Huygelier Hanne, Moore Margaret Jane, Demeyere Nele, Gillebert Céline R
Department of Brain and Cognition, KU Leuven, Leuven, Belgium.
Department of Experimental Psychology, University of Oxford, Oxford, UK.
J Int Neuropsychol Soc. 2020 Aug;26(7):668-678. doi: 10.1017/S1355617720000041. Epub 2020 Mar 30.
To diagnose egocentric neglect after stroke, the spatial bias of performance on cancellation tasks is typically compared to a single cutoff. This standard procedure relies on the assumption that the measurement error of cancellation performance does not depend on non-spatial impairments affecting the total number of cancelled targets. Here we assessed the impact of this assumption on false-positive diagnoses.
We estimated false positives by simulating cancellation data using a binomial model. Performance was summarised by the difference in left and right cancelled targets (R-L) and the Centre of Cancellation (CoC). Diagnosis was based on a fixed cutoff versus cutoffs adjusted for the total number of cancelled targets and on single test performance versus unanimous or proportional agreement across multiple tests. Finally, we compared the simulation findings to empirical cancellation data acquired from 651 stroke patients.
Using a fixed cutoff, the rate of false positives depended on the total number of cancelled targets and ranged from 10% to 30% for R-L scores and from 10% to 90% for CoC scores. The rate of false positives increased even further when diagnosis was based on proportional agreement across multiple tests. Adjusted cutoffs and unanimous agreement across multiple tests were effective at controlling false positives. For empirical data, fixed versus adjusted cutoffs differ in estimation of neglect prevalence by 13%, and this difference was largest for patients with non-spatial impairments.
Our findings demonstrate the importance of considering non-spatial impairments when diagnosing neglect based on cancellation performance.
为诊断中风后的自我中心性忽视，通常将删选任务表现的空间偏差与单一临界值进行比较。这一标准程序依赖于这样一种假设，即删选表现的测量误差不取决于影响被删选目标总数的非空间损伤。在此，我们评估了这一假设对假阳性诊断的影响。
我们通过使用二项式模型模拟删选数据来估计假阳性。用左右删选目标的差异（R-L）和删选中心（CoC）来总结表现。诊断基于固定临界值与根据被删选目标总数调整的临界值，以及基于单次测试表现与多次测试中的一致或成比例一致性。最后，我们将模拟结果与从651名中风患者获取的经验性删选数据进行比较。
使用固定临界值时，假阳性率取决于被删选目标的总数，R-L分数的假阳性率在10%至30%之间，CoC分数的假阳性率在10%至90%之间。当诊断基于多次测试中的成比例一致性时，假阳性率甚至进一步增加。调整后的临界值和多次测试中的一致意见有效地控制了假阳性。对于经验性数据，固定临界值与调整后的临界值在忽视患病率估计上相差13%，且这种差异在有非空间损伤的患者中最大。
我们的研究结果表明，在基于删选表现诊断忽视时，考虑非空间损伤的重要性。
