Mi Ji Lee1, Jeong Pyo Son1, Suk Jae Kim1, Sookyung Ryoo1, Sook-Young Woo1, Jihoon Cha1, Gyeong-Moon Kim1, Chin-Sang Chung1, Kwang Ho Lee1, Oh Young Bang2. 1. From the Departments of Neurology (M.J.L., S.J.K., S.R., G.-M.K., C.-S.C., K.H.L., O.Y.B.) and Radiology (J.C.), Samsung Medical Center, School of Medicine (M.J.L., S.J.K., S.R., G.-M.K., C.-S.C., K.H.L., O.Y.B., J.C.) and Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology (J.P.S., O.Y.B.), Sungkyunkwan University, Seoul, Korea; and Biostatistics Team, Samsung Biomedical Research Institute, Seoul, Korea (S.-Y.W.). 2. From the Departments of Neurology (M.J.L., S.J.K., S.R., G.-M.K., C.-S.C., K.H.L., O.Y.B.) and Radiology (J.C.), Samsung Medical Center, School of Medicine (M.J.L., S.J.K., S.R., G.-M.K., C.-S.C., K.H.L., O.Y.B., J.C.) and Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology (J.P.S., O.Y.B.), Sungkyunkwan University, Seoul, Korea; and Biostatistics Team, Samsung Biomedical Research Institute, Seoul, Korea (S.-Y.W.). ohyoung.bang@samsung.com.
Abstract
BACKGROUND AND PURPOSE: Good collateral flow is an important predictor for favorable responses to recanalization therapy and successful outcomes after acute ischemic stroke. Magnetic resonance perfusion-weighted imaging (MRP) is widely used in patients with stroke. However, it is unclear whether the perfusion parameters and thresholds would predict collateral status. The present study evaluated the relationship between hypoperfusion severity and collateral status to develop a predictive model for good collaterals using MRP parameters. METHODS: Patients who were eligible for recanalization therapy that underwent both serial diffusion-weighted imaging and serial MRP were enrolled into the study. A collateral flow map derived from MRP source data was generated through automatic postprocessing. Hypoperfusion severity, presented as proportions of every 2-s Tmax strata to the entire hypoperfusion volume (Tmax≥2 s), was compared between patients with good and poor collaterals. Prediction models for good collaterals were developed with each Tmax strata proportion and cerebral blood volumes. RESULTS: Among 66 patients, 53 showed good collaterals based on MRP-based collateral grading. Although no difference was noted in delays within 16 s, more severe Tmax delays (Tmax16-18 s, Tmax18-22 s, Tmax22-24 s, and Tmax>24 s) were associated with poor collaterals. The probability equation model using Tmax strata proportion demonstrated high predictive power in a receiver operating characteristic analysis (area under the curve=0.9303; 95% confidence interval, 0.8682-0.9924). The probability score was negatively correlated with the volume of infarct growth (P=0.030). CONCLUSIONS: Collateral status is associated with more severe Tmax delays than previously defined. The present Tmax severity-weighted model can determine good collaterals and subsequent infarct growth.
BACKGROUND AND PURPOSE: Good collateral flow is an important predictor for favorable responses to recanalization therapy and successful outcomes after acute ischemic stroke. Magnetic resonance perfusion-weighted imaging (MRP) is widely used in patients with stroke. However, it is unclear whether the perfusion parameters and thresholds would predict collateral status. The present study evaluated the relationship between hypoperfusion severity and collateral status to develop a predictive model for good collaterals using MRP parameters. METHODS:Patients who were eligible for recanalization therapy that underwent both serial diffusion-weighted imaging and serial MRP were enrolled into the study. A collateral flow map derived from MRP source data was generated through automatic postprocessing. Hypoperfusion severity, presented as proportions of every 2-s Tmax strata to the entire hypoperfusion volume (Tmax≥2 s), was compared between patients with good and poor collaterals. Prediction models for good collaterals were developed with each Tmax strata proportion and cerebral blood volumes. RESULTS: Among 66 patients, 53 showed good collaterals based on MRP-based collateral grading. Although no difference was noted in delays within 16 s, more severe Tmax delays (Tmax16-18 s, Tmax18-22 s, Tmax22-24 s, and Tmax>24 s) were associated with poor collaterals. The probability equation model using Tmax strata proportion demonstrated high predictive power in a receiver operating characteristic analysis (area under the curve=0.9303; 95% confidence interval, 0.8682-0.9924). The probability score was negatively correlated with the volume of infarct growth (P=0.030). CONCLUSIONS: Collateral status is associated with more severe Tmax delays than previously defined. The present Tmax severity-weighted model can determine good collaterals and subsequent infarct growth.
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