Dataset Information

Unclassified unilateral persistent sciatic artery in a patient with chronic intermittent claudication.

ABSTRACT: Patients with persistent sciatic artery are at high risk for development of limb ischemia, aneurysm formation, and embolism. In this report, we identify a nonclassified left leg persistent sciatic artery in a patient with chronic limb ischemia. Vascular reconstruction was carried out by common iliac-deep femoral artery bypass to restore adequate arterial flow. Our approach to placement of the distal anastomosis on the deep femoral artery instead of on the popliteal artery, which is used in routine practice, may potentially increase treatment efficacy and decrease surgical complications. At 2-year follow-up, the patient remained asymptomatic and in good health.

SUBMITTER: Duan K

PROVIDER: S-EPMC6704397 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Unclassified unilateral persistent sciatic artery in a patient with chronic intermittent claudication.

Duan Kristina K Huang Jiaqi J Cui Chaoyi C Shi Huihua H Lu Xinwu X Liu Xiaobing X

Journal of vascular surgery cases and innovative techniques 20190814 3

Patients with persistent sciatic artery are at high risk for development of limb ischemia, aneurysm formation, and embolism. In this report, we identify a nonclassified left leg persistent sciatic artery in a patient with chronic limb ischemia. Vascular reconstruction was carried out by common iliac-deep femoral artery bypass to restore adequate arterial flow. Our approach to placement of the distal anastomosis on the deep femoral artery instead of on the popliteal artery, which is used in routi ...[more]

PMID: 31453422

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Project description:BackgroundGuidelines recommend cardiovascular risk reduction and supervised exercise therapy as the first line of treatment in intermittent claudication, but implementation challenges and poor patient compliance lead to significant variation in management and therefore outcomes. The development of a precise risk stratification tool is proposed through a machine-learning algorithm that aims to provide personalized outcome predictions for different management strategies.MethodsFeature selection was performed using the least absolute shrinkage and selection operator method. The model was developed using a bootstrapped sample based on patients with intermittent claudication from a vascular centre to predict chronic limb-threatening ischaemia, two or more revascularization procedures, major adverse cardiovascular events, and major adverse limb events. Algorithm performance was evaluated using the area under the receiver operating characteristic curve. Calibration curves were generated to assess the consistency between predicted and actual outcomes. Decision curve analysis was employed to evaluate the clinical utility. Validation was performed using a similar dataset.ResultsThe bootstrapped sample of 10 000 patients was based on 255 patients. The model was validated using a similar sample of 254 patients. The area under the receiver operating characteristic curves for risk of progression to chronic limb-threatening ischaemia at 2 years (0.892), risk of progression to chronic limb-threatening ischaemia at 5 years (0.866), likelihood of major adverse cardiovascular events within 5 years (0.836), likelihood of major adverse limb events within 5 years (0.891), and likelihood of two or more revascularization procedures within 5 years (0.896) demonstrated excellent discrimination. Calibration curves demonstrated good consistency between predicted and actual outcomes and decision curve analysis confirmed clinical utility. Logistic regression yielded slightly lower area under the receiver operating characteristic curves for these outcomes compared with the least absolute shrinkage and selection operator algorithm (0.728, 0.717, 0.746, 0.756, and 0.733 respectively). External calibration curve and decision curve analysis confirmed the reliability and clinical utility of the model, surpassing traditional logistic regression.ConclusionThe machine-learning algorithm successfully predicts outcomes for patients with intermittent claudication across various initial treatment strategies, offering potential for improved risk stratification and patient outcomes.

Dataset Information

Unclassified unilateral persistent sciatic artery in a patient with chronic intermittent claudication.

Publications

Unclassified unilateral persistent sciatic artery in a patient with chronic intermittent claudication.

Similar Datasets

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