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Objective: Interventional approaches to managing intermittent claudication vary widely. According to Society for Vascular Surgery guidelines, any invasive treatment of claudication must offer long-term benefit at low risk of complications. Our aim was to evaluate contemporary claudication intervention patterns and functional outcomes., Methods: The Vascular Study Group of New England database (2003-2018) was queried for peripheral vascular interventions (PVIs), infrainguinal bypasses, and suprainguinal bypasses for claudication. Perioperative and 1-year outcomes were evaluated., Results: There were 7051 PVIs, 2527 infrainguinal bypasses, and 849 suprainguinal bypasses performed for claudication. Treatment levels were iliac (52.2%), femoral-popliteal (54%), and tibial (5.7%). Isolated tibial interventions were completed in 1.7% of patients. Infrainguinal bypasses were most often to the popliteal artery (81.2%); however, in 18.8% of cases, bypasses were to tibial targets. Suprainguinal bypasses originated primarily from the abdominal aorta (88.6%) but also from the axillary artery (10.6%) and thoracic aorta (0.8%). Common perioperative complications were access site hematoma in 4.9% of PVIs and cardiac complications in 3.7% of infrainguinal bypasses and 11.3% of suprainguinal bypasses. Overall, 30-day mortality was 0.4% to 2%. After 1 year, of patients initially ambulating without assistance, 2.4% to 3.6% required assistance and 0.3% to 1.3% were nonambulatory. Ipsilateral reintervention/amputation-free survival, major amputation-free survival, and survival at 1 year were 81.4% to 90.6%, 92.9% to 94.1%, and 95.3% to 97%, respectively., Conclusions: Multisegment PVI was the most commonly performed intervention for claudication; however, a subset of patients received treatments supported by limited evidence, including isolated tibial PVI and bypasses with axillary inflow and tibial outflow. Interventions had low perioperative morbidity and mortality, yet patients were still at risk for worse functional status and limb loss at 1 year, emphasizing the importance of careful patient selection, medical optimization, and informed consent., (Copyright © 2019 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Background: The goal of this study was to construct and externally validate a risk prediction model for patients who undergo carotid endarterectomy (CEA)., Methods: The Vascular Study Group of New England (VSGNE) and Vascular Quality Initiative (VQI) databases were queried for patients who underwent CEA. Data on perioperative variables, comorbidities, and medications were entered into a logistic regression model as predictors of the composite adverse outcomes in the VSGNE sample. Adverse outcomes were defined if the patient experienced any of the following events: postoperative stroke, myocardial infarction, mortality, or discharge to rehabilitation facility. Backward elimination (α level of .2) was then used to select a more parsimonious model. Calibration was performed to measure how closely predicted outcomes agreed with observed outcomes. To assess calibration we used Hosmer-Lemeshow test, the predictive value of the model was assessed via the C-statistic. The external validation was then performed using the VQI sample after excluding those in the VSGNE sample (VQI-VSGNE) following a similar method., Results: The constructed model showed a substantial predictive capability of adverse outcomes (C = 0.711) with goodness of fit (Hosmer-Lemeshow lack of fit test, P = .494). A significantly higher rate of adverse outcomes was noted for the VQI sample (5.2%; n = 12,075) compared with the VSGNE sample (4.49%; n = 8661; P = .017). The discriminating ability of the VSGNE model remained substantial in the external data (C = 0.702)., Conclusions: The internally validated VSGNE CEA risk model, which robustly predicted adverse outcome after CEA, was externally validated by testing it against the remainder of VQI patients who underwent CEA by a diverse array of physicians. This tool provides a simple and reliable method to risk-stratify CEA patients using only their preoperative conditions. A risk score based on this model can reliably stratify patients according to their risk of adverse outcomes after CEA., (Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Background: A certain number of deaths may result from elective abdominal aortic aneurysm (AAA) repair due to inherent risks of operation; however, no agreement exists about which predictive model for in-hospital mortality is most accurate in predicting these events. This study developed a risk prediction model using Vascular Study Group of New England (VSGNE) data and compared it with established models., Methods: VSGNE data (2003-2013) were queried for patients undergoing elective AAA repair by open or endovascular techniques. Clinical variables and known predictors of mortality were included in a full prediction model. Backward elimination with α = .2 was used to construct a parsimonious model. This VSGNE model was compared with established models-Medicare, Glasgow Aneurysm Score (GAS), and Vascular Governance North West (VGNW)-based on the scope of VSGNE data collection. Model fit was compared with the Vuong test. Model discrimination was compared in equally sized risk-group VSGNE terciles., Results: The overall mortality rate for 4431 elective AAA patients was 1.4%. The discriminating ability of the VSGNE model was high (C statistic = 0.822) and corrected slightly to 0.779 after internal validation. Vuong tests yielded significant overall fit difference favoring the VSGNE model over the Medicare (C statistic = 0.769), VGNW (C statistic = 0.767), and GAS (C statistic = 0.685) models. The VGNW and Medicare models performed better than GAS in predicting mortality among risk-group terciles., Conclusions: The VSGNE risk prediction model is best at forecasting mortality among this patient population. The Medicare and VGNW models showed good discrimination., (Copyright © 2015 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)