McBane, Robert D.

Abstract Patients with cancer and venous thromboembolism (VTE) have higher complication rates of anticoagulation. However, studies supporting this data are relatively old reflecting the era of vitamin K inhibitors. To assess VTE recurrence, major bleeding, and clinically relevant non-major bleeding (CRNMB) in patients (March 1, 2013 to April 30, 2023) with cancer-associated VTE and patients without cancer, the prospective Mayo Clinic Thrombophilia Clinic Registry was analyzed. Over the study period, 4,711 patients with acute VTE were enrolled including 2,064 patients with cancer (mean age 62.5 ± 12.4, 46% female) and 2,647 patients without cancer (mean age 59.4 ± 16.0, 45% female). The most common cancers were gastrointestinal (n = 423, 21%), pancreatic (n = 287, 14%), and genitourinary (n = 198, 10%). Direct oral anticoagulants were used in 1,339 (65%) cancer and in 1,952 (74%) non-cancer patients. Among cancer patients, 12-month Kaplan–Meier probability of VTE recurrence was 2.4-fold greater (7.1% vs. 2.9%, p < 0.001) compared with the non-cancer group, including higher recurrent leg deep vein thrombosis (3.0% vs. 1.4%; p = 0.002) and pulmonary embolism (3.5% vs. 1.0%, p < 0.001). Patients with cancer also had 2.1-fold greater risk of major bleeding (6.3% vs. 3.0%; p < 0.001) including bleeding from the gastrointestinal tract (3.0 vs. 1.4, p = 0.01) compared with patients without cancer. Clinically relevant non-major bleeding events were similar between the groups. In this large prospective registry of VTE management, patients with cancer had significantly higher rates of VTE recurrence and major bleeding compared with patients without cancer, yet the rate of complications are substantially smaller relative to historic values of a prior vitamin K antagonist era.

Background: The calf muscle pump is an understudied contributor to venous return from the lower extremity. This study aimed to determine if calf pump function (CPF) is independently associated with the severity of chronic venous disease classified by CEAP (Clinical-Etiology-Anatomy-Pathophysiology). Methods: The Mayo Clinic Vascular Laboratory database was analyzed from January 2015 through September 2023. Ambulatory adults who underwent venous air plethysmography were included. Venous plethysmography assessed the severity of venous incompetence, and CPF was measured as ejection fraction (EF) per leg. The clinical component (C0 through C6) of the CEAP score was evaluated for each extremity at the time of the study. Results: A total of 7760 limbs from 3733 patients were analyzed. The mean age was 62 years, with 62% women. Venous obstruction was detected in 3.2% of limbs. Venous incompetence severity was categorized as normal (44%), mild (26%), moderate (19%), and severe (10%). A significant trend of reduced CPF was observed with higher CEAP scores ( p < 0.001). Multivariable logistic regression, adjusted for age, sex, degree of venous incompetence, and obstruction showed reduced CPF was a significant predictor (odds ratio 1.84, CI: 1.5–2.2) of active/prior ulcer (C5 or C6). In contrast to more severely reduced CPF, mildly reduced CPF (EF 40–49%) was not associated with active/prior ulcers. Conclusion: This large contemporary study demonstrates that decreased CPF is associated with worse chronic venous disease. Importantly, we demonstrate for the first time that CPF is independently associated with active/prior venous ulcers after accounting for other venous physiologic parameters and demographics.

Hemorrhagic complications associated with regional anesthesia are extremely rare. The fifth edition of the American Society of Regional Anesthesia and Pain Medicine’s Evidence-Based Guidelines on regional anesthesia in the patient receiving antithrombotic or thrombolytic therapy reviews the published evidence since 2018 and provides guidance to help avoid this potentially catastrophic complication. The fifth edition of the American Society of Regional Anesthesia and Pain Medicine’s Evidence-Based Guidelines on regional anesthesia in the patient receiving antithrombotic or thrombolytic therapy uses similar methodology as previous editions but is reorganized and significantly condensed. Therefore, the clinicians are encouraged to review the earlier texts for more detailed descriptions of methods, clinical trials, case series and pharmacology. It is impossible to perform large, randomized controlled trials evaluating a complication this rare; therefore, where the evidence is limited, the authors continue to maintain an ‘antihemorrhagic’ approach focused on patient safety and have proposed conservative times for the interruption of therapy prior to neural blockade. In previous versions, the anticoagulant doses were described as prophylactic and therapeutic. In this version, we will be using ‘low dose’ and ‘high dose,’ which will allow us to be consistent with other published guidelines and more accurately describe the dose in the setting of specific patient characteristics and indications. For example, the same ‘high’ dose may be used in one patient as a treatment for deep venous thrombosis (DVT) and in another patient as prophylaxis for recurrent DVT. Due to the increasing ability to obtain drug-specific assays, we have included suggestions for when ordering these tests may be helpful and guide practice. Like previous editions, at the end of each recommendation the authors have clearly noted how the recommendation has changed from previous editions.

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: Exercise stress testing uses metabolic equivalents of tasks (METs) to measure the energy cost of activities, aiding in the assessment of exercise capacity and cardiovascular health. Despite its significance, the correlation between calf muscle pump function (CPF) and exercise stress testing remains unexplored. We aimed to evaluate the relationship between CPF and peak METs as determined by cardiopulmonary treadmill exercise stress testing. Methods: The study included adults who underwent exercise cardiopulmonary stress testing and venous plethysmography at Mayo Clinic between April 2017 and March 2020. The protocols other than Bruce, Mayo, Modified Naughton, and Naughton protocols were excluded. The CPF ejection fraction (EF) was calculated per leg based on refill volumes post-exercise as a percentage of passive drain refill. The classification of CEAP (Clinical-Etiology-Anatomy-Pathophysiology) was utilized to better understand chronic venous insufficiency (CVI). Results: A total of 155 patients who underwent both exercise stress testing and venous plethysmography were included, with a mean age of 61.31 ± 14.03 years, and 84 (54.2%) were male. The peak measured METs for normal, unilaterally reduced, and bilaterally reduced CPF were 8.5 (2.5), 7.3 (2.1), and 7.1 (2.4), respectively (p=0.004, Figure 1). Multiple linear regression models were developed with METs as the outcome to determine if CPF was an independent predictor of METs on cardiopulmonary exercise stress testing. IIn model 1, the following independent variables were included: resting heart rate, peak heart rate, peak systolic blood pressure, recovery heart rate at minute 1, and worst EF (Table 1). In model 1, with only exercise parameters, lower EF was associated with lower METs (p=0.03). In a second analysis, variables identified as statistically significant with METs in the initial model were included, along with CEAP class (model 2) and CCI (model 3) (Table 2). In model 2, CEAP class 3 or higher was associated with decreased METs on the exercise stress test. This correlation implies that individuals with moderate to severe CVI may influence exercise capacity, demonstrating the interconnectedness of the cardiovascular system. Moreover, in model 3, the CCI, a predictor for mortality, was not significantly associated with METs. Conclusion: Our findings revealed that more severe CVI (CEAP class and reduced CPF) was associated with reduced exercise capacity after accounting for other factors.

Background The study aimed to describe the patterns and trends of initiation, discontinuation, and adherence of oral anticoagulation (OAC) in patients with new‐onset postoperative atrial fibrillation (POAF), and compare with patients newly diagnosed with non‐POAF. Methods and Results This retrospective cohort study identified patients newly diagnosed with atrial fibrillation or flutter between 2012 and 2021 using administrative claims data from OptumLabs Data Warehouse. The POAF cohort included 118 366 patients newly diagnosed with atrial fibrillation or flutter within 30 days after surgery. The non‐POAF cohort included the remaining 315 832 patients who were newly diagnosed with atrial fibrillation or flutter but not within 30 days after a surgery. OAC initiation increased from 28.9% to 44.0% from 2012 to 2021 in POAF, and 37.8% to 59.9% in non‐POAF; 12‐month medication adherence increased from 47.0% to 61.8% in POAF, and 59.7% to 70.4% in non‐POAF. The median time to OAC discontinuation was 177 days for POAF, and 242 days for non‐POAF. Patients who saw a cardiologist within 90 days of the first atrial fibrillation or flutter diagnosis, regardless of POAF or non‐POAF, were more likely to initiate OAC (odds ratio, 2.92 [95% CI, 2.87–2.98]; P <0.0001), adhere to OAC (odds ratio, 1.08 [95% CI, 1.04–1.13]; P <0.0001), and less likely to discontinue (odds ratio, 0.83 [95% CI, 0.82–0.85]; P <0.0001) than patients who saw a surgeon or other specialties. Conclusions The use of and adherence to OAC were higher in non‐POAF patients than in POAF patients, but they increased over time in both groups. Patients managed by cardiologists were more likely to use and adhere to OAC, regardless of POAF or non‐POAF.

Introduction: Abdominal aortic aneurysms (AAA) are common among individuals over age 65 with a prevalence of 9%. Most AAAs are small and require surveillance imaging to monitor size and growth rates. Tools capable of predicting growth rates would be clinically useful. Hypothesis: We investigated how computational hemodynamics derived from CT angiography (CTA) differentiated slow and rapid-growing AAAs. Methods: Using readily available computing power in the clinical workflow, 3D models were generated for 25 subjects with AAA from available 3D CTA data. AAA growth rates were categorized as slow ( less than 5 mm/year) or rapid (greater than 5 mm/year) based on serial imaging. A geometrical analysis was performed using 7 indices (Volume, Surface Area, Length, Max/Min radius, Expansion Ratio [Mean radius/vessel radius], Proximal vessel area, Voronoi diagram-based metrics) for each AAA. Computational hemodynamics were performed using commercial software (ANSYS-Fluent). Computational hemodynamics were completed for all cases (see Fig. 1). In-house software assessed wall shear stress-(WSS) and vortex-based hemodynamics. Support vector machine (SVM) was run with cross-validation (50 iterations) to assess predictive strength using these 16 parameters. Results: Among 25 AAAs studied, 7 were rapid and 18 were slow-growing. The combination of proximal vessel area, spatiotemporally averaged WSS, and spatially averaged OSI provided the best accuracy for growth differentiation. The area under receiving operating curve (AUROC) and total accuracy are 0.770 and 0.765, respectively, with 48% and 86% of rapid and slow-growing AAAs correctly identified, respectively. Conclusions: Computational hemodynamics appears feasible with reasonable growth rate prediction. With optimization, this tool may enable resource utilization refinement for imaging surveillance strategies by distinguishing rapid from slow-growing AAAs.