Olde Dubbelink Tom B G, De Kleermaeker Floriaan G C M, Meulstee Jan, Bartels Ronald H M A, Claes Franka, Verhagen Wim I M
Department of Neurology, Canisius-Wilhelmina Hospital, Nijmegen, Netherlands.
Department of Neurology, VieCuri Hospital, Venlo, Netherlands.
Front Neurol. 2020 Sep 25;11:577052. doi: 10.3389/fneur.2020.577052. eCollection 2020.
In diagnosing carpal tunnel syndrome (CTS) there is no consensus about the upper limit of normal (ULN) of the cross-sectional area (CSA) of the median nerve at the carpal tunnel inlet. A previous study showed wrist circumference is the most important independent predictor for the ULN. In this study we optimised a wrist circumference-dependent ULN equation for optimal diagnostic accuracy and compared it to the generally used fixed ULN of 11 mm. CSA and wrist circumference were measured in a prospective cohort of 253 patients (clinically defined CTS) and 96 healthy controls. An equation for the ULN for CSA was developed by means of univariable regression analysis. We calculated -scores for all patients and healthy controls, and analysed these scores in a ROC curve and a decision plot. Sensitivity and specificity were determined and compared to fixed ULN values. We found augmented diagnostic accuracy of our newly developed equation y = 0.88 x -4.0, where y = the ULN of the CSA and x = wrist circumference. This equation has a corresponding sensitivity and specificity of 75% compared to a sensitivity of 70% while using a fixed cut-off value of 11 mm ( = 0.015). Optimising the regression equation for wrist circumference-dependent ULN cross-sectional area of the median nerve at the wrist inlet might improve diagnostic accuracy of ultrasonography in patients with carpal tunnel syndrome and seems to be more accurate than using fixed cut-off values.
在诊断腕管综合征(CTS)时,对于腕管入口处正中神经横截面积(CSA)的正常上限(ULN)尚无共识。先前的一项研究表明,腕围是ULN最重要的独立预测因素。在本研究中,我们优化了一个依赖于腕围的ULN方程以获得最佳诊断准确性,并将其与常用的11mm固定ULN进行比较。在一个前瞻性队列中,对253例患者(临床诊断为CTS)和96例健康对照者测量了CSA和腕围。通过单变量回归分析得出了CSA的ULN方程。我们计算了所有患者和健康对照者的z评分,并在ROC曲线和决策图中分析了这些评分。确定了敏感性和特异性,并与固定的ULN值进行比较。我们发现新开发的方程y = 0.88x - 4.0具有更高的诊断准确性,其中y = CSA的ULN,x =腕围。与使用11mm固定临界值时70%的敏感性相比,该方程的敏感性和特异性为75%(P = 0.015)。优化腕管入口处依赖于腕围的正中神经ULN横截面积的回归方程可能会提高超声检查对腕管综合征患者的诊断准确性,并且似乎比使用固定临界值更准确。