ArticleJournal of vascular surgery2016
The Vascular Quality Initiative Cardiac Risk Index for prediction of myocardial infarction after vascular surgery.
Article in Journal of vascular surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 4 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 4 syntheses or guidelines pooled it, 114 citations in OpenAlex.
- 2022 ACC/AHA guideline for the diagnosis and management of aortic disease: A report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines.The Journal of thoracic and cardiovascular surgery · 2023Guideline
- 2022 ACC/AHA Guideline for the Diagnosis and Management of Aortic Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines.Journal of the American College of Cardiology · 2022Guideline
- Guideline
- External Validation of Risk Prediction Models to Improve Selection of Patients for Carotid Endarterectomy.Stroke · 2022Pooled it
- Risk of complications in vascular surgery: development of a clinical predictive model.Jornal vascular brasileiro · 2026Article
- Predicting 1-year successful clinical use of an arteriovenous access for hemodialysis using machine learning.NPJ digital medicine · 2025Article
- The Operative Role of Artificial Intelligence in Vascular Surgery: A Systematic Review of Literature.Cureus · 2025Review
- Predicting outcomes following endovascular aortoiliac revascularization using machine learning.NPJ digital medicine · 2025Article
- Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.Journal of the American Heart Association · 2025Article
- Using machine learning to predict outcomes following transcarotid artery revascularization.Scientific reports · 2025Article
- Risk factors for perioperative stroke, myocardial infarction, and death in patients undergoing carotid endarterectomy under local anesthesia: a systematic review and meta-analysis.Frontiers in surgery · 2025Review
- Predicting inferior vena cava filter complications using machine learning.Journal of vascular surgery. Venous and lymphatic disorders · 2024Article
- Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.Journal of the American Heart Association · 2024Article
- Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.Journal of the American Heart Association · 2024Article
- Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD.JAMA network open · 2024Article
- Predictive value of geriatric nutritional risk index in cardiac and cerebrovascular events after endovascular aortic aneurysm repair.Frontiers in cardiovascular medicine · 2024Article
- Risk Prediction Models for Peri-Operative Mortality in Patients Undergoing Major Vascular Surgery with Particular Focus on Ruptured Abdominal Aortic Aneurysms: A Scoping Review.Journal of clinical medicine · 2023Article
- Sex-specific differences in the standard of care for infrarenal abdominal aortic aneurysm repair, and risk of major adverse cardiovascular events and death.The British journal of surgery · 2023Article
- Endovascular treatment of infrarenal aortic aneurysm using the ANKURA stent graft - one-center case series.Postepy w kardiologii interwencyjnej = Advances in interventional cardiology · 2023Article
- Migration of High Cardiac Risk Patients from Open to Endovascular Procedures is Evident within the Society for Vascular Surgery Vascular Quality Initiative.Annals of vascular surgery · 2022Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
objectiveThe objective of this study was to develop and to validate the Vascular Quality Initiative (VQI) Cardiac Risk Index (CRI) for prediction of postoperative myocardial infarction (POMI) after vascular surgery.
methodsWe developed risk models for in-hospital POMI after 88,791 nonemergent operations from the VQI registry, including carotid endarterectomy (CEA; n = 45,340), infrainguinal bypass (INFRA; n = 18,054), suprainguinal bypass (SUPRA; n = 2678), endovascular aneurysm repair (EVAR; n = 18,539), and open abdominal aortic aneurysm repair (OAAA repair; n = 4180). Multivariable logistic regression was used to create an all-procedure and four procedure-specific risk calculators based on the derivation cohort from 2012 to 2014 (N = 61,236). Generalizability of the all-procedure model was evaluated by applying it to each procedure subtype. The models were validated using a cohort (N = 27,555) from January 2015 to February 2016. Model discrimination was measured by area under the receiver operating characteristic curve (AUC), and performance was validated by bootstrapping 5000 iterations. The VQI CRI calculator was made available on the Internet and as a free smart phone app available through QxCalculate.
resultsOverall POMI incidence was 1.6%, with variation by procedure type as follows: CEA, 0.8%; EVAR, 1.0%; INFRA, 2.6%; SUPRA, 3.1%; and OAAA repair, 4.3% (P < .001). Predictors of POMI in the all-procedure model included age, operation type, coronary artery disease, congestive heart failure, diabetes, creatinine concentration >1.8 mg/dL, stress test status, and body mass index (AUC, 0.75; 95% confidence interval [CI], 0.73-0.76). The all-procedure model demonstrated only minimally reduced accuracy when it was applied to each procedure, with the following AUCs: CEA, 0.65 (95% CI, 0.59-0.70); INFRA, 0.69 (95% CI, 0.64-0.73); EVAR, 0.72 (95% CI, 0.65-0.80); SUPRA, 0.62 (95% CI, 0.52-0.72); and OAAA, 0.63 (95% CI, 0.56-0.70). Procedure-specific models had unique predictors and showed improved prediction compared with the all-procedure model, with the following AUCs: CEA, 0.69 (95% CI, 0.66-0.72); INFRA, 0.75 (95% CI, 0.73-0.78); EVAR, 0.76 (95% CI, 0.73-0.80); and OAAA, 0.72 (95% CI, 0.69-0.77). Bias-corrected AUC (95% CI) from internal validation for the models was as follows: all procedures, 0.75 (0.73-0.76); CEA, 0.68 (0.65-0.71); INFRA, 0.74 (0.72-0.76); EVAR, 0.73 (0.70-0.78); and OAAA repair, 0.68 (0.65-0.73).
conclusionsThe VQI CRI is a useful and valid clinical decision-making tool to predict POMI after vascular surgery. Procedure-specific models improve accuracy when they include unique risk factors.
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