Citations

Aim. Thromboembolic presentation (stroke, mesenteric or limb ischemia) in non-valvular atrial fibrillation may occur randomly or may identify unique subpopulations of patients with this dysrhythmia. The aim of this study was to determine whether future major adverse cardiovascular event rates (myocardial infarction, stroke, peripheral arterial embolism, death) differ between patients with peripheral embolism (AFPE) compared to those suffering stroke (AFS). Methods. An inception cohort (1995–2005) with AFPE (n=216) were compared to randomly selected AFS patients (n=241). Results. Patients with AFPE were older than AFS patients (79±10 vs 73±13 years; p<0.001) with a greater percentage of women (71% vs 45%; p<0.001). The most common presentation of AFPE patients was limb ischemia (90%). Except for prior stroke, all CHADS-2 variables were more prevalent in AFPE (Table ). During 3.3±3.6 years of follow up, survival was lower in AFPE compared to either AFS (p<0.001) or age/gender matched controls (p<0.001; Figure ). AFPE patients had lower survival free from recurrent embolization compared to AFS (82% vs 100%; p=0.005). Recurrence free survival for MI and stroke were similar. Conclusions. Patients with peripheral embolization represent a unique subset of non-valvular atrial fibrillation with an increased risk of recurrence and high mortality rates compared to those suffering stroke. Comparison of clinical and echocardiographic characteristics

Background and Aim: Cardioembolic stroke from non-valvular atrial fibrillation (NVAF) begins with the development of left atrial appendage thrombus (LAAT). Although nearly 15% of patients with NVAF have LAAT by transesophageal echocardiography, the annual stroke rate is 5% on average. The overall aim of this project is to identify variables associated with embolic propensity of LAAT. Materials and Methods: LAAT specimens were obtained during cardiac surgery (MAZE procedure; n=26). Embolized thrombi (n=51) were obtained during surgical embolectomy from lower (n=36) and upper extremities (n=11) and mesenteric arteries (n=4). Platelet-rich regions within sectioned thrombi were sampled by punch biopsy from which total RNA was extracted and analyzed for expression of 5 platelet specific genes: A1 domain of factor XIII, (F13A1); integrin α 2b β 3 (ITGA2B); glycoprotein IX (GP9), platelet factor 4 (PF4), glycoprotein Ib (GP1BB). Gene expression was quantified using TaqMan MGB-probe based quantitative real-time polymerase chain reaction normalized to β 2 macroglobulin using cDNAs reverse transcribed from total RNA. Results: Factor XIII gene expression (Figure ) was significantly lower in embolic (mean fold change 0.08) compared to left atrial thrombi in situ (mean fold change 0.28; p=0.03). Expression of other genes did not show significant change by embolic status. Conclusions: Variable Factor XIII gene expression in thrombi generated during NVAF may in part explain the propensity to embolization. Whereas Factor XIII stabilizes thrombi by crosslinking fibrin without influencing atrial attachment, this data suggests that embolization occurs through fragmentation.

Background Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all‐cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer‐assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events. Methods and Results Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle–brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all‐cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow‐up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78–3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49–2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43–22.39]) at 5 years. Conclusions An artificial intelligence–enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.

Pulmonary embolism (PE) is the third most common cause of cardiovascular death and necessitates prompt, accurate risk assessment at initial diagnosis to guide treatment and reduce associated mortality. Intermediate-risk PE, defined as the presence of right ventricular (RV) dysfunction in the absence of hemodynamic compromise, carries a significant risk for adverse clinical outcomes and represents a unique diagnostic challenge. While small clinical trials have evaluated advanced treatment strategies beyond standard anticoagulation, such as thrombolytic or endovascular therapy, there remains continued debate on the optimal care for this patient population. Here, we review the most recent risk stratification models, highlighting differences between prediction scores and their limitations, and discuss the utility of serologic biomarkers and imaging modalities to detect right ventricular dysfunction. Additionally, we examine current treatment recommendations including anticoagulation strategies, use of thrombolytics at full and reduced doses, and utilization of invasive treatment options. Current knowledge gaps and ongoing studies are highlighted.

Introduction: Predictive algorithms using multiple clinical variables can identify patients at higher risk for abdominal aortic aneurysms (AAA), however they are cumbersome and cannot used directly by patients. A simple, cheap, noninvasive, and readily available (virtual) screening tool for identification of AAA could aid in screening algorithms. Aims: We sought to determine if a machine learning algorithm could predict the presence of an AAA by using a image of the face Methods: Diagnostic imaging studies were extracted from the electronic health record (EHR) from 5/7/2018 through 1/1/2023 and analyzed by a natural language processing algorithm previously validated to identify abdominal aortic aneurysms. Patient EHR profile pictures were extracted and matched to imaging studies. Various deep neural network (DNN) architectures were explored, all trained as classifiers on the face images along with clinical variables to predict clinical outcomes. All models used were CNNs with an EfficientNet architecture. Model performance was evaluated by standard metrics such as the area under the curve (AUC), specificities (Sp), and sensitivities (Sn). All performance metrics reported results from the test subset of the primary dataset and did not contain any observations considered during training. Results: A total of 5522 patients with facial images and AAA diagnostic studies were analyzed, among whom 1314 (23.8%) had a AAA. The optimal model had an AUC of 0.72 (Sn 0.44, Sp 0.83) for predicting AAA and remained similar in women (AUC 0.7), men (AUC 0.65), age >65 (AUC 0.63), and age <=65 (AUC 0.72) (Table). Conclusions: Using only an image of a patients face, the presence of an AAA could be identified with moderate performance in men, women, and in those less than 65. The model’s performance in demographics not typically screened for AAA may allow for targeted screening programs in these groups.

Introduction: Pulmonary vein thrombosis (PVT) is often diagnosed incidentally but data on its management and outcomes are sparse. Methods: We queried our institutional health record to identify patients ≥18 years of age diagnosed with PVT. Demographics, medications, risk factors, diagnostics, treatment, and sequelae were collected by manual chart review. Suspected tumor thrombus cases were excluded. Results: Seventy-two patients with PVT were identified (median age 62 years, 50% male). The cohort was mostly white (85%), overweight (BMI 26 kg/m), with frequent comorbidities including hypertension (44%), hyperlipidemia (38%), atrial fibrillation/flutter (29%), and history of venous thromboembolism (21%). The most common PVT risk factors were active malignancy (54%), lobectomy (21%), surgery within 30 days (19%), and extrinsic vein compression (15%). Most PVT were diagnosed by CT (86%) and affected a single vein (86%), mostly commonly the left upper PV (32%). Laboratory studies revealed median platelet count 243 х 10 9 cells/L (n=58) and D-dimer elevated in 6 patients when measured (67%, n=9). About one-third of patients were on therapeutic anticoagulation (37%) at PVT diagnosis. Most patients were started on new anticoagulation (78%, n=56), with unfractionated heparin as the most common initial therapy (68%). Of those anticoagulated, few were treated with a limited duration (20%, n=11, median 54 d, IQR 95) while most received indefinite anticoagulation (80%, n=45), primarily warfarin (45%), apixaban (24%), or enoxaparin (21%). Serial imaging was obtained in 69% (n=50, median 65 d, IQR 116) and demonstrated resolution of PVT in 32 (64%) patients. Common PVT complications noted were as follows: 4 year risk of LA thrombus (26%, 12/16 anticoagulated), mechanical ventilation (13%), pneumonia (9%), and ischemic stroke (9%, 5/6 anticoagulated). Conclusions: PVT is often associated with active malignancy, lobectomy, recent surgery, and extrinsic vein compression. Most PVT resolve on serial imaging. Clinicians should be aware of potential high-risk sequelae including LA thrombus and stroke as well as high anticoagulation failure rates when managing PVT.

Intro: Venous thromboembolism (VTE) is a common cause of death among cancer patients, second only to cancer progression. Identifying factors predicting worse outcomes among cancer patients with VTE would be useful clinically. For cancer patients with pulmonary emboli (PE), it remains unclear whether concomitant leg deep vein thrombosis (DVT) as a source for further embolization adversely impacts this risk. The aim of this project was to determine the impact of residual DVT on adverse outcomes among cancer patients suffering acute PE. Methods: Consecutive patients with confirmed acute PE (March 1, 2013 - June 30, 2021) underwent ultrasound (US) imaging and were divided into two groups depending on the presence or absence of leg DVT. PE severity was classified based on AHA criteria. Patients were followed prospectively for VTE recurrence, major and clinically relevant non-major bleeding (CRNMB) and all-cause mortality. Results: Over the study interval, 1,035 cancer patients with PE were enrolled (Table 1). Of these, 375 (36.2%) had leg DVT confirmed by US. Patients with leg DVT were older (64.2 vs 61.4 years, p = 0.01), heavier (90.2 vs 85.1 kg, p &lt;0.001), with lower metastatic disease prevalence (49.1% vs 57.1%, p = 0.01) compared to those without leg DVT. Cancer type distribution also differed between groups. PE patients with DVT more often had prostate, genitourinary, and hematologic cancers while those without DVT more often had pancreatic and breast cancers. Patients with DVT more often had symptomatic PE (48.2% vs. 35.9%, p &lt; 0.001), massive (2.9% vs 0.5%, p &lt; 0.001), and submassive (22.0% vs 7.6%, p &lt; 0.001). Patients with DVT more often received unfractionated heparin (39.5% vs 18.7%, p &lt; 0.001) whereas those without DVT were more frequently treated with low molecular weight heparin (57.5% vs. 44.3%, p &lt;0.001), and apixaban (16.4% vs. 11.7%, p &lt; 0.05). Anticoagulant duration, VTE recurrence, major bleeding and CRNMB were similar between groups (Table 2). Conclusion: Among a large cohort of cancer patients with pulmonary emboli, significant clinical and cancer specific differences exist comparing those with and without leg DVT.While theoretically increasing the risk of recurrent embolization, the presence of residual DVT does not appear to adversely impact important outcomes among cancer patients adequately treated with anticoagulation.

Purpose/Introduction: Patients with cancer and venous thromboembolism (VTE) have higher complication rates including thrombosis recurrence and bleeding. Real world prospective clinical outcome estimates of VTE management comparing cancer and non-cancer patients are limited. To assess VTE recurrence, major bleeding, and clinically relevant non-major bleeding (CRNMB) in patients with cancer and without cancer, the prospective Mayo Clinic Thrombophilia Clinic Registry was analyzed. Methods: Upon recruitment, consecutive patients with confirmed acute VTE (03/01/2013 - 04/30/2023) were treated in a standardized, guideline-sanctioned, protocol driven strategy incorporating shared patient-decision making. Patients were divided into groups based on cancer status. After enrollment, patients were actively followed at 3 month intervals, in person whenever feasible, by mailed questionnaire or scripted phone interview to assess vital status, medication compliance, VTE recurrence, major bleeding and CRNMB. Results: Over the study time-frame, 2,064 patients (53.8% male, 46.2% female) with a cancer and 2,647 patients (54.9% male, 45.1% female) without cancer were enrolled. The most common cancers were gastrointestinal (n=423, 20.5%), pancreatic (n=287, 13.9%), genitourinary (n=198, 9.6%), hematologic (n=171, 8.3%), and lung cancer (n=170, 8.2%). Patients with cancer were older, had lower weight and lower platelet counts compared to non-cancer patients (Table1). Pulmonary embolism (PE, 52.6% vs 43.7%, p&lt;0.001), upper extremity DVT (9.1% vs 6.0%, p&lt;0.001), and splanchnic DVT (11.1% vs 6.9%, p&lt;0.001) were more frequent among cancer patients. In contrast, leg DVT was more frequent among non-cancer patients (65.4% vs.47.2%, p&lt;0.001). While mean duration of anticoagulation was similar between groups, notable differences in 3 month and &gt; 9 month durations were evident as were initial anticoagulant choices (Table 1). Despite a well-organized protocol driven and guideline sanctioned management strategy, patients with active cancer experienced a 2.2-fold higher rate of VTE recurrence (p&lt;0.001) and a 1.8 fold higher rate of major bleeding (p&lt;001) compared to non-cancer patients (Table 2). CRNMB rates did not differ by cancer status. Conclusions: In this large prospective, guideline-driven and patient-centric registry of VTE management, patients with cancer had significantly higher rate of VTE recurrence and major bleeding, compared to non-cancer patients. These data provide important estimates for power calculations for future randomized trials of VTE treatment.

Background: Reduced or low-dose direct oral anticoagulants (DOACs) have been studied in randomized control trials for the extended prevention of venous thromboembolism (VTE) after 6 months of treatment at full, therapeutic doses. The real-world effectiveness as well as utilization of this approach in clinical practice compared to continuing at therapeutic doses is unclear. Aims: Examine the effectiveness, safety and utilization of reduced-dose DOACs in cancer patients and overweight patients. Methods: Consecutive patients with VTE were identified using the Mayo Clinic VTE Registry from March 1st, 2013 to December 31st, 2021. Patients were followed prospectively for outcomes of VTE recurrence, death, major bleeding and clinically relevant non major bleeding (CRNMB) either in person or by survey. Patients with recurrent VTE or bleeding during the first 3 months were excluded from further analysis. After completion of anticoagulation treatment for 3 months with either rivaroxaban or apixaban, patients continuing anticoagulation were evaluated in a nested case-control study. Results: A total of 404 patients (283 (70%) on apixaban and 121 (30%) on rivaroxaban) were identified in the low dose DOAC and 3060 in the full dose anticoagulation group. Overall, the mean age was 60.7 years (SD 14.4), mean weight was 89.9 kg (SD 23.8) and 55.1% were males. Within the full cohort, patients with active cancer were less likely to be prescribed low dose DOACs (HR 0.56; 95%CI 0.42-0.73; p&lt;0.001). However, low dose DOAC prescription was more likely in patients with pulmonary embolism alone (HR 1.48; 95%CI 1.21-1.80; p&lt;0.001) or in combination with DVT (HR 1.54; 95%CI 1.25,1.90; p&lt;0.001). Patients transitioned to low dose DOACs had similar age, sex and weight compared to the full dose anticoagulation group, however, fewer patients with weight ≥120 kg had reduced dosing (HR 0.68; 95%CI 0.46,0.99; p=0.046). The mean time to start of a low dose DOAC was 5.1 months overall, 5.4 months in cancer patients, and 3.6-4.9 months in non-cancer patients (Table 1). Non-cancer patients had significantly lower transition numbers in those ≥120kg (HR 0.62; 95%CI 0.40,0.97; p=0.038). In non-cancer patients In comparing weight, ≥120kg to &lt;120kg, in the low dose group, only death was statistically different with higher risk in those ≥120kg (HR 6.44; 95%CI1.27,32.81; p=0.025). Cancer patients transitioned to low dose DOACs around 5 months on average with a median of 3.3 months. Apixaban continued to be the dominant DOAC used (78%) in the low dose group. The cancers with the most patients on low dose DOACs include lymphoma, prostate, melanoma, ovarian, and breast. There was no significant difference in VTE recurrence or death in the cancer group on different DOAC doses. Conclusion: Our results show that anticoagulant prescribers are not always waiting until 6 months to transition to low doses and low doses are being utilized in cancer patients despite lack of cancer-specific studies during the analyzed time period. Although the sample size limits the precision of this analysis, we did not find statistically significant increases in VTE recurrence with low dose DOACs in either cancer patients or with elevated body weight. We did find CRNMB but not MB was observed more frequently in weights above 120kg, possibly from unadjusted confounding in this unadjusted analysis.

Abstract Background Published data on the risk of venous thromboembolism (VTE) with coronavirus disease 2019 (COVID-19) vaccines are scarce and inconclusive, leading to an unmet need for further studies. Methods A retrospective, multicentered study of adult patients vaccinated for one of the three approved COVID-19 vaccines in the United States of America and a pre-COVID-19 cohort of patients vaccinated for influenza at two institutions: Mayo Clinic Enterprise sites and the Medical College of Wisconsin, looking at rate of VTE over 90 days. VTE was identified by applying validated natural language processing algorithms to relevant imaging studies. Kaplan–Meier curves were used to evaluate rate of VTE and Cox proportional hazard models for incident VTE after vaccinations. Sensitivity analyses were performed for age, sex, outpatient versus inpatient status, and type of COVID-19 vaccine. Results A total of 911,381 study subjects received COVID-19 vaccine (mean age: 56.8 [standard deviation, SD: 18.3] years, 55.3% females) and 442,612 received influenza vaccine (mean age: 56.5 [SD: 18.3] years, 58.7% females). VTE occurred within 90 days in 1,498 (0.11%) of the total 1,353,993 vaccinations: 882 (0.10%) in the COVID-19 and 616 (0.14%) in the influenza vaccination cohort. After adjusting for confounding variables, there was no difference in VTE event rate after COVID-19 vaccination compared with influenza vaccination (adjusted hazard ratio: 0.95 [95% confidence interval: 0.85–1.05]). No significant difference in VTE rates was observed between the two cohorts on sensitivity analyses. Conclusion In this large cohort of COVID-19-vaccinated patients, risk of VTE at 90 days was low and no different than a pre-COVID-19 cohort of influenza-vaccinated patients.