ECAS progression score: a web-based model to predict progression of extracranial carotid artery stenosis.

BACKGROUND AND PURPOSE: To develop and validate a risk model (Extracranial Carotid Artery Stenosis progression score, ECAS-PS) and to predict risk of ECAS progression. METHODS: The ECAS-PS was developed based on the Renqiu Stroke Screening Study (RSSS), in which eligible participants were randomly divided into derivation (60%) and validation (40%) cohorts. ECAS at baseline and follow-up was diagosed by carotid duplex ultrasound according to the published criteria. ECAS progression was defined as an increase in ECAS to≥50% for those with a baseline of <50% or as an increase to a higher category of stenosis if the baseline stenosis was ≥50%. Independent predictors of ECAS progression 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 4111 participants were included and the mean age was 64.3. A total number of 29 (0.7%), 24 (0.6%) and 48 (1.2%) patients progressed during 2-year follow-up for left, right and bilateral (either left or right) carotid artery, respectively. The ECAS-PS was developed from a set of predictors of ECAS progression. The ECAS-PS demonstrated good discrimination in both the derivation and validation cohorts (AUROC range: 0.824-0.917). The Hosmer-Lemeshow tests of ECAS progression score were not significant in the derivation and validation cohorts (all P > 0.05). CONCLUSION: The ECAS progression score is a valid model for predicting the risk of ECAS progression. Further validation of the ECAS-PS in different populations and larger samples is warranted.

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

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.