PDF(Pubmed)
Abstract：
UNASSIGNED: Head computed tomography (CT) is used to predict neurological outcome after cardiac arrest (CA). The current reference standard includes quantitative image analysis by a neuroradiologist to determine the Gray-White-Matter Ratio (GWR) which is calculated via the manual measurement of radiodensity in different brain regions. Recently, automated analysis methods have been introduced. There is limited data on the Inter-rater agreement of both methods.
UNASSIGNED: Three blinded human raters (neuroradiologist, neurologist, student) with different levels of clinical experience retrospectively assessed the Gray-White-Matter Ratio (GWR) in head CTs of 95 CA patients. GWR was also quantified by a recently published computer algorithm that uses coregistration with standardized brain spaces to identify regions of interest (ROIs). We calculated intraclass correlation (ICC) for inter-rater agreement between human and computer raters as well as area under the curve (AUC) and sensitivity/specificity for poor outcome prognostication.
UNASSIGNED: Inter-rater agreement on GWR was very good (ICC 0.82-0.84) between all three human raters across different levels of expertise and between the computer algorithm and neuroradiologist (ICC 0.83; 95% CI 0.78-0.88). Despite high overall agreement, we observed considerable, clinically relevant deviations of GWR measurements (up to 0.24) in individual patients. In our cohort, at a GWR threshold of 1.10, this did not lead to any false poor neurological outcome prediction.
UNASSIGNED: Human and computer raters demonstrated high overall agreement in GWR determination in head CTs after CA. The clinically relevant deviations of GWR measurement in individual patients underscore the necessity of additional qualitative evaluation and integration of head CT findings into a multimodal approach to prognostication of neurological outcome after CA.
UNASSIGNED: Three blinded human raters (neuroradiologist, neurologist, student) with different levels of clinical experience retrospectively assessed the Gray-White-Matter Ratio (GWR) in head CTs of 95 CA patients. GWR was also quantified by a recently published computer algorithm that uses coregistration with standardized brain spaces to identify regions of interest (ROIs). We calculated intraclass correlation (ICC) for inter-rater agreement between human and computer raters as well as area under the curve (AUC) and sensitivity/specificity for poor outcome prognostication.
UNASSIGNED: Inter-rater agreement on GWR was very good (ICC 0.82-0.84) between all three human raters across different levels of expertise and between the computer algorithm and neuroradiologist (ICC 0.83; 95% CI 0.78-0.88). Despite high overall agreement, we observed considerable, clinically relevant deviations of GWR measurements (up to 0.24) in individual patients. In our cohort, at a GWR threshold of 1.10, this did not lead to any false poor neurological outcome prediction.
UNASSIGNED: Human and computer raters demonstrated high overall agreement in GWR determination in head CTs after CA. The clinically relevant deviations of GWR measurement in individual patients underscore the necessity of additional qualitative evaluation and integration of head CT findings into a multimodal approach to prognostication of neurological outcome after CA.
摘要：
未经证实：头部计算机断层扫描（CT）用于预测心脏骤停（CA）后的神经系统预后。当前的参考标准包括由神经放射学家进行的定量图像分析，以确定通过手动测量不同大脑区域的放射密度来计算的灰白物质比（GWR）。最近,介绍了自动分析方法。关于这两种方法的评分者间协议的数据有限。
未经授权：三个盲目的人类评估者（神经放射学家，神经科医生,具有不同临床经验的student）回顾性评估了95例CA患者的头部CT的灰白物质比（GWR）。GWR还通过最近发布的计算机算法进行了量化，该算法使用与标准化大脑空间的共配准来识别感兴趣区域（ROI）。我们计算了人和计算机评估者之间评估者之间的组内相关性（ICC），以及曲线下面积（AUC）和不良结果预测的敏感性/特异性。
UNASSIGNED：在不同专业水平的所有三个人类评估者之间以及计算机算法和神经放射学家之间（ICC0.83；95％CI0.78-0.88）之间，关于GWR的评估者之间的协议非常好（ICC0.82-0.84）。尽管总体上达成了很高的共识，我们观察到相当多的，个别患者GWR测量值的临床相关偏差（高达0.24）。在我们的队列中，在GWR阈值为1.10时,这并未导致任何错误的不良神经学结局预测.
UNASSIGNED：人类和计算机评估人员在CA后的头部CT中在GWR确定方面表现出高度的总体一致性。个别患者的GWR测量的临床相关偏差强调了需要进行额外的定性评估并将头部CT发现整合到多模式方法中以预测CA后的神经系统结局。
未经授权：三个盲目的人类评估者（神经放射学家，神经科医生,具有不同临床经验的student）回顾性评估了95例CA患者的头部CT的灰白物质比（GWR）。GWR还通过最近发布的计算机算法进行了量化，该算法使用与标准化大脑空间的共配准来识别感兴趣区域（ROI）。我们计算了人和计算机评估者之间评估者之间的组内相关性（ICC），以及曲线下面积（AUC）和不良结果预测的敏感性/特异性。
UNASSIGNED：在不同专业水平的所有三个人类评估者之间以及计算机算法和神经放射学家之间（ICC0.83；95％CI0.78-0.88）之间，关于GWR的评估者之间的协议非常好（ICC0.82-0.84）。尽管总体上达成了很高的共识，我们观察到相当多的，个别患者GWR测量值的临床相关偏差（高达0.24）。在我们的队列中，在GWR阈值为1.10时,这并未导致任何错误的不良神经学结局预测.
UNASSIGNED：人类和计算机评估人员在CA后的头部CT中在GWR确定方面表现出高度的总体一致性。个别患者的GWR测量的临床相关偏差强调了需要进行额外的定性评估并将头部CT发现整合到多模式方法中以预测CA后的神经系统结局。
