Citations

Abstract VEXAS (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) syndrome, caused by somatic mutations in UBA1, is an autoinflammatory disorder with diverse systemic manifestations. Thrombosis is a prominent clinical feature of VEXAS syndrome. The risk factors and frequency of thrombosis in VEXAS syndrome are not well described, due to the disease’s recent discovery and the paucity of large databases. We evaluated 119 patients with VEXAS syndrome for venous and arterial thrombosis and correlated their presence with clinical outcomes and survival. Thrombosis occurred in 49% of patients, mostly venous thromboembolism (VTE; 41%). Almost two-thirds of VTEs were unprovoked, 41% were recurrent, and 20% occurred despite anticoagulation. The cumulative incidence of VTE was 17% at 1 year from symptom onset and 40% by 5 years. Cardiac and pulmonary inflammatory manifestations were associated with time to VTE. M41L was positively associated specifically with pulmonary embolism by univariate (odds ratio [OR]: 4.58, confidence interval [CI] 1.28-16.21, P = .02) and multivariate (OR: 16.94, CI 1.99-144.3, P = .01) logistic regression. The cumulative incidence of arterial thrombosis was 6% at 1 year and 11% at 5 years. The overall survival of the entire patient cohort at median follow-up time of 4.8 years was 88%, and there was no difference in survival between patients with or without thrombosis (P = .8). Patients with VEXAS syndrome are at high risk of VTE; thromboprophylaxis should administered be in high-risk settings unless strongly contraindicated.

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.

Background: Ca-NBTE primarily affects the mitral valve (MV) and aortic valve (AV), with rare involvement of the right heart valves. Premortem descriptions of this condition are limited to a few case reports. This study presents an initial clinical series focusing on patients with Ca-NBTE specifically affecting their right heart valves. We describe the underlying cancers, types of valve lesions, and associated embolic complications. Patients and Methods: A electronic search of medical records (March 31, 2002, to June 30, 2022) was followed by a manual review to identify adult patients with echocardiographically detected NBTE, active cancer, and no infectious endocarditis or lupus anticoagulant/antiphospholipid antibodies. Results: Out of 115 identified patients with Ca-NBTE (mean age 63.2±9.7 years, 66.1% female), there were 8 cases (7.0%) affecting right heart valves (mean age 64.5±12.1 years, 3 females) with underlying cancer: pancreatic (n=3), lung (n= 2), genitourinary (n=1), gastric (n=1) and neck sarcomatoid carcinoma (n=1). TV was affected in 8 patients and PV in one case; 4 patients had Ca-NBTE limited only to PV, 2 patients had MV and TV, 1 patient had mitral, aortic and TV, and 1 patient with pancreatic cancer had all four valves affected by NBTE. 5 patients had embolic complications to the brain with one patient in that group also experiencing emboli to the kidney and spleen. Out of 4 patients with NBTE limited to TV, two still had brain emboli, one had only pulmonary embolism, and one patient had no embolic complications. Conclusion: Around 7% of Ca-NBTE cases involve the right heart valves, with half of these cases limited to the TV. The cancer profile in these cases resembles that seen in left heart valve involvement.

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: Warfarin is a commonly prescribed anticoagulant for treating atrial fibrillation, mechanical valves, and venous thromboembolism. Warfarin dose management remains challenging due to dosing variability between patients and warfarin’s narrow therapeutic window. Time in therapeutic range (TTR) is critical to warfarin’s safety and efficacy, but TTR typically remains low (40-50%) in community practices. Specialized anticoagulation clinics and protocolized approaches can increase TTR but have great administrative burdens and health care costs. Aim: To develop standardized optimal warfarin dose decision support using a machine learning model based on time series anticoagulation data and patient demographic characteristics Methods: The dataset included 12,497 warfarin patients monitored in the anticoagulation tracker of electronic medical records across the Mayo Clinic Enterprise. The dataset contained time series anticoagulation data (warfarin dose, INR, INR target range) and patient demographic characteristics. We implemented an offline deep reinforcement learning model (DRL) to predict the cumulative warfarin dose for the days until the next INR test based on a patient’s historical anticoagulation data. Prior approaches utilized traditional supervised learning methods such as regression or Long Short Term Memory (LSTM) neural networks to approximate a function mimicking the behavior of the training data. On the other hand, DRL learned an optimal dosing policy through continuous interaction and feedback from the training data. The key advantage of DRL is the model can learn to behave differently (and potentially better) for suboptimal clinical states in the data such as overdosing or underdosing. To evaluate the DRL model we compared the predicted warfarin doses with the physician-prescribed doses Results: DRL model’s prediction accuracy was 96.96%, outperforming our implementation of a baseline LSTM model with a prediction accuracy of 70.58%. Further evaluation of the DRL model indicated that the model correctly adjusted the warfarin dose at time steps when patients had out-of-range INRs. Conclusions: Offline deep reinforcement learning demonstrates potential in supporting warfarin dose management to maximize TTR.

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.

Introduction: Use of validated risk stratification tools for work up of suspected pulmonary embolism (PE) is an endorsed recommendation of the American Society of Hematology and the American College of Emergency Physicians. Due to variations in clinical practice, electronic health record (EHR) systems, and medical provider trainings, the use, documentation, and implementation of these tools vary widely. Current processes do not allow for electronic capture and quality reporting at most institutions. Methods: We sought to design and implement clinical pre-test probability (PTP) tools at three large healthcare institutions for use in emergency departments (ED). At each site, a hematologist partnered with ED physician leadership and local information technology experts to design and implement PTP tools in clinical workflows. Overall, 38 EDs (high volume tertiary EDs and smaller regional EDs) located in the United States along the East Coast and Midwest regions were involved. A major goal of implementation was integration into clinical workflow and automatic documentation. After design and implementation, awareness and education of the new EHR tool was distributed electronically to ED providers. After implementation, use of PTP tools was analyzed between September 12, 2022 and January 11, 2023. PTP use was examined as percent of visits for which patients underwent CT pulmonary angiography (CTPA). Results: Each institution chose the 3 tier Wells' Score for implementation while one site designed an integrated Wells'/PERC or YEARS score calculator. Two sites implemented the tool as an optional flowsheet and one site implemented the tool as mandatory parameters within the orders for PE imaging studies. The mandatory tool could be bypassed with prespecified selections or if a D-dimer existed within 48 hours . Complete details of the site-specific implementation are shown in Table 1. Over the 4-month evaluation timeframe, there was a total of 270,214 ED encounters. Uptake and utilization of the PTP tools are shown in Table 2. Use of PTP was highest at the site with forced PTP documentation which ranged from 49-53% of ordered CTPAs, compared to Site 2 where use was 2-3%, and Site 1 where use ranged from 1-3%. At Site 1, use of PTP increased slightly over the study period with signs that PE yield on imaging was increasing as well (3.4% to 5.9%). At Site 2, PE yield on imaging was overall high (9-10%), and remained similar with similar use of PTP tools over the study period. Use of PTP and PE yield (6-8%) on imaging also remained similar throughout the study at Site 3. Conclusion: Forced use of PTP within the orders for CTPA led to the highest use of PTP (Site 3) but this did not result in meaningfully higher PE yield on CTPA compared to the other sites. PE yield at each site was at or above the 5% yield for PE on CTPA which has been previously reported. Given that PTP uptake was relatively stable over the measurement period at Site 2 and 3, this indicates the process had mostly stabilized and that other strategies are needed to improve uptake of PTP, reduce utilization of CTPA, and further increase yield on CTPA. Increasing PTP use and rising PE yield at Site 1 demonstrated ongoing potential for improvements past this study timeframe.

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.

Introduction: ASH Guidelines for diagnosis of pulmonary embolism (PE) start with risk assessment using a pretest probability tool (PTP) followed by D-dimer testing or CT pulmonary angiography (CTPA) depending on risk. We aimed to develop an electronic clinical quality measure (eCQM) to encourage broader use of a validated PTP scoring tool in emergency departments (EDs). The Centers for Medicare & Medicaid Services require reporting of eCQMs which consist of value sets or lists of standardized codes linked to patient data within the electronic health record. We sought to identify a standardized way to identify CTPA and abnormal D-dimer tests using administrative codes (i.e. CPT, LOINC, SNOMEDCT codes) across three institutions. Methods: Value sets identifying CTPA and D-dimers were reviewed from existing eCQMs. Three institutions representing 38 EDs from across the country submitted data from all ED encounters between September 12, 2022 and January 11, 2023. Imaging types were reviewed from each of the CPT codes and LOINC (Logical Observation Identifiers Names and Codes). We determined the number of ED encounters, pre-test probability tool use, and diagnosis of PE using different codes. We calculated sensitivity, specificity, positive and negative predictive value (PPV and NPV, respectively). Results: Over the 4-month study period, 270,214 encounters were identified across 38 EDs. Each institution identified CTPA with site-specific codes (Table 1). The Full CTPA Value Set identified 55% more encounters than the site-specific codes (Full set 15,054/9679 site-specific codes). LOINC code 88322-3 was only used at one site. CPT code 71275 had the best sensitivity and specificity, but still had false positives (PPV 82%) (Table 2). D-dimer values were identified as LOINC code 48065-7 and 91556-1 at Site 1 and 48067-3 at Site 2. SNOMEDCT codes were not used at any site despite being the way other eCQMs identify elevated D-dimer results. Different D-dimer tests with different normal ranges were used at each site. All sites did not have an electronically extractable positive indicator for D-dimer. Conclusion: Due to persistent false positives using CPT Code 71275 for identification of CTPA, using this code would lead to inclusion of patients for whom PTP was not required or recommended. Additionally, coding for an abnormal D-dimer test result are not standardized across institutions. Therefore, administrative codes cannot be used to develop eCQMs whose aim is to evaluate whether CTPA is ordered appropriately based on the PTP risk level and laboratory testing.