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Risk score to predict concurrent extracranial and intracranial artery stenosis in acute ischemic stroke.
Journal of the Neurological Sciences 2016 April 16
OBJECTIVE: The aim of this study was to develop and validate a risk score for predicting combined extracranial and intracranial artery stenosis (EICAS) in acute ischemic stroke patients.
METHODS: Patients with acute ischemic stroke were consecutively and prospectively enrolled in this study. Eligible patients were randomly divided into derivation and validation cohorts. Variables collected at presentation were used for predicting EICAS after acute ischemic stroke. Multivariable logistic regression was used to identify independent risk factors. Points for EICAS were generated by β-coefficients. Sensitivity and specificity of the risk score was assessed by the area under the receiver operating characteristic curve (AUROC). Cutoff value for predicting EICAS was determined by maximum Youden index.
RESULTS: There were 629 patients with EICAS and 352 patients without cranial artery stenosis. Among EICAS, 321 patients were in the derivation cohort and 308 in the validation cohort. Five independent predictors including age, diabetes mellitus, hypertension, Low density lipoprotein (LDL) and heart disease were used to develop a 10-point score. The score was termed ADLH2 (age, diabetes mellitus, hypertension, LDL, heart disease).The ADLH2 showed good discrimination in the derivation (AUROC 0.695; 95% confidence interval, 0.656-0.735) and validation (AUROC 0.672; 95% confidence interval, 0.631-0.712) cohort. The cutoff value at maximum Youden index was 4 with the sensitivity 0.479 and specificity 0.719. Stroke patients with ADLH2 score≥4 need further examination and therapy for EICAS.
CONCLUSIONS: The ADLH2 score is a valid risk score to identify high-risk EICAS individuals who need further investigation and treatment.
METHODS: Patients with acute ischemic stroke were consecutively and prospectively enrolled in this study. Eligible patients were randomly divided into derivation and validation cohorts. Variables collected at presentation were used for predicting EICAS after acute ischemic stroke. Multivariable logistic regression was used to identify independent risk factors. Points for EICAS were generated by β-coefficients. Sensitivity and specificity of the risk score was assessed by the area under the receiver operating characteristic curve (AUROC). Cutoff value for predicting EICAS was determined by maximum Youden index.
RESULTS: There were 629 patients with EICAS and 352 patients without cranial artery stenosis. Among EICAS, 321 patients were in the derivation cohort and 308 in the validation cohort. Five independent predictors including age, diabetes mellitus, hypertension, Low density lipoprotein (LDL) and heart disease were used to develop a 10-point score. The score was termed ADLH2 (age, diabetes mellitus, hypertension, LDL, heart disease).The ADLH2 showed good discrimination in the derivation (AUROC 0.695; 95% confidence interval, 0.656-0.735) and validation (AUROC 0.672; 95% confidence interval, 0.631-0.712) cohort. The cutoff value at maximum Youden index was 4 with the sensitivity 0.479 and specificity 0.719. Stroke patients with ADLH2 score≥4 need further examination and therapy for EICAS.
CONCLUSIONS: The ADLH2 score is a valid risk score to identify high-risk EICAS individuals who need further investigation and treatment.
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