McBane, Robert

Background: Chronic venous insufficiency (CVI) can be evaluated using Duplex ultrasonography (US) and air plethysmography (PG), yet comparative performance remains unclear. Methods: We retrospectively identified patients who underwent venous insufficiency US and PG within a 90-day interval from March 1, 2015 through July 31, 2024. CVI severity was classified by the clinical (C) component of the Clinical, Etiologic, Anatomic, and Pathophysiologic (CEAP) classification system and categorized as mild (C0–2), moderate (C3), or severe (C4–6). PG and insufficiency US findings were compared across categories and machine learning models were trained to predict severe CVI. Results: We analyzed 1478 limbs from 839 patients who were predominantly women (62%) with a mean age of 61 years (± 14 years). Severe CVI was present in 32.3% of limbs. US detected venous incompetence in 67.4% of limbs overall and in 75.3% of severe CVI limbs. Abnormal PG findings occurred in 72.9% of limbs overall and in 88.5% of severe CVI limbs. PG parameters in machine learning models outperformed US parameters in predicting severe CVI (area under the receiver operating characteristic curve [AUROC] 0.82 vs 0.65). The top PG model (multilayer perceptron [MLP]) achieved an AUROC of 0.82, versus the best US model (gradient boosting) with an AUROC of 0.65. Limiting PG data to incompetence and obstruction parameters modestly reduced performance but remained higher than US (logistic regression AUROC 0.72). Conclusion: Our findings indicate that PG assessment in CVI offers superior performance compared to US. Overall, these results validate the quantitative whole-limb hemodynamic approach using PG, which provides a more complete understanding of the pathology behind CVI.

Post-pulmonary embolism syndrome (PPES) encompasses a diverse spectrum of long-term complications that are associated with persistent dyspnea, exercise intolerance, and a decrease in quality of life. The PPES constellation ranges from life-threatening conditions such as chronic thromboembolic pulmonary hypertension (CTEPH) to nonlife-threatening but still debilitating conditions such as chronic thromboembolic pulmonary disease (CTED) or post-PE deconditioning. The safety and efficacy of exercise and cardiopulmonary rehabilitation programs to prevent and manage these symptoms in PPES has not been well established. Herein, we review contributors to PPES and synthesize the evidence for exercise training after PE to treat and prevent PPES.

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.

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.

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.

AbstractPatients with thrombophilia remain concerned about venous thromboembolism (VTE) risk with COVID‐19 vaccinations. The aim of this study was to examine VTE outcomes in patients with inherited or acquired thrombophilia who were vaccinated for COVID‐19. Vaccinated patients ≥18 years between November 1, 2020 and November 1, 2021 were analyzed using electronic medical records across the Mayo Clinic enterprise. The primary outcome was imaging confirmed acute VTE occurring 90 days before and after the date of the first vaccine dose. Thrombophilia patients were identified through laboratory testing results and ICD‐10 codes. A total of 792 010 patients with at least one COVID‐19 vaccination were identified. Six thousand sixty‐seven of these patients were found to have a thrombophilia, among whom there was a total of 39 VTE events after compared to 51 VTE events before vaccination (0.64% vs. 0.84%, p = .20). In patients with Factor V Leiden or prothrombin gene mutation, VTE occurred in 27 patients before and in 29 patients after vaccination (0.61 vs. 0.65%, p = .79). In patients with antiphospholipid syndrome, VTE occurred in six patients before and four patients after vaccination (0.59% vs. 0.39%, p = .40). No difference was observed in the overall VTE rate when comparing the postvaccination 90 days to the prevaccination 90 days, adjusted hazard ratio 0.81 (95% confidence interval: 0.53–1.23). In this subgroup of COVID‐19 vaccinated patients with thrombophilia, there was no increased risk for acute VTE postvaccination compared to the prevaccination timeframe. These results are consistent with prior studies and should offer additional reassurance to patients with inherited or acquired thrombophilia.

AbstractBackgroundClinical picture and outcome of incidental pulmonary embolism (iPE) compared to symptomatic pulmonary embolism (sPE) remain unclear.MethodsDemographics, recurrent venous thromboembolism (VTE), mortality, major bleeding, and clinically relevant nonmajor bleeding (CRNMB) were compared between iPE and sPE patients who were followed prospectively at Mayo Thrombophilia Clinic (March 1, 2013 to August 1, 2020).ResultsOut of 3576 VTE patients, 1417 (39.6%) had PE: 562 (39.7%) iPE and 855 sPE. Patients with cancer were more likely to have iPE (400 iPE vs. 314 sPE) compared to those without cancer (162 iPE vs. 541 sPE). VTE recurrence rate (all per 100 person‐years) was similar in all iPE and sPE patients (3.34 vs. 3.68, p = .50), with cancer (4.16 vs. 4.89, p = .370), and without cancer patients (0.89 vs. 2.80, p = .25). Higher mortality observed in all patients with iPE compared to sPE (46.45 vs. 23.47, p < .001) and with cancer (56.41 vs. 45.77, p = .03) became not significant after adjustment for age, antiplatelet therapy, metastases, and cancer location. Noncancer iPE patients had higher mortality (15.95 vs. 7.18, p = .006) even after adjustment (p = .05). The major bleeding rate was also higher in all patients iPE compared to sPE (7.10 vs. 3.68, p = .03), but not after adjustment (p = .974); higher major bleeding rate in noncancer patients (6.49 vs. 1.25, p = .007) remained significant after adjustment (.02). CRNMB rate was similar to iPE and sPE patients.ConclusioniPE represents a more serious clinical condition compared to sPE as indicated by the higher mortality and major bleeding but these differences reflect underlying comorbidities rather than the seriousness of the embolic event.

Background: Patients with peripheral artery disease (PAD) are at increased risk for major adverse limb and cardiac events including mortality. Developing screening tools capable of accurate PAD identification is a necessary first step for strategies of adverse outcome prevention. This study aimed to determine whether machine analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with PAD. Methods: Consecutive patients (4/8/2015 – 12/31/2020) undergoing rest and postexercise ankle–brachial index (ABI) testing were included. Patients were randomly allocated to training, validation, and testing subsets (70%/15%/15%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict normal (> 0.9) or PAD (⩽ 0.9) using rest and postexercise ABI. A separate dataset of 151 patients who underwent testing during a period after the model had been created and validated (1/1/2021 – 3/31/2021) was used for secondary validation. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate test performance. Results: Among 11,748 total patients, 3432 patients met study criteria: 1941 with PAD (mean age 69 ± 12 years) and 1491 without PAD (64 ± 14 years). The predictive model with highest performance identified PAD with an AUC 0.94 (CI = 0.92–0.96), sensitivity 0.83, specificity 0.88, accuracy 0.85, and positive predictive value (PPV) 0.90. Results were similar for the validation dataset: AUC 0.94 (CI = 0.91–0.98), sensitivity 0.91, specificity 0.85, accuracy 0.89, and PPV 0.89 (postexercise ABI comparison). Conclusion: An artificial intelligence-enabled analysis of a resting Doppler arterial waveform permits identification of PAD at a clinically relevant performance level.