title: Development of a duplex-derived velocity risk prediction model of disease progression in patients with moderate asymptomatic carotid artery stenosis.
abstract: OBJECTIVE
  Previously, we described risk factors for disease progression in moderate asymptomatic carotid artery stenosis (ASCAS). The aim of the current study was to develop a risk prediction model for disease progression in this group.
  
  
  METHODS
  All patients presenting between January 2005 and May 2012 with moderate (50%-69%) ASCAS, as determined by carotid artery duplex imaging, were included. Cox proportional hazard regression models accounting for measured duplex peak systolic velocity and end-diastolic velocity, and the internal carotid artery (ICA)/common carotid artery (CCA) ratio, with and without previously identified risk factors for progression (age, smoking, dual antiplatelet therapy), were used to develop receiver operating characteristic curves for predicting disease progression.
  
  
  RESULTS
  The study analyzed 282 patients (52% male), aged 71 ± 9 years, with 2.6 ± 0.1 years follow-up and 25% disease progression at a mean time of 2.02 ± 0.18 years. Initial peak systolic velocity, end-diastolic velocity, and the ICA/CCA ratio were all significant independent predictors of progression. Receiver operating characteristic curve analyses suggested that a prediction model based on ICA/CCA ratio alone had optimal prediction efficacy (hazard ratio, 2.01; Harrell's C, 0.74; P < .001). Patients with ICA/CCA >2.5, 3.3, and 3.8 were found to have >10%, >20%, and >30% risk of disease progression over 2 years, respectively. Model sensitivity and specificity for predicting 10% risk of disease progression at 2 years was 80.7% and 64.0%, respectively (positive predictive value, 22.9%; negative predictive value, 96.1%).
  
  
  CONCLUSIONS
  We propose a clinical prediction model for moderate ASCAS disease progression that can be used to risk-stratify patients with >10% risk of progression at 2 years using ICA/CCA ratios. Implementation of this model may be useful for identifying high-risk patients who would benefit from routine carotid disease surveillance follow-up.
authors.affiliation: Division of Vascular and Endovascular Therapy, Johns Hopkins Medical Institutions, Baltimore, Md.
authors.forename: Caitlin W
authors.initials: CW
authors.lastname: Hicks
authors.affiliation: Division of Vascular and Endovascular Therapy, Johns Hopkins Medical Institutions, Baltimore, Md.
authors.forename: Joseph K
authors.initials: JK
authors.lastname: Canner
authors.affiliation: Division of Vascular and Endovascular Therapy, Johns Hopkins Medical Institutions, Baltimore, Md.
authors.forename: Isibor
authors.initials: I
authors.lastname: Arhuidese
authors.affiliation: Division of Vascular and Endovascular Therapy, Johns Hopkins Medical Institutions, Baltimore, Md.
authors.forename: Natalia O
authors.initials: NO
authors.lastname: Glebova
authors.affiliation: Division of Vascular and Endovascular Therapy, Johns Hopkins Medical Institutions, Baltimore, Md.
authors.forename: Eric
authors.initials: E
authors.lastname: Schneider
authors.affiliation: Division of Vascular and Endovascular Therapy, Johns Hopkins Medical Institutions, Baltimore, Md.
authors.forename: Umair
authors.initials: U
authors.lastname: Qazi
authors.affiliation: Division of Vascular and Endovascular Therapy, Johns Hopkins Medical Institutions, Baltimore, Md.
authors.forename: Bruce
authors.initials: B
authors.lastname: Perler
authors.affiliation: Division of Vascular and Endovascular Therapy, Johns Hopkins Medical Institutions, Baltimore, Md. Electronic address: bmalas1@jhmi.edu.
authors.forename: Mahmoud B
authors.initials: MB
authors.lastname: Malas
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; 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; D016009:Chi-Square Distribution; D003661:Decision Support Techniques; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012372:ROC Curve; D012039:Regional Blood Flow; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D018616:Ultrasonography, Doppler, Duplex