3.36-6.34), smoking (OR 2.1;95%CI 1.14-3.85), atrial fibrillation (OR 1.6;95%CI1.1-2.4) and younger age (OR 0.98;95%CI0.97-0.99) were independent predictors of LVO. A2L2 score showed 82%/49% positive/negative predictive values with 66% accuracy (64%/72% sensitivity/specificity) for LVO prediction. Age (OR 1.05;95%CI 1.03-1.07), atrial fibrillation (OR 4.85;95%CI 1.5-15.7), diabetes (OR 2.62;95% CI 1.14-6.05), dyslipidemia (OR 2;95% CI 1.04-3.87), A2L2 score (OR 2.68;95% CI 1.45-4.98), longer onset-to-groin time (OR 1.003;95% CI 1.001-1.01), MT procedure (OR 1.01;95%CI 1.003-1.02) general anaesthesia (OR 2.06;95% CI 1.1-3.83) and symptomatic intracranial hemorrhage (OR 12.10;95%CI 3.15-46.44) were independent predictors of PO. CONCLUSIONS: A2L2 score independently predicted LVO and PO after MT. Patient characteristics and procedural factors determined PO of LVO patients after MT.
ABSTRACT
Aims: The aim of this study is to determine the feasibility, detection rate, and therapeutic implications of large-scale smartphone-based screening for atrial fibrillation (AF). Methods and results: Subjects from the general population in Belgium were recruited through a media campaign to perform AF screening during 8 consecutive days with a smartphone application. The application analyses photoplethysmography traces with artificial intelligence and offline validation of suspected signals to detect AF. The impact of AF screening on medical therapy was measured through questionnaires. Atrial fibrillation was detected in the screened population (n = 60.629) in 791 subjects (1.3%). From this group, 55% responded to the questionnaire. Clinical AF [AF confirmed on a surface electrocardiogram (ECG)] was newly diagnosed in 60 individuals and triggered the initiation of anti-thrombotic therapy in 45%, adjustment of rate or rhythm controlling strategies in 62%, and risk factor management in 17%. In subjects diagnosed with known AF before screening, a positive screening result led to these therapy adjustments in 9%, 39%, and 11%, respectively. In all subjects with clinical AF and an indication for oral anti-coagulation (OAC), OAC uptake increased from 56% to 74% with AF screening. Subjects with clinical AF were older with more co-morbidities compared with subclinical AF (no surface ECG confirmation of AF) (P < 0.001). In subjects with subclinical AF (n = 202), therapy adjustments were performed in only 7%. Conclusion: Smartphone-based AF screening is feasible at large scale. Screening increased OAC uptake and impacted therapy of both new and previously diagnosed clinical AF but failed to impact risk factor management in subjects with subclinical AF.
