Citations

OBJECTIVE The definitive influence of prestroke disability on outcomes in patients with a low Alberta Stroke Program Early CT Score (ASPECTS) treated with endovascular thrombectomy (EVT) for acute ischemic stroke (AIS) due to large-vessel occlusion (LVO) remains unknown. This study aimed to investigate the impact of prestroke disability on outcomes in this specific population. METHODS Data from 32 international centers for AIS-LVO patients with a low ASPECTS who underwent EVT between January 2013 and December 2022 were analyzed retrospectively. Low ASPECTS and prestroke disability were defined as ASPECTS values of 2–5 and prestroke modified Rankin Scale (mRS) score ≥ 2. The primary outcome was a return to the prestroke mRS score at 90 days. Secondary outcomes were independent ambulation (mRS scores of 0–3) or a return to the prestroke mRS score at 90 days, good functional outcome (mRS scores of 0–2) or a return to the prestroke mRS score at 90 days, successful recanalization, and 90-day mortality. Safety outcomes were any intracranial hemorrhage or symptomatic intracranial hemorrhage. A symptomatic intracranial hemorrhage was defined as an intracranial hemorrhage with an associated worsening of ≥ 4 points in the National Institutes of Health Stroke Scale score. Outcomes were compared between patients with and without prestroke disability. RESULTS Of 293 patients, 50 (17.1%) had a prestroke disability. Of 50 patients, 20 (40.0%), 24 (48.0%), and 6 (12.0%) had prestroke mRS scores of 2, 3, and 4, respectively. The primary outcome showed no significant difference between the two groups. Compared with patients without prestroke disability, those with prestroke disability had a significantly smaller proportion of independent ambulation or return to prestroke mRS score (adjusted OR 0.13, 95% CI 0.03–0.53) and good functional outcome or return to prestroke mRS score (adjusted OR 0.21, 95% CI 0.05–0.91). Other secondary and safety outcomes showed no significant difference between the two groups. CONCLUSIONS The present study indicated that prestroke disability was not associated with a return to the prestroke mRS score at 90 days or intracranial hemorrhage. Physicians should not routinely exclude AIS-LVO patients with a low ASPECTS who have prestroke disability from EVT based on prestroke disability alone.

OBJECTIVE Tobacco smoking is among the factors known to significantly augment the risk of untreated intracranial aneurysm (IA) growth and rupture. Smoking appears to have a variable effect on different endovascular treatment modalities. The impact of smoking on the safety, efficacy, and outcomes of Woven EndoBridge (WEB) device use for wide-neck IAs has not been evaluated. This study aimed to investigate the outcomes of WEB devices by smoking status. METHODS A retrospective multicenter analysis was conducted on the data of patients from 36 sites worldwide treated with the WEB device for intracranial saccular aneurysms. Patients were stratified based on smoking status (current, former, and never smokers). The Student t-test and chi-square test were performed for continuous and categorical variables, respectively. Multivariable logistic regression was used to adjust for confounders. RESULTS Of 1376 patients with available smoking status, 504 were current smokers, 358 were former smokers, and 514 were never smokers. Upon adjusting for significant confounders, no association was found between smoking and recurrence outcomes (OR 1.39, 95% CI 0.69–2.80; p = 0.36), thromboembolic and hemorrhagic complications, and mortality among IAs treated with the WEB device. There was no statistically significant difference in outcomes between former and never smokers (OR 1.23, 95% CI 0.70–2.18; p = 0.46). The location of aneurysms differed between smoking groups, with former smokers having more anterior circulation aneurysms compared with current and never smokers (99.0% vs 96.9% vs 95.3%; p = 0.01). In terms of clinical symptoms, headache and dizziness were more common in the never smokers compared with current and former smokers (13.9% vs 8.9% vs 7.7%, p = 0.01). CONCLUSIONS This large-scale study suggests no significant correlation between smoking and the recurrence of IAs treated with the WEB device. Biological studies are warranted to better understand the biological impact of smoking on the growth and rupture of treated IAs.

Background The contact aspiration (CA) technique is often used to perform endovascular thrombectomy (EVT) for acute ischemic stroke (AIS); however, rescue strategies are necessary if CA fails to achieve recanalization. This study investigates the outcomes of incorporating stent retriever (SR) thrombectomy in the rescue strategy following failed CA. Methods EVT patients with failed CA attempts were identified from a large multicenter registry and stratified by rescue technique: CA alone or incorporating SR in the rescue strategy. Outcomes included successful recanalization, 90-day functional outcomes (defined by the modified Rankin Scale (mRS) score), symptomatic intracranial hemorrhage (sICH), and 90-day mortality. Results Among 1885 patients with failed CA attempts, conversion to SR was associated with higher recanalization rates (85.2% vs 80.6%; p=0.03), higher rates of second-pass recanalization (31.2% vs 23.4%; p<0.001), and better 90-day outcomes (mRS 0–2: 35.2% vs 29.9%; p=0.04) when compared with repeated CA attempts. Trevo SRs showed higher odds of successful recanalization (adjusted odds ratio (aOR)=1.9; p=0.02), second-pass recanalization (aOR=1.7; p=0.01), and reduced odds of sICH (aOR=0.3; p=0.02). EmboTrap SRs were associated with higher odds of 90-day mortality (aOR=2.6; p=0.004) and sICH (aOR=2.9; p=0.04) and lower odds of recanalization (aOR=0.5; p=0.03). Conclusions Incorporating SR in the rescue strategy after a failed CA improves recanalization rates and functional outcomes. Trevo SRs demonstrated superior efficacy and safety when incorporated into the rescue strategy.

Introduction/Purpose The multicenter, prospective, single‐arm, controlled, non‐randomized SCENT trial on flow diverter (FD) treatment for intracranial aneurysms (IA) was analyzed for patients’ age and IA characteristics impacting stroke and occlusion rates over 5 years. Materials and Methods Impact on major ipsilateral stroke and IA occlusion was studied by stratifying age as </= 65 years versus >65 years. Twice as many subjects treated were </= 65 years old (n=119/180). Product‐limit (Kaplan‐Meier) estimates of time to both endpoints, stratified by age group, were created. Univariate predictors of time to stroke were identified by including candidate variables in univariable proportional hazards regression models. Those variables found to be significant (p<0.10) at the univariate level were entered into a multivariable survival model to identify independent predictors. The stepwise selection produced a final reduced model with a significance level to both enter and stay set at 0.05. Results When parent artery stenosis and IA size were entered into a multiple‐stepwise survival model, only stenosis remained as an independently significant predictor of time to stroke. At 3‐year follow‐up, there were a total of 23 strokes (12.8%), with 11 (47.8%) occurring in subjects </= 65 years; there was a greater risk for seniors (HR1.96, 95% CI 0.83‐4.78, p=.122). Four patients (4/180; 2.2%) experienced aneurysm ruptured within the first week post‐treatment, with 3 being </= 65 and the fourth aged 66 years. No new strokes were reported between 3 and 5 years. Complete IA occlusion rates for seniors were 60.0% (33/55), 67.6% (25/37), and 85.7% (18/21) at 12, 36, and 60 months, respectively, as compared to 79.1% (87/110), 82.5% (66/80), and 91.8% (56/61) for younger subjects. The time to complete IA occlusion was shorter in younger patients (HR1.53, 95% CI 1.07‐2.19, p=.020). Five subjects (2.8%) underwent retreatment, 2 in 60‐year‐old patients, and one each aged 64, 70, and 75. Conclusion Stroke, early rupture risk, and aneurysm occlusion in patients treated with FD are related to age and pre‐existing parent artery stenosis, IA size, and location. With demographic shifts, future treatments need to focus on expedited and improved healing.

Background: Risk stratification for suspected pulmonary emboli (PE) remains an area of active research. Current risk stratification algorithms have moderate discriminatory capabilities and are guideline-recommended, but they are poorly utilized in practice, with most clinicians relying predominantly on their clinical training and personal “gestalt.” Our group recently derived an Artificial Intelligence (AI) algorithm using electrocardiograms (ECG) to predict the presence of PE (AUC 0.69) on computed tomography scans of the pulmonary arteries (CTPA) in a cohort of 79K patients. Aim: Validate the AI-ECG algorithm in a contemporary dataset of patients with suspected PE. Methods: Emergency Department (ED) visits between 9/1/2022 and 12/31/2023 across the Mayo Clinic Enterprise were searched using a unified data platform. ED visits in patients over 18 years of age with a discretely documented Wells' PE or PERC Rule, a D-dimer result, or a CTPA were included. Only CTPA imaging and D-dimers performed within 48 hours of ED admission were eligible to be included. Patients were included in the final cohort if an ECG was performed within 6 hours of ED admission, D-dimer testing, or CTPA. Patients were considered negative for PE if CTPA was negative, and when this wasn't performed, a negative D-dimer (high sensitivity) or PERC rule of 0 was considered negative. A previously derived Artificial Neural Network algorithm to analyze the ECG to predict PE was applied to the dataset for validation. Results: A total of 27,028 ED visits in 24,288 unique patients meet inclusion criteria. Among this group, the first ED visit with a CTPA and an ECG within 6 hours was included, leaving 18,936 patients for the final validation cohort. The mean age of the cohort was 54.7 (SD 18.7), and 57% were male. Among the entire cohort, 13,573 underwent D-dimer testing for which 4,895 (36%) were positive at standard thresholds. A total of 10,486 patients underwent CTPA (55% of patients evaluated), of which 455 were positive for PE (4.3%). Using the AI-ECG model from the derivation cohort applied to this validation cohort resulted in an AUC of 0.69, identical to the derivation cohort. Using ECG model estimates alone, low (0.74%, n=2447, NPV 99.3%), elevated (2.27%, n=15404, NPV, 97.72%), and high-risk (8.02%, n=1085, NPV 91.98%) categories for PE can be created. A negative D-dimer in the elevated risk group increased the negative predictive value (NPV) to &gt;99.9% and could be used to reduce imaging in this group further. Using the proposed ECG to D-dimer cascade risk stratification could further reduce the need for PE imaging for an overall NPV of 99.9% and sensitivity of 95.2%. We then used additional clinical parameters to train a new algorithm with additional clinical data and the AI-ECG algorithm estimates. Multiple machine learning algorithms were then evaluated in a dataset divided into training (2/3) and testing (1/3) datasets, after downsampling (to address class imbalance). The best results were obtained with AI-ECG results and D-dimer: Ada Boost (AUC 0.93, F1 score 0.895) compared to standard logistic regression (AUC 0.79, F1 score 0.746). The addition of age, sex, and oxygen saturation did not improve model performance. Conclusion: We have now validated the previously derived AI-ANN ECG algorithm for PE in a contemporary and independent cohort and demonstrate potential for integration into clinical PE assessment algorithms. Unlike prior risk stratification processes, ECGs are invariably performed in patients with cardiopulmonary symptoms and have the potential to prevent missed or delayed PE diagnosis by proactively identifying high-risk patients. Additionally, we demonstrate that the results of this algorithm can be combined with D-dimer values, resulting in a simple, powerful, predictive algorithm (AUC 0.93) that pairs cardiovascular electrophysiology with laboratory biomarkers and could lead to a new paradigm in clinical risk stratification for PE.

Background: Many medications, electrolyte perturbations, and several diseases can prolong the QTc beyond its 99 th percentile value resulting in acquired QT prolongation (AQTP). In contrast, approximately 1 in 2,000 people have congenital long QT syndrome (LQTS) hallmarked by pathological QT prolongation. Recently, we developed an AI-ECG model to distinguish these two groups, however this model does not elucidate which ECG segments distinguish LQTS from AQTP. Methods: Using the cohort from our previous study, all patients with LQTS evaluated in the Mayo Clinic Genetic Heart Rhythm Clinic and controls were included. Next, every patient/control with ≥1 ECG above age- and sex-specific 99 th percentile for QTc was selected and matched at 1:100 ratio. Subsequently, 5 ML algorithms (logistic regression, Naïve Bayes, random forest, Gradient boosting and XGBoost) were trained using 52 features representing segments of the 12-lead ECG (e.g. wave peak times, amplitudes, areas under wave, duration; 52x12 features). Training and testing sets were spilt at 80:20 ratio and 5-fold cross validation was performed to prevent overfitting. Following analysis, the top 100 features were re-analyzed to refine results. Results: Among >1,600 patients with LQTS, 690 had ≥ 1 ECG with a QTc above the established threshold compared to 28,186 controls. Following age- and sex-matching and splitting, 22,410 (training), and 5,776 (testing) ECGs were used. Of the 5 ML algorithms, XGBoost demonstrated best performance (AUC 0.913, accuracy 0.836, sensitivity 0.857, specificity 0.835) to distinguish LQTS from AQTP. Following filtering for the top 100 features, performance of the algorithm remained high with AUC of 0.912 (accuracy 0.84, sensitivity 0.841, specificity 0.84). The features with the greatest impact on classification output included time from T wave onset to T wave peak (leads V1, V4, V4), QRS interval duration (leads V1, V3, V4, aVR), and amplitude at end of ST segment (aVF). Conclusions: Distinguishing monogenetically driven LQTS from multi-factorial AQTP is critical in clinical practice for proper management, mitigation of offending QT-prolonging factors, and treatment. Building on our previously developed AI-ECG-LQTS ‘mutation detector’, this model identifies some of the elements within the ‘black box’ that distinguish an ECG and QTc stemming from a patient with LQTS compared to a similar prolonged QTc value that is arising due to non-genetic, acquired QT-prolonging factors.

Background: Acute Kidney Injury (AKI) is a multifactorial complication following repair of Stanford Type A aortic dissection (TAAD) with an alarmingly high incidence, varying from 20 to 77%. Postoperative AKI following life-threatening disease tends to be much more complex. However, the exact role of postprocedural AKI in the prognosis of patients undergoing TAAD repair has not been elucidated. Aims: This meta-analysis aimed to evaluate the prognostic significance of postprocedural AKI in patients undergoing TAAD repair. Methods: A literature search was conducted using PubMed, EMBASE, and SCOPUS databases. The primary endpoint was 30-day mortality with several secondary endpoints. Risk ratios (RR) with 95% confidence intervals (CIs) were pooled using Review Manager software. Statistical significance was set at p <0.05. Random-effects meta-analyses were performed for all outcomes. Results: Our analysis included 21 studies comprising 10396 patients. Patients with AKI exhibited a significantly higher risk of 30-day mortality (RR=3.98; 95% CI: 3.04-5.22; p<0.00001; I 2 =57%), stroke (RR= 2.05; 95% CI: 1.68-2.50; p<0.00001; I 2 =32%), dialysis requirement (RR= 32.9; 95% CI: 10.39-104.24; p<0.00001; I 2 =90%) (Figure 1), cardiovascular complications (RR= 2.85; 95% CI: 1.65-4.92; p<0.0002; I 2 =85%), sepsis (RR= 4.92; 95% CI: 2.62-9.24; p<0.00001; I 2 =64%), and re-exploration for bleeding (RR=2.46; 95%CI: 1.79-3.39; p<0.00001; I 2 =58%) compared to those without AKI (Figure 2). Conclusion: Patients with AKI demonstrated a significantly increased risk of mortality, stroke, need for dialysis, sepsis, postoperative cardiovascular complications, and re-exploration for bleeding. Further prospective studies are required to better understand the prevention strategies and long-term effects on prognosis of AKI post-repair for TAAD.

Abstract Background Existing clinical preoperative risk assessment tools such as the Revised Cardiac Risk Index (RCRI) have been used extensively to predict perioperative major adverse cardiovascular events (MACE). Electrocardiogram (ECG) analysis using Deep Learning can identify hidden features that human eyes cannot identify and might help further risk stratify patients undergoing noncardiac surgery. Objective We aimed to develop a Deep Learning model that predicts perioperative MACE in patients undergoing elective noncardiac surgeries. Methods We included patients ≥ 18-year-old who underwent elective non-cardiac surgery between 2000-2021. MACE was validated in duplicate and were defined as a composite of in-hospital myocardial infarction, cardiac arrest or mortality. A convolutional neural network was developed using ECGs within 30 days before surgery. We randomly split subjects into training, internal validation, and testing datasets in a 7:1:2 ratio. Performance was evaluated using the area under the receiver operator characteristics curve (AUC) values on the testing dataset. Models trained included AI-ECG models to predict in-hospital MACE and to predict in-hospital mortality, and AI-ECG+RCRI models to predict in-hospital MACE and to predict in-hospital mortality. All models were validated using 30-day validated outcomes (MACE or all-cause mortality) in a community-based cohort and were compared with the Revised Cardiac Risk Index (RCRI) score using AUC values. Findings: We included 195,214 patients who underwent a total of 241,999 surgeries. 169,214 ECGs were used in the training dataset, 24,234 in the internal validation dataset, and 48,551 in the testing dataset. In the test cohort, the AI-ECG algorithm discriminated risk for in-hospital MACE with an AUC of 0.82 (95% CI 0.79-0.85). The algorithm similarly discriminated 30-day MACE with an AUC value of 0.79 (95% CI 0.77-0.81) surpassing the discrimination of the RCRI score AUC of 0.69 (95% CI 0.66-0.72). All models’ performances are shown in Figure1A. The AI-ECG model to predict in-hospital mortality had an AUC of 0.83 (95% CI 0.80-0.86) and predicted 30-day mortality with an AUC of 0.80 (95% CI 0.77-0.82). Other models’ performances are shown Figure1B. Training the AI-ECG models including RCRI data did not improve the AI-ECG models’ performance. Conclusion AI-ECG models with no additional clinical data can improve perioperative risk prediction in non-cardiac surgery, outperforming a conventional and widely used risk stratification tool such as the RCRI.

AbstractBackgroundThe clinical relevance of cardiac troponin (cTn) elevation in takotsubo syndrome (TTS) remains uncertain. The present study sought to investigate the role of cardiac troponin (cTn) elevations in mortality prediction of patients with Takotsubo syndrome (TTS).MethodsPatients enrolled in the International Takotsubo (InterTAK) Registry from January 2011 to February 2020 with available data on peak cTn levels were included in the analysis. Peak cTn levels during the index hospitalization were used to define clinically relevant myocardial injury. The threshold at which clinically relevant myocardial injury drives mortality at 1 year was identified using restricted cubic spline analysis.ResultsOut of 2′938 patients, 222 (7.6%) patients died during 1‐year follow‐up. A more than 28.8‐fold increase of cTn above the upper reference limit was identified as threshold for clinically relevant myocardial injury. The presence of clinically relevant myocardial injury was significantly associated with an increased risk of mortality at 5 years (adjusted HR 1.58, 95% CI 1.18–2.12, p =.002). Clinically relevant myocardial injury was related to an increased 5‐year mortality risk in patients with apical TTS (adjusted HR 1.57, 95% CI 1.21–2.03, p =.001), in presence of physical stressors (adjusted HR 1.60, 95% CI 1.22–2.11, p =.001), and in absence of emotional stressors (adjusted HR 1.49, 95% CI, 1.17–1.89, p =.001).ConclusionThis study for the first time determined a troponin threshold for the identification of TTS patients at excess risk of mortality. These findings advance risk stratification in TTS and assist in identifying patients in need for close monitoring and follow‐up.

OBJECTIVE The Woven EndoBridge (WEB) device was approved to treat wide-necked bifurcation aneurysms. The device is designed as an intrasaccular flow disruptor covering aneurysm widths up to 10 mm. Although prior studies combined all aneurysm sizes, it is known that aneurysms behave differently in response to endovascular treatment based on their size. Therefore, the authors’ objective was to identify ideal middle cerebral artery (MCA) aneurysm width and neck sizes most suitable for WEB treatment. METHODS The WorldWideWEB consortium is a large multicenter retrospective database that analyzes intracranial aneurysms treated with the WEB device. In this study, all unruptured MCA bifurcation aneurysms with available measurements were included. Cutoff values based on aneurysm width and neck in relation to aneurysm occlusion status were measured using the receiver operating characteristic (ROC) curve. Propensity score matching (PSM) was then used to compare treatment outcomes between aneurysms smaller and larger than the cutoff value for both width and neck size. RESULTS The ideal cutoff values for MCA bifurcation aneurysm width and neck were 6.1 mm and 4.6 mm, respectively. On PSM, 87 matched pairs were compared based on width size (≤ 6.1 mm and > 6.1 mm), and 77 matched pairs were compared based on neck size (≤ 4.6 mm and > 4.6 mm). There was a significant difference in adequate aneurysm occlusion between aneurysms smaller and larger than those cutoff values for both widths (93% vs 76%, p = 0.0017) and neck sizes (90% vs 70%, p = 0.0026). The retreatment rate was also significantly higher for larger aneurysms in both parameters. CONCLUSIONS This study shows that MCA bifurcation aneurysms ≤ 6.1 mm in width and ≤ 4.6 mm in neck size are significantly better candidates for WEB treatment, leading to improved occlusion status and reduced retreatment rate, which are important considerations when using WEB devices.