title: Predictive multivariate regression to increase the specificity of carotid duplex ultrasound for high-grade stenosis in asymptomatic patients.
abstract: BACKGROUND
  Carotid duplex ultrasound (CDUS) is commonly used to screen for carotid artery stenosis. Specificities of CDUS criteria however are lower than sensitivities, potentially resulting in false-positive examinations with subsequent unnecessary imaging or surgery. Our objective was to establish a multivariate logistic regression to increase the specificity of CDUS for high-grade (≥70%) stenosis.
  
  
  METHODS
  A retrospective review collected CDUS velocities and radiographic measurements from patients who underwent both CDUS and computed tomography angiography (CTA). After stratification with standard CDUS criteria, a logistic regression was created using peak systolic velocity (PSV), end diastolic velocity (EDV), and PSV ratio (PSV of internal carotid artery [ICA]/PSV of common carotid artery [CCA]) as predictor variables. A receiver operating characteristic curve was generated to test the model's predictive ability. A cutoff probability for unequivocal high-grade stenosis was chosen based on optimal specificity. The regression model was applied to patients with equivocal high-grade stenosis. Probabilities for detection of high-grade stenosis were calculated. Descriptive statistics were generated to quantify the accuracy of the model.
  
  
  RESULTS
  A total of 244 vessels were included. Standardized velocity criteria for ≥70% stenosis yielded a sensitivity of 90.6% (95% confidence interval [CI], 82.3-95.6%), specificity of 63.5% (95% CI, 55.4-70.5%), positive predictive value (PPV) of 57.0% (95% CI, 48.8-65.5%), and negative predictive value (NPV) of 92.7% (95% CI, 85.8-96.5%). Regression analysis produced a model for predicting the probability of high-grade stenosis defined as probability = logit(-1) (-4.97 + [0.00938 × PSV] + [0.0135 × EDV] + [0.103 × PSV ICA/CCA ratio]). A cutoff probability of 0.65 for high-grade stenosis yielded a sensitivity of 54.7% (95% CI, 43.9-65.0%), specificity of 94.3% (95% CI, 89.3-97.2%), PPV of 83.9% (95% CI, 71.6-91.9%), and NPV of 79.3% (95% CI, 72.8-84.5%). A cutoff PSV of 400 cm/sec was chosen for unequivocal stenosis of ≥70%. A total of 94 patients were found to meet criteria for high-grade stenosis (PSV ≥ 230 cm/sec) but fall short of criteria for unequivocal high-grade stenosis (PSV < 400 cm/sec). Application of the regression model resulted in identification of 15 patients with probability ≥0.65 for high-grade stenosis and 79 patients with probability <0.65. This resulted in a 16% potential reduction in CTA scans.
  
  
  CONCLUSIONS
  Our regression model provides increased specificity of CDUS for high-grade stenosis in patients who have met initial highly sensitive screening criteria. Application of this model may limit the need for additional imaging and increase the threshold for operative intervention in asymptomatic patients with equivocal high-grade carotid stenosis.
authors: University of Rochester School of Medicine and Dentistry, Rochester, NY 14642.
authors: Anthony P
authors: AP
authors: Carnicelli
authors: University of Rochester School of Medicine and Dentistry, Rochester, NY 14642.
authors: Jonathan J
authors: JJ
authors: Stone
authors: University of Rochester School of Medicine and Dentistry, Rochester, NY 14642.
authors: Adam
authors: A
authors: Doyle
authors: University of Rochester School of Medicine and Dentistry, Rochester, NY 14642.
authors: Amit
authors: A
authors: Chowdhry
authors: University of Rochester School of Medicine and Dentistry, Rochester, NY 14642.
authors: David L
authors: DL
authors: Gillespie
authors: University of Rochester School of Medicine and Dentistry, Rochester, NY 14642. Electronic address: ankur_chandra@urmc.rochester.edu.
authors: Ankur
authors: A
authors: Chandra
mesh_terms: D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D058070:Asymptomatic Diseases; D001783:Blood Flow Velocity; D017536:Carotid Artery, Common / Q000000981:diagnostic imaging* / Q000503:physiopathology; D002343:Carotid Artery, Internal / Q000000981:diagnostic imaging* / Q000503:physiopathology; D016893:Carotid Stenosis / Q000000981:diagnostic imaging* / Q000503:physiopathology / Q000628:therapy; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D012372:ROC Curve; D012039:Regional Blood Flow; D012189:Retrospective Studies; D012720:Severity of Illness Index; D018616:Ultrasonography, Doppler, Duplex