Kyle R Diamond1, Karen Woo2, Dan Neal3, Yuanyuan Zhao4, Roan J Glocker5, Daniel J Bertges6, Jessica P Simons7. 1. Division of Vascular and Endovascular Surgery, University of Massachusetts Medical School, Worcester, Mass. 2. Division of Vascular Surgery, David Geffen School of Medicine at UCLA, Los Angeles, Calif. 3. Society for Vascular Surgery Vascular Quality Initiative, Lebanon, NH. 4. Division of Vascular Surgery, The Dartmouth Institute, Dartmouth College, Lebanon, NH. 5. Division of Vascular Surgery, University of Rochester School of Medicine, Rochester, NY. 6. Division of Vascular Surgery, University of Vermont Medical Center, Burlington, Vt. 7. Division of Vascular and Endovascular Surgery, University of Massachusetts Medical School, Worcester, Mass. Electronic address: jessica.simons@umassmemorial.org.
Abstract
BACKGROUND: The Vascular Quality Initiative (VQI) Cardiac Risk Index (CRI) was developed to estimate the risk of postoperative myocardial infarction (POMI) for noncardiac vascular procedures. Whereas suprainguinal bypass carried the second highest odds of POMI, the performance of the all-procedure model declined when it was applied to the suprainguinal subset. We sought to improve the VQI CRI for application in this high-risk group undergoing open revascularization for aortoiliac occlusive disease. METHODS: The VQI Suprainguinal Bypass Registry was queried for elective procedures performed between January 2010 and March 2017. Logistic regression was used to create a model for estimating the risk of in-hospital POMI with preoperative variables including demographics, comorbidities, and planned inflow source. After adjustment for overfitting, internal validation was performed using both bootstrapping and 10-fold cross-validation methods. RESULTS: Among 8157 procedures, the incidence of POMI was 3.2% (n = 258). After bootstrapping variable selection, age, graft inflow, preoperative stress test, American Society of Anesthesiologists class, indication for procedure, and coronary artery disease were chosen for inclusion as predictors in the final risk model. The final model demonstrated good discrimination (area under the curve = 0.725). On internal validation, the model discriminated well (area under the curve = 0.713), with good calibration (plot intercept = 0.0006 and slope = 1.001). Using this model, POMI risk estimates ranged from 0.6% to 30.4%. CONCLUSIONS: Whereas the incidence of POMI among all suprainguinal bypasses was 3%, model-based estimates ranged 50-fold, from 0.6% to 30.4%. This underscores the heterogeneity of this cohort and the need for patient-specific risk estimation. Although some of the strongest predictors were nonmodifiable (eg, age), the model provided specific estimates according to graft inflow and stress testing. This supraspecific VQI CRI module risk predictor may enhance preoperative counseling by influencing the decision to pursue preoperative stress testing and ultimately the type of revascularization strategy chosen.
BACKGROUND: The Vascular Quality Initiative (VQI) Cardiac Risk Index (CRI) was developed to estimate the risk of postoperative myocardial infarction (POMI) for noncardiac vascular procedures. Whereas suprainguinal bypass carried the second highest odds of POMI, the performance of the all-procedure model declined when it was applied to the suprainguinal subset. We sought to improve the VQI CRI for application in this high-risk group undergoing open revascularization for aortoiliac occlusive disease. METHODS: The VQI Suprainguinal Bypass Registry was queried for elective procedures performed between January 2010 and March 2017. Logistic regression was used to create a model for estimating the risk of in-hospital POMI with preoperative variables including demographics, comorbidities, and planned inflow source. After adjustment for overfitting, internal validation was performed using both bootstrapping and 10-fold cross-validation methods. RESULTS: Among 8157 procedures, the incidence of POMI was 3.2% (n = 258). After bootstrapping variable selection, age, graft inflow, preoperative stress test, American Society of Anesthesiologists class, indication for procedure, and coronary artery disease were chosen for inclusion as predictors in the final risk model. The final model demonstrated good discrimination (area under the curve = 0.725). On internal validation, the model discriminated well (area under the curve = 0.713), with good calibration (plot intercept = 0.0006 and slope = 1.001). Using this model, POMI risk estimates ranged from 0.6% to 30.4%. CONCLUSIONS: Whereas the incidence of POMI among all suprainguinal bypasses was 3%, model-based estimates ranged 50-fold, from 0.6% to 30.4%. This underscores the heterogeneity of this cohort and the need for patient-specific risk estimation. Although some of the strongest predictors were nonmodifiable (eg, age), the model provided specific estimates according to graft inflow and stress testing. This supraspecific VQI CRI module risk predictor may enhance preoperative counseling by influencing the decision to pursue preoperative stress testing and ultimately the type of revascularization strategy chosen.
Authors: Juliet Blakeslee-Carter; Zdenek Novak; John Axley; William F Gaillard; Graeme E McFarland; Benjamin J Pearce; Emily L Spangler; Marc A Passman; Adam W Beck Journal: Ann Vasc Surg Date: 2022-04-13 Impact factor: 1.607
Authors: John Axley; Zdenek Novak; Juliet Blakeslee-Carter; Graeme E McFarland; Emily L Spangler; Benjamin J Pearce; Marc A Passman; Mark A Patterson; Danielle C Sutzko; Adam W Beck Journal: Ann Vasc Surg Date: 2020-09-22 Impact factor: 1.466