Citations

Introduction: Patients diagnosed with ischemic stroke routinely undergo prolonged cardiac telemetry (PCT) to evaluate for occult atrial fibrillation (AF), but this approach is limited by high cost and low yield. Clinical scores including CHA2DS2-VASc and CHARGE-AF can risk stratify patients but lack desired accuracy. Therefore, we aimed to evaluate an artificial intelligence (AI)-enabled ECG algorithm to predict AF detected by PCT after index stroke. Methods: This retrospective study included all adult patients with ischemic stroke evaluated at Mayo Clinic with baseline electrocardiogram (ECG) and PCT between 2018-2020. We recorded demographics, CHA2DS2-VASc score, CHARGE-AF score, stroke etiology, duration of PCT, and if PCT detected AF. ECGs were analyzed using the AI-ECG algorithm to determine AF likelihood. Stroke etiology was adjudicated using TOAST and embolic stroke of undetermined source (ESUS) definitions. The ability of the AI-ECG algorithm, CHA2DS2-VASc, and CHARGE-AF to predict AF detected by PCT was assessed via receiver operating characteristics analysis, calculating the area under curve (AUC) (c-statistic). Sensitivity and specificity analyses were performed for each tool using optimal cutoffs (using maximum Youden indices). Results: 863 patients were included; median age was 69 years, 496 (57.5%) were male, and 561 (65%) patients were categorized as ESUS. PCT detected AF in 85 (9.8%) patients. Median duration of cardiac telemetry was 30 (IQR 25-30) days. Sensitivity and specificity analyses are outlined in Table 1. The AI-ECG algorithm outperformed the CHA2DS2-VASc score (AUC 0.690 vs. 0.597, p=0.013), but was similar to the CHARGE-AF score (AUC 0.690 vs 0.651, p=0.233). Conclusions: Our AI-ECG algorithm demonstrated improved ability to predict AF detection on PCT after index stroke when compared to the CHA2DS2-VASc score, but was similar in performance to the CHARGE-AF score. All three methods had suboptimal accuracy.

Background The modified Rankin Scale (mRS) score of ≤2 (functional independence) has been the most common primary endpoint of modern mechanical thrombectomy (MT) trials. However, unlike mRS 0-1, mRS score of 2 indicates disability. An important proportion of the mRS 2 patients are home dependent and report a significant decrease in their quality of life. Purpose To investigate excellent outcome (mRS 0-1) rates after MT. Methods We systematically searched Ovid MEDLINE, Ovid EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science databases. Randomized controlled trials (RCTs) investigating the role of modern MT in acute ischemic stroke were screened. Posterior circulation and large-core infarct trials were excluded from the study. The data regarding excellent outcomes (mRS: 0-1), functional independence (mRS: 0-2), and reperfusion were collected. Results Twenty-two RCTs comprising 5692 patients were included in the meta-analysis. The overall mRS 0-1 rate was 31.24% (95% CI = 26.95–36.2). The rate of successful reperfusion was 81.8% (95% CI = 77.93–85.86). MT achieved significantly higher rates of mRS 0-1 compared to standard care alone (OR = 2.04; 95% CI = 1.64–2.55; P-value < 0.001), with no heterogeneity detected among studies (I 2 = 0%; P-value = 0.52). The direct MT and MT plus intravenous thrombolytic treatment groups’ excellent outcome rates were comparable (OR = 0.98; 95% CI = 0.82–1.18; P-value = 0.863). Also, aspiration and stent retriever thrombectomy techniques provided similar excellent outcome rates (OR = 0.76; 95% CI = 0.55–1.05; P-value = 0.141). Conclusions Our results prove the additional benefit of MT over standard care, using a stricter definition for favorable functional outcome. Nearly one-third of patients presenting with large artery occlusion and treated with MT had no disability at 90 days. While this is remarkable, our results also indicate that reperfusion alone is often not enough to prevent disability and underline the need for better neuroprotection strategies.

Introduction: Artificial Intelligence-enabled electrocardiogram (AI-ECG) can estimate the probability of having atrial fibrillation (AFib) while in sinus rhythm. Recurrent stroke in subjects without known AFib may be due to undiagnosed AFib. We evaluated the association between AI-ECG AFib and recurrent ischemic stroke and with incident AFib Methods: Consecutive adults admitted to a Mayo Clinic or Mayo Clinic Health System hospital or to Olmsted Community Hospital for ischemic stroke between January,1,2000 and June,30,2022 were identified using the Rochester Epidemiology Project. Recurrent ischemic strokes were identified after reviewing medical records and imaging studies. We excluded those with known AFib or without ECGs within 3 years prior to the first ischemic stroke. The AI-ECG network was previously developed using 649,931 ECGs from > 180,000 subjects. The primary endpoint was recurrent ischemic stroke; secondary endpoint was incident AFib. Cox model adjusted for age, sex, and CHA2DS2-VASc2 score variables. Results: We included 4,816 subjects, mean age 70±15 yr, 54% male, 92% white, median CHA2DS2-VASc2 score 4 [3-5], median follow-up 3 [1-7] yr. 280 subjects (6%) had a second stroke and 366 (8%) were diagnosed with incident AFib over follow-up. Those with a positive AI-ECG AFib had an increased risk for recurrent ischemic stroke(HR 1.54, 95% CI 1.21-1.96, P<0.001 Figure A) This association remained significant after adjustment for CHA2DS2-VASc2 score(HR 1.37 95% CI 1.07-1.76, P=0.01). These associations were not affected by anticoagulation use over follow-up. AI-ECG AFib was also associated with incident AFib(HR 2.42 95% CI 1.96-2.98, P<0.001 Figure B) Conclusions: AI-ECG AFib predicts recurrent ischemic stroke in the community, possibly reflecting undiagnosed AFib that can be detected by AI-ECG, as it also predicts incident AFib. These results underscore the potential utility of AI-ECG for risk stratification of patients with ischemic stroke.