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Refractory heart failure and intermittent claudication secondary to supra-renal coral reef aorta.

ABSTRACT: Coral reef aorta (CRA), a rare disease, is characterized by severe calcification of the juxta-renal and suprarenal aorta that grows into the lumen and leads to severe stenosis. A 70-year-old woman with refractory hypertension and lower limb claudication presented with hypertension and congestive heart failure. Treatment with vasodilators and diuresis led to oliguria and exacerbated kidney function, while her congestion remained. Abdominal computerized tomography showed a bulky, irregular localized supra-renal aortic calcification with stenosis. A peripheral artery ultrasound and angiography showed no occlusive lesions in the distal run-off vessels. Based on her medical history and the unique aspects of the localized calcified lesion, CRA was diagnosed. We suspected that the congestive heart failure, refractory hypertension, and renal failure resulted from the supra-renal aortic stenosis. Because she developed oliguria with diuretics and vasodilators, we performed an open graft replacement with a thoracoabdominal approach. The reddish-brown calcified mass came off easily and was very fragile. The postoperative course was uneventful, and her heart and renal failure were completely resolved. This is the first report showing the fragility of CRA. Considering its fragility, catheter treatment may need to be avoided to prevent distal embolism. <Learning objective: When we encounter a calcified lesion localized at the level of the juxta and supra-renal aorta with extensive growth into the lumen, coral reef aorta (CRA) should be suspected. Considering that CRA lesion can be very fragile in contrast to the usual atherosclerotic lesion, as shown in our case, CRA might possibly increase the risk of distal embolization. Therefore, open graft replacement could be safer than endovascular stent-graft placement.>.

SUBMITTER: Kojima S

PROVIDER: S-EPMC7588505 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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Refractory heart failure and intermittent claudication secondary to supra-renal coral reef aorta.

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