Computed tomography angiographic biomarkers help identify vulnerable carotid artery plaque.

OBJECTIVE: The current risk assessment for patients with carotid atherosclerosis relies primarily on measuring the degree of stenosis. More reliable risk stratification could improve patient selection for targeted treatment. We have developed and validated a model to predict for major adverse neurologic events (MANE; stroke, transient ischemic attack, amaurosis fugax) that incorporates a combination of plaque morphology, patient demographics, and patient clinical information. METHODS: We enrolled 221 patients with asymptomatic carotid stenosis of any severity who had undergone computed tomography angiography at baseline and ≥6 months later. The images were analyzed for carotid plaque morphology (plaque geometry and tissue composition). The data were partitioned into training and validation cohorts. Of the 221 patients, 190 had complete records available and were included in the present analysis. The training cohort was used to develop the best model for predicting MANE, incorporating the patient and plaque features. First, single-variable correlation and unsupervised clustering were performed. Next, several multivariable models were implemented for the response variable of MANE. The best model was selected by optimizing the area under the receiver operating characteristic curve (AUC) and Cohen's kappa statistic. The model was validated using the sequestered data to demonstrate generalizability. RESULTS: A total of 62 patients had experienced a MANE during follow-up. Unsupervised clustering of the patient and plaque features identified single-variable predictors of MANE. Multivariable predictive modeling showed that a combination of the plaque features at baseline (matrix, intraplaque hemorrhage [IPH], wall thickness, plaque burden) with the clinical features (age, body mass index, lipid levels) best predicted for MANE (AUC, 0.79), In contrast, the percent diameter stenosis performed the worst (AUC, 0.55). The strongest single variable for discriminating between patients with and without MANE was IPH, and the most predictive model was produced when IPH was considered with wall remodeling. The selected model also performed well for the validation dataset (AUC, 0.64) and maintained superiority compared with percent diameter stenosis (AUC, 0.49). CONCLUSIONS: A composite of plaque geometry, plaque tissue composition, patient demographics, and clinical information predicted for MANE better than did the traditionally used degree of stenosis alone for those with carotid atherosclerosis. Implementing this predictive model in the clinical setting could help identify patients at high risk of MANE.

Preoperative risk score for the prediction of mortality after repair of ruptured abdominal aortic aneurysms.

OBJECTIVE: Even in the ruptured endovascular aneurysm repair first era, there are still patients who will not survive their ruptured abdominal aortic aneurysm (rAAA). All previously published mortality risk scores include intraoperative variables and are not helpful with the decision to operate or in providing preoperative patient and family counseling. The purpose of this study was to develop a practical preoperative risk score to predict mortality after repair of rAAA. METHODS: Data of all patients with rAAA presenting between January 1, 2002, and October 31, 2013, were collected. Logistic regression was used to evaluate predictive variables both univariately and jointly, and the results of multivariate models guided the definition of the final simplified scoring algorithm. RESULTS: There were 303 patients who presented during the study period. Sixteen patients died in the emergency department, en route to surgery, or after choosing comfort care. Preoperative variables most predictive of mortality were age >76 years (odds ratio [OR], 2.11; confidence interval [CI], 1.47-4.97; P = .011), creatinine concentration >2.0 mg/dL (OR, 3.66; CI, 1.85-7.24; P < .001), pH <7.2 (OR, 2.58; CI, 1.27-5.24; P = .009), and systolic blood pressure ever <70 mm Hg (OR, 2.70; CI, 1.46-4.97; P = .002). Assigning 1 point for each variable, patients were stratified according to the preoperative rAAA mortality risk score (range, 0-4). For all repairs, at 30 days, patients with 1 point suffered 22% mortality; 2 points, 69% mortality; and 3 points, 80% mortality. All patients with 4 points died. There was a mortality benefit for ruptured endovascular aneurysm repair across all categories. CONCLUSIONS: Our rAAA mortality risk score is based on four variables readily assessed in the emergency department and allows accurate prediction of 30-day mortality after repair of rAAAs. It also has a direct impact on clinical decision-making by adding prognostic information to the decision to transfer patients to tertiary care centers and aiding in preoperative discussions with patients and their families.