ECAS score: a web-based risk model to predict moderate and severe extracranial carotid artery stenosis

Abstract

Background and purpose

To develop and validate a risk model (Extracranial Carotid Artery Stenosis score, ECAS score) to predict moderate and severe ECAS. Furthermore, we compared discrimination of the ECAS score and three existing models with regard to both moderate and severe ECAS.

Methods

The ECAS score was developed based on the Renqiu Stroke Screening Study (RSSS), in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. ECAS was diagnosed by carotid duplex ultrasound according to the published criteria. Independent predictors of moderate (≥50%) and severe (≥70%) ECAS were obtained using multivariable logistic regression. The area under the receiver operating characteristic curve (AUROC) and the Hosmer–Lemeshow test were used to assess model discrimination and calibration.

Results

A total of 5010 participants were included and the mean age was 64.3. The proportion of ECAS of < 50%, 50–69%, 70–99% and occlusion was 4.4, 0.5, 0.4, and 0.4%, respectively. The ECAS score was developed from sets of predictors of moderate and severe ECAS. The ECAS score demonstrated good discrimination in the derivation and validation cohorts (AUROC range: 0.785–0.846). The Hosmer–Lemeshow tests of ECAS score for moderate and severe ECAS were not significant in the derivation and validation cohorts (all P > 0.05). When compared to the three existing models, the ECAS score showed significantly better discrimination for both moderate and severe ECAS (all P < 0.001).

Conclusion

The ECAS score is a valid model for predicting moderate and severe ECAS. Further validation of the ECAS score in different populations and larger samples is warranted.

Keywords:

• Extracranial carotid artery
• stenosis
• risk model
• prediction