Citations

Abstract Aims ECG abnormalities are often the first signs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and we hypothesized that an artificial intelligence (AI)-enhanced ECG could help identify patients with ARVC and serve as a valuable disease-detection tool. Methods and results We created a convolutional neural network to detect ARVC using a 12-lead ECG. All patients with ARVC who met the 2010 task force criteria and had disease-causative genetic variants were included. All case ECGs were randomly assigned in an 8:1:1 ratio into training, validation, and testing groups. The case ECGs were age- and sex-matched with control ECGs at our institution in a 1:100 ratio. Seventy-seven patients (51% male; mean age 47.2 ± 19.9), including 56 patients with PKP2, 7 with DSG2, 6 with DSC2, 6 with DSP, and 2 with JUP were included. The model was trained using 61 case ECGs and 5009 control ECGs; validated with 7 case ECGs and 678 control ECGs and tested in 22 case ECGs and 1256 control ECGs. The sensitivity, specificity, positive and negative predictive values of the model were 77.3, 62.9, 3.32, and 99.4%, respectively. The area under the curve for rhythm ECG and median beat ECG was 0.75 and 0.76, respectively. Conclusion Our study found that the model performed well in excluding ARVC and supports the concept that the AI ECG can serve as a biomarker for ARVC if a larger cohort were available for network training. A multicentre study including patients with ARVC from other centres would be the next step in refining, testing, and validating this algorithm.

Background As the number of thrombectomy passes increases during endovascular therapy (EVT) for acute stroke, neurointerventionalists must weigh the risks of potential complications against the risk of failing to recanalize the affected vessel. Thus, we investigated the effectiveness and safety of EVT for patients with acute stroke in which ≥5 EVT passes were performed. Methods This retrospective cohort study from January 2013 to December 2022 included 31 thrombectomy‐capable centers. Patients in whom ≥5 EVT passes were made were included in the analysis. The patients with successful recanalization defined as modified Thrombolysis in Cerebral Infarction ≥2b at 5, 6, 7, or more passes were compared with unsuccessful recanalization. We also compared procedure time, separated by 30 minutes. Primary outcome was a favorable 90‐day outcome (modified Rankin scale score of 0–2). Secondary outcomes were intracranial hemorrhage and death. Results A total of 407 patients met the inclusion criteria, of which 175 were women (43.1%); the mean age was 67.3 years. In 265 (65.1%) patients, successful recanalization was achieved: 134 patients with 5 passes, 67 patients with 6 passes, and 64 with ≥7 passes. Intravenous recombinant tissue plasminogen activator administration was more common in the unsuccessful recanalization group (successful recanalization with 5, 6, and ≥7 EVT passes and unsuccessful recanalization: 35.3%, 30.3%, 18.8%, and 45.4%, respectively). Successful recanalization with 5, 6, and ≥7 EVT passes was associated with favorable outcome compared with unsuccessful recanalization group (adjusted odds ratio successful recanalization with 5, 6, and ≥7 EVT passes to unsuccessful recanalization [95% CIs]: 8.29 [2.13–32.3]; 14.0 [3.27–60.3]; and 5.26 [1.12–24.8], respectively). However, symptomatic intracranial hemorrhage was not significantly different between the groups. Regarding procedure time, favorable outcome was not significantly different in each group, while symptomatic intracranial hemorrhage occurred more frequently in the 60 to 89 minutes group compared with the <30 minutes group (adjusted odds ratio, 60–89 minutes group to <30 minutes group [95% CI]: 5.86 [1.09–31.6]). Conclusion These findings suggest that a benefit in outcome persists for recanalization, without significant increase in complications. In addition, there was no significant difference in favorable outcomes among procedure time.

Abstract Introduction: First pass effect (FPE), achievement of complete recanalization (mTICI 2c/3) with a single pass, is a significant predictor of favorable outcomes for endovascular treatment (EVT) in large vessel occlusion stroke (LVO). However, data concerning the impact on functional outcomes and predictors of FPE in medium vessel occlusions (MeVO) are scarce. Patients and Methods: We conducted an international retrospective study on MeVO cases. Multivariable logistic modeling was used to establish independent predictors of FPE. Clinical and safety outcomes were compared between the two study groups (FPE vs non-FPE) using logistic regression models. Good outcome was defined as modified Rankin Scale 0–2 at 3 months. Results: Eight hundred thirty-six patients with a final mTICI ⩾ 2b were included in this analysis. FPE was observed in 302 patients (36.1%). In multivariable analysis, hypertension (aOR 1.55, 95% CI 1.10–2.20) and lower baseline NIHSS score (aOR 0.95, 95% CI 0.93–0.97) were independently associated with an FPE. Good outcomes were more common in the FPE versus non-FPE group (72.8% vs 52.8%), and FPE was independently associated with favorable outcome (aOR 2.20, 95% CI 1.59–3.05). 90-day mortality and intracranial hemorrhage (ICH) were significantly lower in the FPE group, 0.43 (95% CI, 0.25–0.72) and 0.55 (95% CI, 0.39–0.77), respectively. Conclusion: Over 2/3 of patients with MeVOs and FPE in our cohort had a favorable outcome at 90 days. FPE is independently associated with favorable outcomes, it may reduce the risk of any intracranial hemorrhage, and 3-month mortality.

OBJECTIVE The Woven EndoBridge (WEB) device is an intrasaccular flow disruptor designed for wide-necked bifurcation aneurysms. These aneurysms may require the use of a concomitant stent. The objective of this study was to determine the clinical and radiological outcomes of patients undergoing stent-assisted WEB treatment. In addition, the authors also sought to determine the predictors of a concomitant stent in aneurysms treated with the WEB device. METHODS The data for this study were taken from the WorldWideWEB Consortium, an international multicenter cohort including patients treated with the WEB device. Aneurysms were classified into two groups based on treatment: stent-assisted WEB and WEB device alone. The authors compared clinical and radiological outcomes of both groups. Univariable and multivariable binary logistic regression analyses were performed to determine factors that predispose to stent use. RESULTS The study included 691 intracranial aneurysms (31 with stents and 660 without stents) treated with the WEB device. The adequate occlusion status did not differ between the two groups at the latest follow-up (83.3% vs 85.6%, p = 0.915). Patients who underwent stenting had more thromboembolic (32.3% vs 6.5%, p < 0.001) and procedural (16.1% vs 3.0%, p < 0.001) complications. Aneurysms treated with a concomitant stent had wider necks, greater heights, and lower dome-to-neck ratios. Increasing neck size was the only significant predictor for stent use. CONCLUSIONS This study demonstrates that there is no difference in the degree of aneurysm occlusion between the two groups; however, complications were more frequent in the stent group. In addition, a wider aneurysm neck predisposes to stent assistance in WEB-treated aneurysms.

ImportanceLip, oral, and pharyngeal cancers are important contributors to cancer burden worldwide, and a comprehensive evaluation of their burden globally, regionally, and nationally is crucial for effective policy planning.ObjectiveTo analyze the total and risk-attributable burden of lip and oral cavity cancer (LOC) and other pharyngeal cancer (OPC) for 204 countries and territories and by Socio-demographic Index (SDI) using 2019 Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study estimates.Evidence ReviewThe incidence, mortality, and disability-adjusted life years (DALYs) due to LOC and OPC from 1990 to 2019 were estimated using GBD 2019 methods. The GBD 2019 comparative risk assessment framework was used to estimate the proportion of deaths and DALYs for LOC and OPC attributable to smoking, tobacco, and alcohol consumption in 2019.FindingsIn 2019, 370 000 (95% uncertainty interval [UI], 338 000-401 000) cases and 199 000 (95% UI, 181 000-217 000) deaths for LOC and 167 000 (95% UI, 153 000-180 000) cases and 114 000 (95% UI, 103 000-126 000) deaths for OPC were estimated to occur globally, contributing 5.5 million (95% UI, 5.0-6.0 million) and 3.2 million (95% UI, 2.9-3.6 million) DALYs, respectively. From 1990 to 2019, low-middle and low SDI regions consistently showed the highest age-standardized mortality rates due to LOC and OPC, while the high SDI strata exhibited age-standardized incidence rates decreasing for LOC and increasing for OPC. Globally in 2019, smoking had the greatest contribution to risk-attributable OPC deaths for both sexes (55.8% [95% UI, 49.2%-62.0%] of all OPC deaths in male individuals and 17.4% [95% UI, 13.8%-21.2%] of all OPC deaths in female individuals). Smoking and alcohol both contributed to substantial LOC deaths globally among male individuals (42.3% [95% UI, 35.2%-48.6%] and 40.2% [95% UI, 33.3%-46.8%] of all risk-attributable cancer deaths, respectively), while chewing tobacco contributed to the greatest attributable LOC deaths among female individuals (27.6% [95% UI, 21.5%-33.8%]), driven by high risk-attributable burden in South and Southeast Asia.Conclusions and RelevanceIn this systematic analysis, disparities in LOC and OPC burden existed across the SDI spectrum, and a considerable percentage of burden was attributable to tobacco and alcohol use. These estimates can contribute to an understanding of the distribution and disparities in LOC and OPC burden globally and support cancer control planning efforts.

ABSTRACT Aims Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. Methods and results A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85–0.92), a sensitivity of 0.85 (0.78–0.95) and a specificity of 0.76 (0.74–0.79) in the internal validation cohort and an AUC of 0.82 (0.73–0.91), a sensitivity of 0.74 (0.61–0.87) and a specificity of 0.79 (0.77–0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. Conclusion A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death.

Background For acute proximal intracranial artery occlusions, contact aspiration may be more effective than stent‐retriever for first‐line reperfusion therapy. Due to the lack of data regarding medium vessel occlusion thrombectomy, we evaluated outcomes according to first‐line technique in a large, multicenter registry. Methods Imaging, procedural, and clinical outcomes of patients with acute proximal medium vessel occlusions (M2, A1, or P1) or distal medium vessel occlusions (M3, A2, P2, or further) treated at 37 sites in 10 countries were analyzed according to first‐line endovascular technique (stent‐retriever versus aspiration). Multivariable logistic regression and propensity‐score matching were used to estimate the odds of the primary outcome, expanded Thrombolysis in Cerebral Infarction score of 2b–3 (“successful recanalization”), as well as secondary outcomes (first‐pass effect, expanded Thrombolysis in Cerebral Infarction 2c‐3, intracerebral hemorrhage, and 90‐day modified Rankin scale, 90‐day mortality) between treatment groups. Results Of the 440 included patients (44.5% stent‐retriever versus 55.5% aspiration), those treated with stent‐retriever had lower baseline Alberta Stroke Program Early Computed Tomography Scale scores (median 8 versus 9; P <0.01), higher National Institutes of Health Stroke Scale scores (median 13 versus 11; P =0.02), and nonsignificantly fewer medium‐distal occlusions (M3, A2, P2, or other: 17.4% versus 23.8%; P =0.10). Use of a stent‐retriever was associated with 15% lower odds of successful recanalization (odds ratio [OR], 0.85; [95% CI 0.74–0.98]; P =0.02), but this was not significant after multivariable adjustment in the total cohort (adjusted OR, 0.88; [95% CI 0.72–1.09]; P =0.24), or in the propensity‐score matched cohort (n=105 in each group) (adjusted OR, 0.94; [95% CI 0.75–1.18]; P =0.60). There was no significant association between technique and secondary outcomes in the propensity‐score matched adjusted models. Conclusion In this large, diverse, multinational medium vessel occlusion cohort, we found no significant difference in imaging or clinical outcomes with aspiration versus stent‐retriever thrombectomy.

Introduction: Over 100 FDA-approved medications, electrolyte perturbations, and many disease states can prolong the QTc beyond its 99 th percentile value resulting in acquired QT prolongation. In contrast, approximately 1 in 2000 people have congenital long QT syndrome (LQTS) hallmarked by pathological QT prolongation secondary to genetic defects in the heart. Hypothesis: An artificial intelligence (AI) deep neural network (DNN) can distinguish patients with LQTS from those with acquired QT prolongation. Methods: The study cohort included all patients with LQTS evaluated in the Windland Smith Rice Genetic Heart Rhythm Clinic and controls from Mayo Clinic’s ECG data vault comprising over 2.5 million patients. For the AI-DNN model, every patient/control with ≥ 1 ECG above age- and sex- specific 99 th percentile values for QTc [> 460 ms for all patients (male/female) < 13 years of age, or > 470 ms for men and > 480 ms for women above this age] was included. An AI-DNN involving a multi-layer c onvolutional n eural n etwork (CNN) was developed to classify patients. LQTS patients were age- and sex- matched to controls at 1:5 ratio. Results: Among the 1,599 patients with genetically confirmed LQTS, 808 had ≥ 1 ECG with QTc above the threshold (2,987 ECGs) compared to 361,069/2.5M controls (14% of Mayo Clinic patients getting an ECG, ‘presumed negative’; 989,313 ECGs). Following age- and sex- matching and splitting, 3,309 (training), 411 (validation) and 887 (testing) control ECGs were used. This model distinguished LQTS from those with acquired QT prolongation with an AUC of 0.896 (accuracy 85%, sensitivity 77%, specificity 88%, PPV 0.58, NPV 0.94). After exclusion of patients with a wide QRS (>150 ms) or pacemaker, the model remained successful in distinguishing the two groups (AUC 0.85, accuracy 78%, sensitivity 78%, specificity 87%, PPV 59%, NPV 94%). Conclusions: For patients with a QTc exceeding its 99 th percentile values, this novel AI-DNN functions as a LQTS mutation detector being able to identify patients with abnormal QT prolongation secondary to a LQTS-causative mutation rather than acquired QT prolongation with a >50% positive predictive value. This algorithm may facilitate screening for this potentially lethal, yet highly treatable, genetic heart disease.

Introduction: Ebstein’s anomaly (EA) is a rare congenital anomaly of the tricuspid valve associated with heart failure and arrhythmias. Currently available imaging modalities like transthoracic echocardiography, cardiac magnetic resonance imaging (MRI), or computerized tomography (CT) are not viable for screening large asymptomatic populations. Hypothesis: A deep learning algorithm, implemented using a convolutional neural network (CNN) applied to the standard 12-lead ECG can be used as an accurate screening tool for EA in children and adults. Method: We identified 1,154 EA patients and 21,430 age- and sex-matched controls (1:20 ratio) between 1987 - 2020. A CNN was trained, validated and tested using digital 12-lead ECGs in 70%, 10% and 20% of the cohort respectively, divided by random shuffling. Results: The mean age was 26.68±19.23 years (41.8% aged < 18 years, 56.41% female) for the EA cohort and 28.36±18.65 years (37.2% aged <18 years, 55.67% female) for the control cohort. Following training and validation the area under the curve (AUC) for detecting EA in the testing cohort was 0.976 (95% CI 0.962 - 0.989) with prediction accuracy of 0.959. In subgroup analysis, the AUC was comparable in males (0.970, 95% CI 0.946-0.995) and females (0.979, 95% CI 0.964-0.994), those <18 (0.980, 95% CI 0.958-1.000) and ≥18 years old (0.972, 95% CI 0.954-0.989), as well as in those with right bundle branch block (RBBB) (0.969, 95% CI 0.950-0.989) and without RBBB (0.950, 95% CI 0.922-0.978) on ECG. Conclusion: Artificial intelligence enhanced ECG based detection of EA is possible with high diagnostic accuracy. Following further refinement and external validation, this algorithm can facilitate early detection of EA using ECGs, a widely available and inexpensive tool. Figure: Receiver operating characteristic (ROC) curve for the performance of the artificial intelligence CNN in detecting EA in the test cohort stratified by (A) age (< 18 and ≥ 18 years) and sex and (B) presence of RBBB.