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We present an 81-year-old male with an extensive past medical history and an enlarging abdominal aortic aneurysm (AAA). Axial imaging showed dense aortoiliac calcification with a complete occlusion of the right common iliac and high-grade stenosis of the left common iliac artery (CIA). Shockwave Intravascular Lithotripsy and the pave-and-crack technique utilizing an endoconduit were used to dilate the left external iliac artery and the CIA to facilitate placement of an aortic stent graft and exclude the patient’s AAA. This method gives surgeons the ability to treat patients with AAAs endovascularly, despite a heavily diseased and/or diminutive iliac anatomy. We also present a contemporary literature review of the utilization of the pave-and-crack technique as well as tips and tricks of how we do it.

Abstract INTRODUCTION We investigated the association between alpha‐synuclein (α‐syn) pathology and brain glucose metabolism across the cognitive spectrum of Alzheimer's disease (AD) co‐pathologies. METHODS Fluorodeoxyglucose positron emission tomography (FDG‐PET) data from 829 Alzheimer's Disease Neuroimaging Initiative participants (648 cognitively impaired [CI], 181 unimpaired [CU]) were compared between α‐syn seed amplification assay (SAA) positive and negative groups. Interactions with cerebrospinal fluid (CSF) AD biomarkers were examined. RESULTS SAA+ was associated with widespread hypometabolism among CI individuals, particularly in posterior cortical regions, independent of CSF amyloid and tau levels in the occipital lobes. Regional hypometabolism mediated the effect of α‐syn SAA on disease severity in CI individuals, independent of CSF amyloid and tau levels. There were no influences of SAA on FDG‐PET in CU individuals. DISCUSSION This study supports a model in which α‐syn aggregation influences metabolic dysfunction, which then influences clinical disease severity, independent of AD. SAA+ could help optimize participant selection and outcome measures for clinical trials in AD. Highlights α‐synuclein seed amplification positivity (SAA+) was associated with hypometabolism in cognitively impaired individuals. Hypometabolism mediated the influence of α‐synuclein on disease severity. Occipital hypometabolism in SAA+ was independent of cerebrospinal fluid levels of Alzheimer's disease pathology. These findings can optimize future clinical trials targeting α‐synuclein pathology.

The Enhancing the Quality and Transparency of Health Research (EQUATOR) Network indexes over 600 reporting guidelines designed to improve the reproducibility of manuscripts across medical fields and study designs. Although several such reporting guidelines touch on adverse events that may occur in the context of a study, there is a large body of research whose primary focus is on adverse events, near-misses and medical errors that do not currently have a dedicated reporting guideline to help set reporting standards and facilitate comparisons across studies. As part of the process prescribed by EQUATOR for developing such a reporting guideline, we performed a needs assessment, evaluating whether existing standards address key features of a proposed reporting guideline in development, entitled Standard Elements in Studies of Adverse Events and Medical Error (SESAME). We evaluated 12 EQUATOR reporting guidelines for the presence of eight key features of SESAME. Five of the 12 failed to include any of these key features. None of the remaining seven incorporated more than four of the eight SESAME key components, confirming the need for a dedicated reporting guideline for studies of adverse events and medical errors.

ABSTRACTBackgroundThe factors contributing to sustained ventricular arrhythmias (VAs) >30 days after left ventricular assist device (LVAD) implantation are not well‐established.ObjectivesThis study aimed to predict the factors associated with late sustained VAs after LVAD implantation and develop a predictive score to identify patients with an increased risk of late sustained VAs after the procedure.MethodsWe performed a retrospective cohort study on a training cohort of 623 patients and a validation cohort of 157 patients who underwent LVAD implantation at the Mayo Clinic (Rochester, Phoenix, and Jacksonville) from January 1, 2000, to December 30, 2020. Late sustained VAs were defined as ventricular tachycardia (VT) and ventricular fibrillation (VF) occurring >30 days after the LVAD procedure. Detailed chart reviews of the electronic health records within the Mayo Clinic and outside medical records were performed.ResultsA total of 780 patients were included in our study. Late sustained VAs occurred in 30% (n = 232) of the patients. The significant factors associated with late sustained VAs after LVAD were the history of VAs prior to LVAD (34.1% vs. 23.0%, p < 0.01), implantable cardiac defibrillator (ICD) (87.9% vs. 77.6%, p < 0.01) or cardiac resynchronization therapy (CRT) in situ (43.5% vs. 33.6%, p = 0.008), VT ablation prior to LVAD (5.2% vs. 1.8%, p = 0.010), use of amiodarone (49.1% vs. 38.7%, p = 0.007), use of mexiletine (15.5% vs. 5.7%, p < 0.01), and higher left ventricular end‐diastolic diameter (LVEDD) on echocardiography prior to LVAD implantation (71.4 vs. 68.7 mm, p = 0.002). During follow‐up, the patients who developed late sustained VAs after LVAD had lower survival than those who did not (HR = 1.96, 95% CI:156–2.4, p < 0.001). The average time from LVAD to orthotopic heart transplant was longer among late sustained VAs patients (23 vs. 14 months, p < 0.01). The “VIN” risk score was created to identify four risk groups: low (scores 0), intermediate (score 1), high (score 2), and very high (score 3). The rates of late VA's in the training cohort at 1 year were 9.5%, 14%, 18%, and 25% which was almost similar to the risk of late VA's at validation cohort 10%, 12%, 20%, and 63%, respectively.ConclusionsLate sustained VAs occurred in 30% of the patients who received LVAD and were associated with lower survival. The VIN risk score was developed and validated to stratify patients into low, intermediate, high, and very high risk of late sustained VAs.

Abstract The design of clinical trials in Alzheimer's disease (AD) must consider the development of new plasma, cerebrospinal fluid (CSF), and imaging biomarkers. They must also define clinically meaningful outcomes for patients and set endpoints that measure these outcomes accurately. With the accelerated United States Food and Drug Administration (FDA) approval of the first anti‐amyloid, disease‐modifying treatment for AD, a monoclonal antibody called aducanumab, the landscape of clinical trial design is evolving. Enrolment in clinical trials may be impacted by the availability of this and other treatments, and trial design must take into consideration that patients may desire a disease‐modifying treatment rather than potentially being randomized to the placebo arm. The Alzheimer's Association Research Roundtable (AARR) Fall 2021 meeting discussed the consideration of well‐defined AD staging criteria in protocol design and how they influence more standardized inclusion/exclusion criteria for trials, as well as what constitutes meaningful differentiation between the stages. Discussion explored the current state of knowledge regarding biomarkers and how they can inform AD staging criteria, as many trials are now designed based on specific biomarker features, further underscoring the importance of coordinating AD staging criteria and biomarkers. The relationship between cognition and biomarkers has been studied and this must continue as trials move forward. Researchers, patients, clinicians, regulatory scientists, and payers discussed the state of the field as well as the future of symptomatic Alzheimer's disease clinical trials. Highlights The Alzheimer's Association Research Roundtable (AARR) convened leaders from academia and industry as well as patients, care partners, clinicians, regulators, and payers to discuss the topic of operationalizing selection criteria for clinical trials and the role of biomarkers. Well‐defined Alzheimer's disease (AD) staging criteria are an important consideration in study protocol design. Staging criteria and biomarkers must be coordinated to yield high‐quality clinical trial results that have meaning for patients with AD by selecting a population most likely to benefit from a specific treatment.

ABSTRACTIntroductionLung transplantation has been evolving since its inception in 1963. Over recent years, literature has suggested a shift in the perioperative strategy of mechanical support toward extracorporeal membrane oxygenation (ECMO) as the preferred modality of extracorporeal life support (ECLS) in lung transplantation. The Survey of ECLS Application in Lung transplantation (SEAL) was designed to elucidate the current practice patterns of perioperative ECLS within the United States.MethodsIn this cross‐sectional study, a physician from 62 adult lung transplantation centers across the United States of America (USA) was surveyed on their institutional practices regarding the perioperative management of lung transplantation patients, with a focus on mechanical support.ResultsThe survey completion rate was 74% (46/62 eligible institutions). Most transplant centers utilize venoarterial (VA) ECMO (78%, 36/46) and/or venovenous (VV) ECMO (93%, 29/43) as a bridge to lung transplantation. When ECLS is used intraoperatively, 61% of (28/46) responding programs use VA ECMO as their preferred support. All programs use transesophageal echocardiography (TEE) intraoperatively, 85% (33/39) cannulate for ECMO centrally, and 74% (29/39) use a combination of inhaled and intravenous anesthesia while using ECMO intraoperatively. Most programs do not use antifibrinolytic during VA ECMO (62%, 28/45). Anticoagulation management and VA ECMO flows throughout the procedure showed considerable variation.ConclusionData from SEAL illustrates that though there are some practice commonalities within the United States, there is also quite a bit of variability in practice. Multiple dominant practices within the USA are consistent with a recently published International Society of Heart and Lung Transplantation consensus.

Abstract Purpose Developing large‐scale, standardized radiographic registries for anterior cruciate ligament (ACL) injuries with artificial intelligence (AI) tools can enhance personalized orthopaedics. We propose deploying Artificial Intelligence for Knee Imaging Registration and Analysis (AKIRA), a trio of deep learning (DL) algorithms, to automatically classify and annotate radiographs. We hypothesize that algorithms can efficiently organize radiographs based on laterality, projection, identify implants and classify osteoarthritis (OA) grade. Methods A collection of 20,836 knee radiographs from all time points of treatment (mean orthopaedic follow‐up 70.7 months; interquartile range [IQR]: 6.8–172 months) were aggregated from 1628 ACL‐injured patients (median age 26 years [IQR: 19–42], 57% male). Three DL algorithms (EfficientNet, YOLO [You Only Look Once] and Residual Network) were employed. Radiograph laterality and projection (anterior‐posterior [AP], lateral, sunrise, posterior‐anterior, hip–knee–ankle and Camp‐Coventry intercondylar [notch]) were labelled by a DL model. Manually provided labels of metal fixation implants were used to develop a DL object detection algorithm. The degree of OA, both as measured by specific Kellgren–Lawrence (KL) grades, as well as based on a binarized label of OA (defined as KL Grade ≥2), on standing AP radiographs were classified using a DL algorithm. Individual model performances were evaluated on a subset of images prior to the deployment of AKIRA to registry construction using all ACL radiographs. Results The classification algorithms showed excellent performance in classifying radiographic laterality (F1 score: 0.962–0.975) and projection (F1 score: 0.941–1.0). The object detection algorithm achieved high precision–recall (area under the precision‐recall curve: 0.695–0.992) for identifying various metal fixations. The KL classifier reached concordances of 0.39–0.40, improving to 0.81–0.82 for binary OA labels. Sequential deployment of AKIRA following internal validation processed and labelled all 20,836 images with the appropriate views, implants, and the presence of OA within 88 min. Conclusion AKIRA effectively automated the classification and object detection in a large radiograph cohort of ACL injuries, creating an AI‐enabled radiographic registry with comprehensive details on laterality, projection, implants and OA. Study Design Cross‐sectional study. Level of Evidence Level IV.