Automatic segmentation of intraluminal thrombosis of abdominal aortic aneurysms from CT angiography using a mixed-scale-driven multiview perception network (M2Net) model.

Intraluminal thrombosis (ILT) plays a critical role in the progression of abdominal aortic aneurysms (AAA). Understanding the role of ILT can improve the evaluation and management of AAAs. However, compared with highly developed automatic vessel lumen segmentation methods, ILT segmentation is challenging. Angiographic contrast agents can enhance the vessel lumen but cannot improve boundary delineation of the ILT regions; the lack of intrinsic contrast in the ILT structure significantly limits the accurate segmentation of ILT. Additionally, ILT is not evenly distributed within AAAs; its sparsity and scattered distributions in the imaging data pose challenges to the learning process of neural networks. Thus, we propose a multiview fusion approach, allowing us to obtain high-quality ILT delineation from computed tomography angiography (CTA) data. Our multiview fusion network is named Mixed-scale-driven Multiview Perception Network (M2Net), and it consists of two major steps. Following image preprocessing, the 2D mixed-scale ZoomNet segments ILT from each orthogonal view (i.e., Axial, Sagittal, and Coronal views) to enhance the prior information. Then, the proposed context-aware volume integration network (CVIN) effectively fuses the multiview results. Using contrast-enhanced computed tomography angiography (CTA) data from human subjects with AAAs, we evaluated the proposed M2Net. A quantitative analysis shows that the proposed deep-learning M2Net model achieved superior performance (e.g., DICE scores of 0.88 with a sensitivity of 0.92, respectively) compared with other state-of-the-art deep-learning models. In closing, the proposed M2Net model can provide high-quality delineation of ILT in an automated fashion and has the potential to be translated into the clinical workflow.

Ten-Year Trend of Oral Anticoagulation Use in Postoperative and Nonpostoperative Atrial Fibrillation in Routine Clinical Practice.

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.

Pulmonary vein thrombosis: Clinical presentation and outcomes.

BACKGROUND: Pulmonary vein thrombosis (PVT) is a rare thromboembolic disease with potential high-risk complications related to arterial embolization, but little is known regarding risk factors and outcomes. OBJECTIVE: To describe the etiology, management, and clinical course of PVT. METHODS: Institutional health records were queried (1/1/2001-12/30/2023) to identify patients ≥18 years of age diagnosed with PVT. Thrombosis, bleeding, respiratory failure, and all-cause mortality were analyzed. Suspected tumor thrombus cases were excluded. RESULTS: 72 patients with PVT were identified (median age 62 years, 50 % female), and PVT was overall rare at 3.1 diagnosed cases per year at our institution. PVT primarily affected a single vein (89 %), most commonly the left upper PV (40 %). Of these, 37 % occurred while on therapeutic anticoagulation. The most common risk factors included cancer (55 %) and related surgical lobectomy (21 %). Extrinsic vein compression (17 %) and recent surgery (19 %) were also common; 19 % were deemed idiopathic. Most patients (76 %) were treated with anticoagulation and frequently indefinite duration (80 %). During a median follow-up of 11.7 months (IQR 39.5 months), serial imaging (available for 68 %) revealed PVT resolution in 64 %. Four-year Kaplan-Meier probability of outcome included: left atrial thrombus (21 %), need for mechanical ventilation (14 %), pneumonia (9 %), and ischemic stroke (9 %). The mortality rate was 46 % with median survival 14 months after PVT diagnosis. CONCLUSION: PVT is often associated with active malignancy, lobectomy, recent surgery, and extrinsic vein compression; 1 in 5 cases were idiopathic. Notable complications include left atrial thrombus with arterial embolism including stroke. With anticoagulation, most thrombi resolve over time. Mortality rates are high, reflecting the high the prevalence of cancer.

Calf rEF: Impact of Calf Muscle Pump Dysfunction With Reduced Ejection Fraction on All-Cause Mortality.

OBJECTIVE: To evaluate mortality outcomes by varying degrees of reduced calf muscle pump (CMP) ejection fraction (EF). PATIENTS AND METHODS: Consecutive adult patients who underwent venous air plethysmography testing at the Mayo Clinic Gonda Vascular Laboratory (January 1, 2012, through December 31, 2022) were divided into groups based on CMP EF for the assessment of all-cause mortality. Other venous physiology included measures of valvular incompetence and clinical venous disease (CEAP [clinical presentation, etiology, anatomy, and pathophysiology] score). Mortality rates were calculated using the Kaplan-Meier method. RESULTS: During the study, 5913 patients met the inclusion criteria. During 2.84-year median follow-up, there were 431 deaths. Mortality rates increased with decreasing CMP EF. Compared with EF of 50% or higher, the hazard ratios (95% CIs) for mortality were as follows: EF of 40% to 49%, 1.4 (1.0 to 2.0); EF of 30% to 39%, 1.6 (1.2 to 2.4); EF of 20% to 29%, 1.7 (1.2 to 2.4); EF of 10% to 19%, 2.4 (1.7 to 3.3) (log-rank P≤.001). Although measures of venous valvular incompetence did not independently predict outcomes, venous disease severity assessed by CEAP score was predictive. After adjusting for several clinical covariates, both CMP EF and clinical venous disease severity assessed by CEAP score remained independent predictors of mortality. CONCLUSION: Mortality rates are higher in patients with reduced CMP EF and seem to increase with each 10% decrement in CMP EF. The mortality mechanism does not seem to be impacted by venous valvular incompetence and may represent variables intrinsic to muscular physiology.

CoaguChek and Coag-Sense PT2 Meter Point of Care INR Device Validation.

OBJECTIVE: To standardize international normalized ratio (INR) measurements and improve data integrity by enabling electronic result transmission for warfarin monitoring, two point-of-care (POC) devices were evaluated against an internal plasma INR reference method. METHODS: A multicenter study was pursued (January 24, 2022, through October 19, 2022) to compare concordance of two commercially available POC devices, Coag-Sense PT2 Meter (Coag-Sense) and CoaguChek XS Pro and Plus devices (CoaguChek), against an internal plasma INR method among patients treated with warfarin. Bias and linear regression analysis were assessed for these devices including dosing decision accuracy compared with plasma INR reference. RESULTS: Two hundred ninety-nine patients treated with warfarin across three Mayo Clinic sites agreed to participate. Atrial fibrillation (n=191, 63.9%), venous thromboembolism (n=65; 21.7%), and heart valve prosthesis (n=46; 15.4%) were common anticoagulant indications with a 2.5 INR target for 280 (93.6%) of patients. For the CoaguChek devices, 243 (81.3%) of values fell within 0.2 INR units with plasma INR referent and 285 (95.3%) within 0.4 units (R2=0.93). For the Coag-Sense device, 102 (34.1%) of values fell within 0.2 INR units and 180 (60.2%) within 0.4 INR units of plasma INR values, (R2=0.83; P<.0001). Using the plasma INR as the gold standard, appropriate dosing recommendations would have occurred for 292 (97.7%) of the CoaguChek and 244 (81.6%) of the Coag-Sense results. CONCLUSION: Compared with a plasma referent, INR values obtained from the CoaguChek devices exhibited less systematic bias compared with Coag-Sense measures. This translates to a greater percentage of concordant management decisions between POC and laboratory INR methods.

Extending venous thromboembolism secondary prevention with apixaban in cancer patients. The EVE trial.

BACKGROUND: Cancer-associated venous thromboembolism (VTE) management guideline recommendations include continued therapeutic anticoagulation while active cancer persists. The Federal Drug Administration label for apixaban for secondary VTE prevention includes a dose reduction to 2.5 mg twice daily after 6 months of treatment. OBJECTIVES: The study's purpose was to determine whether this dose reduction is advisable for cancer-associated VTE. METHODS: A randomized, double-blind trial compared apixaban 2.5 mg with 5 mg twice daily for 12 months among cancer patients with VTE who had completed 6 to 12 months of anticoagulation therapy. The primary outcome was combined major bleeding plus clinically relevant nonmajor bleeding. RESULTS: Of 370 patients recruited, 360 were included in the intention-to-treat analyses. Major plus clinically relevant nonmajor bleeding occurred in 16 of 179 patients (8.9%) in the apixaban 2.5 mg group compared with 22 of 181 patients (12.2%) in the 5 mg group (hazard ratio [HR], 0.72; 95% CI, 0.38-1.37; P = .39). Major bleeding occurred in 2.8% of the apixaban 2.5 mg group and in 2.2% of the 5 mg group (HR, 1.26; 95% CI, 0.34-4.66; P = .73). Recurrent VTE or arterial thrombosis occurred in 9 of 179 patients (5.0%) in the apixaban 2.5 mg group and 9 of 181 patients (5.0%) in the 5 mg group (HR, 1.0; 95% CI, 0.40-2.53; P = 1.00). All-cause mortality rates were similar between groups, 13% vs 12% (HR, 1.14; 95% CI, 0.63-2.04; P = .67). CONCLUSION: For secondary prevention of cancer-associated VTE, apixaban 2.5 mg compared with 5 mg twice daily did not lower combined bleeding events (EVE trial NCT03080883).

Reduced calf muscle pump function is not explained by handgrip strength measurements.

OBJECTIVE: Reduced calf muscle pump function (CPF) is an independent risk factor for venous thromboembolism and mortality. We aimed to evaluate the relationship between handgrip strength (HGS) and CPF. METHODS: Patients referred to the Gonda Vascular Laboratory for noninvasive venous studies were identified and consented. Patients underwent standard venous air plethysmography protocol. CPF (ejection fraction) was measured in each lower extremity of ambulatory patients by comparing refill volume after ankle flexes and passive refill volumes. The cutoff for reduced CPF (rCPF) was defined as an ejection fraction of <45%. Maximum HGS bilaterally was obtained (three trials per hand) using a dynamometer. HGS and CPF were compared (right hand to calf, left hand to calf) and the correlation between the measures was evaluated. RESULTS: 115 patients (mean age, 59.2 ± 17.4 years; 67 females, mean body mass index, 30.83 ± 6.46) were consented and assessed for HGS and CPF. rCPF was observed in 53 right legs (46%) and 67 left legs (58%). CPF was reduced bilaterally in 45 (39%) and unilaterally in 30 (26%) patients. HGS was reduced bilaterally in 74 (64.3%), unilaterally in 23 (20%), and normal in 18 (15.7%) patients. Comparing each hand/calf pair, no significant correlations were seen between HGS and CPF. The Spearman's rank correlation coefficients test yielded values of 0.16 for the right side and 0.10 for the left side. CONCLUSIONS: There is no significant correlation between HGS and CPF, demonstrating that HGS measurements are not an acceptable surrogate for rCPF, indicating different pathophysiological mechanisms for each process.

Artificial intelligence of arterial Doppler waveforms to predict major adverse outcomes among patients with diabetes mellitus.

OBJECTIVE: Patients with diabetes mellitus (DM) are at increased risk for peripheral artery disease (PAD) and its complications. Arterial calcification and non-compressibility may limit test interpretation in this population. Developing tools capable of identifying PAD and predicting major adverse cardiac event (MACE) and limb event (MALE) outcomes among patients with DM would be clinically useful. Deep neural network analysis of resting Doppler arterial waveforms was used to detect PAD among patients with DM and to identify those at greatest risk for major adverse outcome events. METHODS: Consecutive patients with DM undergoing lower limb arterial testing (April 1, 2015-December 30, 2020) were randomly allocated to training, validation, and testing subsets (60%, 20%, and 20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict all-cause mortality, MACE, and MALE at 5 years using quartiles based on the distribution of the prediction score. RESULTS: Among 11,384 total patients, 4211 patients with DM met study criteria (mean age, 68.6 ± 11.9 years; 32.0% female). After allocating the training and validation subsets, the final test subset included 856 patients. During follow-up, there were 262 deaths, 319 MACE, and 99 MALE. Patients in the upper quartile of prediction based on deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 3.58; 95% confidence interval [CI], 2.31-5.56), MACE (HR, 2.06; 95% CI, 1.49-2.91), and MALE (HR, 13.50; 95% CI, 5.83-31.27). CONCLUSIONS: An artificial intelligence enabled analysis of a resting Doppler arterial waveform permits identification of major adverse outcomes including all-cause mortality, MACE, and MALE among patients with DM.

Risk Stratification and Management of Intermediate-Risk Acute Pulmonary Embolism.

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.

Artificial Intelligence of Arterial Doppler Waveforms to Predict Major Adverse Outcomes Among Patients Evaluated for Peripheral Artery Disease.

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.

Comparison of Venous Thromboembolism Outcomes after COVID-19 and Influenza Vaccinations.

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.

Exploring a frequency-domain attention-guided cascade U-Net: Towards spatially tunable segmentation of vasculature.

Developing fully automatic and highly accurate medical image segmentation methods is critically important for vascular disease diagnosis and treatment planning. Although advances in convolutional neural networks (CNNs) have spawned an array of automatic segmentation models converging to saturated high performance, none have explored whether CNNs can achieve (spatially) tunable segmentation. As a result, we propose multiple attention modules from a frequency-domain perspective to construct a unified CNN architecture for segmenting vasculature with desired (spatial) scales. The proposed CNN architecture is named frequency-domain attention-guided cascaded U-Net (FACU-Net). Specifically, FACU-Net contains two innovative components: (1) a frequency-domain-based channel attention module that adaptively tunes channel-wise feature responses and (2) a frequency-domain-based spatial attention module that enables the deep network to concentrate on foreground regions of interest (ROIs) effectively. Furthermore, we devised a novel frequency-domain-based content attention module to enhance or weaken the high (spatial) frequency information, allowing us to strengthen or eliminate vessels of interest. Extensive experiments using clinical data from patients with intracranial aneurysms (IA) and abdominal aortic aneurysms (AAA) demonstrated that the proposed FACU-Net met its design goal. In addition, we further investigated the association between varying (spatial) frequency components and the desirable vessel size/scale attributes. In summary, our preliminary findings are encouraging, and further developments may lead to deployable image segmentation models that are spatially tunable for clinical applications.

Real world utilization of Andexanet alfa in the management of oral factor Xa inhibitor-associated gastrointestinal bleeding.

BACKGROUND: Andexanet alfa (AA) is approved for reversal of factor Xa inhibitor (FXaI) bleeds; however, there are limited reports of its use for gastrointestinal bleeding (GIB) in real-world populations. The objective of this study was to report real-world utilization and evaluation of the effectiveness of AA for FXaI-associated GIB. METHODS: This retrospective cohort study including consecutive patients receiving AA for FXaI-associated GIB (7/2018-2/2021). Demographics, blood product administration, hemostatic efficacy, rebleeding, thrombosis, and mortality rates were collected. Hemostatic efficacy (HE), based on corrected hemoglobin at 12 h compared to baseline, was categorized as excellent (<10% decrease), good (≤ 20% decrease), or poor (>20% decrease, > 2 units of additional coagulation intervention or death prior to repeat hemoglobin). Comparative transfusion requirements between efficacy groups was assessed by Wilcoxon-Rank test. RESULTS: Twenty-two patients were included (64% male, median (IQR) age 76 years (67, 80). Most patients (59%, n = 13) were on apixaban, and the primary anticoagulation indication was atrial fibrillation (64%, n = 14). Median initial hemoglobin was 7.5 g/dL (IQR 6.4, 8.8) and 50% (n = 11) were upper GIB. Hemostatic efficacy was excellent in 46% (n = 10), good in 23% (n = 5), and poor in 32% (n = 7). There was no statistically significant difference in red blood cells (RBCs) received between those with excellent/good hemostasis (median 2, IQR 1 to 2) and those with poor hemostasis (median 4, IQR 1.5 to 4.5). Two patients (9%) had arterial thrombotic events within 30 days of reversal. CONCLUSION: In this multicenter, single arm, real-world observational analysis of patients with factor Xa inhibitor associated GIB most patients achieved good hemostasis following administration of AA. There was a 9% 30-day thrombotic event rate. The lack of a control group limits the strength of the conclusions that can be drawn from this study.

Characterization of small abdominal aortic aneurysms' growth status using spatial pattern analysis of aneurismal hemodynamics.

Aneurysm hemodynamics is known for its crucial role in the natural history of abdominal aortic aneurysms (AAA). However, there is a lack of well-developed quantitative assessments for disturbed aneurysmal flow. Therefore, we aimed to develop innovative metrics for quantifying disturbed aneurysm hemodynamics and evaluate their effectiveness in predicting the growth status of AAAs, specifically distinguishing between fast-growing and slowly-growing aneurysms. The growth status of aneurysms was classified as fast (≥ 5 mm/year) or slow (< 5 mm/year) based on serial imaging over time. We conducted computational fluid dynamics (CFD) simulations on 70 patients with computed tomography (CT) angiography findings. By converting hemodynamics data (wall shear stress and velocity) located on unstructured meshes into image-like data, we enabled spatial pattern analysis using Radiomics methods, referred to as "Hemodynamics-informatics" (i.e., using informatics techniques to analyze hemodynamic data). Our best model achieved an AUROC of 0.93 and an accuracy of 87.83%, correctly identifying 82.00% of fast-growing and 90.75% of slowly-growing AAAs. Compared with six classification methods, the models incorporating hemodynamics-informatics exhibited an average improvement of 8.40% in AUROC and 7.95% in total accuracy. These preliminary results indicate that hemodynamics-informatics correlates with AAAs' growth status and aids in assessing their progression.

Comparative Effectiveness of Anticoagulants in Patients With Cancer-Associated Thrombosis.

Importance: Patterns of clinical utilization and comparative effectiveness of anticoagulants for cancer-associated thrombosis (CAT) remain largely unexplored. Objectives: To assess patterns of and factors associated with anticoagulant use and to evaluate the comparative effectiveness of contemporary anticoagulants in patients with active cancer in a clinical setting. Design, Setting, and Participants: This retrospective cohort study obtained deidentified OptumLabs electronic health record claims data from January 1, 2012, to September 30, 2019. Adult patients (≥18 years of age) with a primary cancer diagnosis (except skin cancer) during at least 1 inpatient or 2 outpatient visits within 6 months before the venous thromboembolism (VTE) date were included. Data were analyzed from April 2020 to September 2021. Exposures: The patients were grouped according to the anticoagulant prescribed: (1) direct oral anticoagulants (DOACs), (2) low-molecular-weight heparin (LMWH), and (3) warfarin. Main Outcomes and Measures: Odds ratios (ORs) were used to present the association between factors of interest and utilization of anticoagulants. Main efficacy outcomes included risk of VTE recurrence and all-cause mortality. Main safety outcomes included the risk of hospitalization due to major bleeding. Relative treatment effect estimates were expressed as hazard ratios (HRs) with 95% CIs. Results: This study included 5100 patients (mean [SD] age, 66.3 [12.3] years; 2670 [52.4%] women; 799 [15.7%] Black, 389 [7.6%] Hispanic, and 3559 [69.8%] White individuals). Overall, 2512 (49.3%), 1488 (29.2%), and 1460 (28.6%) filled prescriptions for DOACs, LMWH, and warfarin, respectively. The median (IQR) treatment duration was 3.2 (1.0-6.5) months for DOACs, 3.1 (1.0-6.8) months for warfarin, and 1.8 (0.9-3.8) months for LWMH. Patients with lung (OR, 2.07; 95% CI, 1.12-3.65), urological (OR, 1.94; 95% CI,1.08-3.49), gynecological (OR, 4.25; 95% CI, 2.31-7.82), and colorectal (OR, 2.26; 95% CI, 1.20-4.32) cancer were associated with increased prescriptions for LMWH compared with DOACs. LMWH (HR, 1.47; 95% CI, 1.14-1.90) and warfarin (HR, 1.46; 95% CI, 1.13-1.87) were associated with an increased risk of VTE recurrences compared with DOACs. LMWH was associated with an increased risk of major bleeding (HR, 2.27; 95% CI, 1.62-3.20) and higher all-cause mortality (HR, 1.61; 95% CI, 1.15-2.25) compared with DOACs. Conclusions and Relevance: In this comparative effectiveness study of claims-based data, patients with CAT received anticoagulation for a remarkably short duration in clinical settings. DOACs was associated with a lower risk of VTE recurrence, major bleeding, and mortality. Warfarin may still be considered for patients with contraindications to DOACs and those with poor persistence on LMWH.

Radiomic-based Textural Analysis of Intraluminal Thrombus in Aortic Abdominal Aneurysms: A Demonstration of Automated Workflow.

Our main objective is to investigate how the structural information of intraluminal thrombus (ILT) can be used to predict abdominal aortic aneurysms (AAA) growth status through an automated workflow. Fifty-four human subjects with ILT in their AAAs were identified from our database; those AAAs were categorized as slowly- (< 5 mm/year) or fast-growing (≥ 5 mm/year) AAAs. In-house deep-learning image segmentation models were used to generate 3D geometrical AAA models, followed by automated analysis. All features were fed into a support vector machine classifier to predict AAA's growth status.The most accurate prediction model was achieved through four geometrical parameters measuring the extent of ILT, two parameters quantifying the constitution of ILT, antihypertensive medication, and the presence of co-existing coronary artery disease. The predictive model achieved an AUROC of 0.89 and a total accuracy of 83%. When ILT was not considered, our prediction's AUROC decreased to 0.75 (P-value < 0.001).