An Accumulated Deficits Model Predicts Perioperative and Long-term Adverse Events after Carotid Endarterectomy

Abstract

Background There is increasing recognition that decreased reserve in multiple organ systems, known as accumulated deficits (AD), may better stratify perioperative risk than traditional risk indices. We hypothesized that an AD model would predict both perioperative adverse events and long-term survival after carotid endarterectomy (CEA), particularly important in asymptomatic patients. Methods Consecutive patients undergoing CEA between 1st January 2000 and 31st December 2010 were retrospectively identified. Seven of the deficit items from the Canadian Study of Health and Aging–frailty index (coronary disease, renal insufficiency, pulmonary disease, peripheral vascular disease, heart failure, hypertension, and diabetes) were tabulated for each patient. Predictors of perioperative and long-term outcomes were evaluated using regression analysis. Results About 1,782 CEAs in 1,496 patients (mean age: 71.3 ± 9.3 years, 56.3% male, 35.4% symptomatic) were included. The risk of major adverse events (stroke, death, or myocardial infarction) at 30 days for patients with ≤3 deficits was 2.53% vs. 8.81% for patients with ≥4 deficits (P < 0.001). For patients with ≥5 deficits, the risk was 15.18%. Each additional deficit increased the odds of a 30-day major adverse event and hospital stay >2 days by 1.64 (P < 0.001) and 1.15 (P < 0.001), respectively. In multivariate analysis, the presence of ≥4 deficits was more predictive of perioperative major adverse events (odds ratio [OR] = 3.62, P < 0.001) than symptomatology within 6 months (OR = 1.57, P = 0.08) or octogenarian status (OR = 2.00, P = 0.02). Kaplan-Meier analysis showed significantly decreased survival over time with accumulating deficits (P < 0.001). Patients with ≥4 deficits have a hazards ratio for death of 2.6 compared to patients with ≤3 deficits (P < 0.001). Overall survival is estimated at 79.5% (95% confidence interval [CI]: 0.77–0.82) at 5 years in patients with ≤3 deficits versus 52.4% (95% CI: 0.46–0.58) in patients with ≥4 deficits, respectively. In subgroup analysis of asymptomatic patients, 5-year survival for octogenarian male patients with ≥4 deficits was only 26.8%. For asymptomatic males aged 70–79 years with ≥4 deficits, 5-year survival was 59.9%. Conclusions An AD model is more predictive of perioperative adverse events after CEA than age or symptomatic status. This model remains predictive of long-term survival. In asymptomatic male octogenarians with 4 or more AD, 5-year survival is severely limited.

External Validation of Risk Prediction Models to Improve Selection of Patients for Carotid Endarterectomy

“…After screening of 788 unique reports and assessing the full-texts of 59 for eligibility, we included 15 studies reporting 17 prediction models (Figure 1 and Table V in the Data Supplement). [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] Two (12%) models were developed in populations of symptomatic patients, 33,34 5 (29%) models in populations of asymptomatic patients, [35][36][37] and 9 (53%) in populations of both symptomatic and asymptomatic patients (Table 1). Symptomatic status was included as predictor in these nine prediction models [24][25][26][27][28][29][30][31][32] of which one used type of qualifying event as predictor.…”

“…[24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] Two (12%) models were developed in populations of symptomatic patients, 33,34 5 (29%) models in populations of asymptomatic patients, [35][36][37] and 9 (53%) in populations of both symptomatic and asymptomatic patients (Table 1). Symptomatic status was included as predictor in these nine prediction models [24][25][26][27][28][29][30][31][32] of which one used type of qualifying event as predictor. 28 Other predictors used frequently in the 17 included models were age in 8 (47%), [25][26][27][28][29]32,36,38 sex in 7 (41%), 28,32,34,35,37 heart failure in 11 (65%), [24][25][26]…”

“…Symptomatic status was included as predictor in these nine prediction models [24][25][26][27][28][29][30][31][32] of which one used type of qualifying event as predictor. 28 Other predictors used frequently in the 17 included models were age in 8 (47%), [25][26][27][28][29]32,36,38 sex in 7 (41%), 28,32,34,35,37 heart failure in 11 (65%), [24][25][26][27][28][29][30]37,38 coronary heart disease in 7 (41%), 24,28,[35][36][37]…”

“…Four (24%) models considered outcome events before discharge [25][26][27]38 and 13 (76%) outcome events during 30-days after CEA. 24,[28][29][30][31][32][33][34][35][36][37] Nine models (59%) were internally validated (Table VI in the Data Supplement). 28,29,[34][35][36][37][38]…”

“…The overall risk of bias was deemed low in 4 models, 25,27,35,36 unclear in one model, 28 and high in 12 models. 24,26,[29][30][31][32][33][34]37,38 Concerns with applicability of the models to our population of interest was deemed low in 5 models, 26,27,29,30,35 and high in 12 models. 24,25,28,[31][32][33][34][36][37][38] Reasons for high concerns included using a different predicted outcome for development compared with our external validation or using single center data for development.…”