Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations.

Prior studies have shown that computational fluid dynamics (CFD) simulations help assess patient-specific hemodynamics in abdominal aortic aneurysms (AAAs); patient-specific hemodynamic stressors are frequently used to predict an AAA's growth. Previous studies have utilized both laminar and turbulent simulation models to simulate hemodynamics. However, the impact of different CFD simulation models on the predictive modeling of AAA growth remains unknown and is thus the knowledge gap that motivates this study. Specifically, CFD simulations were performed for 70 AAA models derived from 70 patients' computed tomography angiography (CTA) data with known growth status (i.e., fast-growing [> 5 mm/yr] or slowly growing [< 5 mm/yr]). We used laminar and large eddy simulation (LES) models to obtain hemodynamic parameters to predict AAAs' growth status. Predicting the growth status of AAAs was based on morphological, hemodynamic, and patient health parameters in conjunction with three classical machine learning (ML) classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and generalized linear model (GLM). Our preliminary results estimated aneurysmal flow stability and wall shear stress (WSS) were comparable in both laminar and LES flow simulations. Moreover, computed WSS and velocity-related hemodynamic variables obtained from the laminar and LES simulations showed comparable abilities in differentiating the growth status of AAAs. More importantly, the predictive modeling performance of the three ML classifiers mentioned above was similar, with less than a 2% difference observed (p-value > 0.05). In closing, our findings suggest that two different flow simulations investigated did not significantly affect outcomes of computational hemodynamics and predictive modeling of AAAs' growth status, given the data investigated.

Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study.

BACKGROUND: Management of abdominal aortic aneurysms (AAAs) requires serial imaging surveillance to evaluate the aneurysm dimension. Natural language processing (NLP) has been previously developed to retrospectively identify patients with AAA from electronic health records (EHRs). However, there are no reported studies that use NLP to identify patients with AAA in near real-time from radiology reports. OBJECTIVE: This study aims to develop and validate a rule-based NLP algorithm for near real-time automatic extraction of AAA diagnosis from radiology reports for case identification. METHODS: The AAA-NLP algorithm was developed and deployed to an EHR big data infrastructure for near real-time processing of radiology reports from May 1, 2019, to September 2020. NLP extracted named entities for AAA case identification and classified subjects as cases and controls. The reference standard to assess algorithm performance was a manual review of processed radiology reports by trained physicians following standardized criteria. Reviewers were blinded to the diagnosis of each subject. The AAA-NLP algorithm was refined in 3 successive iterations. For each iteration, the AAA-NLP algorithm was modified based on performance compared to the reference standard. RESULTS: A total of 360 reports were reviewed, of which 120 radiology reports were randomly selected for each iteration. At each iteration, the AAA-NLP algorithm performance improved. The algorithm identified AAA cases in near real-time with high positive predictive value (0.98), sensitivity (0.95), specificity (0.98), F1 score (0.97), and accuracy (0.97). CONCLUSIONS: Implementation of NLP for accurate identification of AAA cases from radiology reports with high performance in near real time is feasible. This NLP technique will support automated input for patient care and clinical decision support tools for the management of patients with AAA. .

Survival Implications of Thrombus Recurrence or Bleeding in Cancer Patients Receiving Anticoagulation for Venous Thromboembolism Treatment.

BACKGROUND: Study aims were to analyze prospectively collected data from patients with cancer-associated venous thromboembolism (VTE) to determine the impact of VTE recurrence and anticoagulant-related bleeding on all-cause mortality. PATIENTS/METHODS: Consecutive cancer patients with acute VTE treated with anticoagulants (March 1, 2013-November 30, 2021) were included in this analysis. Anticoagulant therapy-associated VTE recurrences, major bleeding, and clinically relevant nonmajor bleeding (CRNMB) were assessed for their impact on all-cause mortality outcomes. RESULTS: This study included 1,812 cancer patients with VTE. Of these, there were 97 (5.4%) with recurrent VTE, 98 (5.4%) with major, and 104 (5.7%) with CRNMB while receiving anticoagulants. Recurrent VTE (hazard ratio [HR]: 1.52; 95% confidence interval [CI]: 1.16-2.00; p = 0.0028), major bleeding (HR: 1.82; 95% CI: 1.41-2.31; p = 0.006), and CRNMB (HR; 1.38; 95% CI: 1.05-1.81; p = 0.018) each adversely influenced mortality outcomes. Deep vein thrombosis as the incident thrombotic event type was associated with VTE recurrence (HR: 1.78; 95% CI: 1.08-2.89; p = 0.02). Neither cancer type nor stage, chemotherapy, or Ottawa risk category influenced VTE recurrence. Higher body weights (HR: 1.01; 95% CI: 1.00-1.01; p = 0.005) were associated with increased major bleeding, while high Ottawa scores (HR: 0.66; 95% CI: 0.46-0.96; p = 0.03) and apixaban treatment (HR: 0.62; 95% CI: 0.45-0.84; p = 0.002) were associated with fewer major bleeding outcomes. CONCLUSION: Among cancer patients receiving anticoagulant therapy for VTE, adverse outcomes such as VTE recurrence, major bleeding, or CRNMB increase mortality risk by 40 to 80%. Identifying variables predicting these outcomes may help risk-stratify patients with poor prognosis.

Inpatient Management of Pulmonary Embolism: Clinical Characteristics and Mortality in a High-Volume Tertiary Care Center.

The optimal management strategy for submassive or intermediate risk pulmonary embolism (IRPE)-anticoagulation alone versus anticoagulation plus advanced therapies-remains in equipoise leading many institutions to create multidisciplinary PE response teams (PERTs) to guide therapy. Cause-specific mortality of IRPE has not been thoroughly examined, which is a meaningful outcome when examining the effect of specific interventions for PE. In this retrospective study, we reviewed all adult inpatient admissions between 8/1/2018 and 8/1/2019 with an encounter diagnosis of PE to study all cause and PE cause specific mortality as the primary outcomes and bleeding complications from therapies as a secondary outcome. There were 429 total inpatient admissions, of which 59.7% were IRPE. The IRPE 30-day all-cause mortality was 8.7% and PE cause-specific mortality was 0.79%. Treatment consisted of anticoagulation alone in 93.4% of cases. Advanced therapies-systemic thrombolysis, catheter directed thrombolysis, or mechanical thrombectomy, were performed in only six IRPE cases (2.3%). Decompensation of IRPE cases requiring higher level of care and/or rescue advanced therapy occurred in only five cases (2%). In-hospital major bleeding and clinically relevant non-major bleeding were more common in those receiving systemic thrombolysis (61.5%) compared to anticoagulation combined with other advanced therapies (11.7%). Despite the high overall acuity of PE cases at our institution, in-hospital all-cause mortality was low and cause-specific mortality for IRPE was rare. These data suggest the need to target other clinically meaningful outcomes when examining advanced therapies for IRPE.