American College Of Surgeons - Inspiring Quality: Highest Standards, Better Outcomes

Predictive model assesses MI risk from lower extremity bypass

OCTOBER 23, 2017
Clinical Congress Daily Highlights, Monday First Edition

Using a large multicenter dataset, researchers have created a predictive tool to stratify the risk of perioperative and postoperative myocardial infarction (MI) in lower extremity bypass patients.

The study, led by Anam Pal, MD, MBBS, a surgical resident at Northwell Health Staten Island University Hospital, Staten Island, NY, found that the strongest predictors of MI were ventilator dependence, inpatient status, American Society of Anesthesiology (ASA) class, and critical limb ischemia. Other risk factors considered included high-risk physiologic factors, preoperative statins, African American race, diabetes, open repair, dyspnea, and preoperative beta blocker.

The study looked at 13,678 patients — 5,361 open and 8,317 endovascular — extracted from the 2011-2014 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP®) dataset. Backwards stepwise logistic regression was used to determine independent predictors of MI in a randomly selected subset containing 75 percent of the cohort. The researchers then validated the model using the remaining 25 percent of the cohort. The strength of the model is moderate to good, with a validation c statistic of 0.73, the study concluded. A c statistic of 1.0 indicates perfect predictive ability, while a model with a value of 0.5 performs no better than chance.

Additional Information:
The Scientific Forum presentation, Novel Prediction Model for Cardiac Risk in Lower Extremity Revascularization, was held October 23 at the 2017 Clinical Congress of the American College of Surgeons in San Diego, CA.  Program, webcast and audio information is available online at

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