Koh Justin, Azari Kodi K, Benhaim Prosper
University of California, Los Angeles, CA, USA.
Hand (N Y). 2017 Jan;12(1):43-49. doi: 10.1177/1558944716654660. Epub 2016 Jun 8.
Coincident carpal and cubital tunnel syndromes present a diagnostic challenge, exacerbated by the limitations of nerve conduction study (NCS) for confirming cubital tunnel syndrome. This study develops a diagnostic scoring system, the Koh-Benhaim (KB) score, to identify patients with coincident compression neuropathies. A retrospective review of 515 patients was performed from patients surgically treated for carpal and/or cubital tunnel release. These patients were divided as patients with isolated carpal tunnel syndrome (n = 337) or coincident carpal and cubital tunnel syndromes (n = 178), then characterized according to demographics, medical history, physical examination, and NCS results. Univariate and multivariate logistic regression identified predictors of coincident neuropathy. A clinical score was constructed by integerizing regression coefficients of predictive factors. Receiver operating characteristic (ROC) curves were generated for each iteration of the score. Sensitivities, specificities, and positive and negative predictive values were calculated to identify the best cutoff value. Decreased intrinsic muscle strength, decreased ulnar sensation, positive elbow flexion test, positive cubital tunnel Tinel's sign, and abnormal NCS result were selected. The cutoff value for high risk of coincident compression was 3 points: positive predictive value, 82.9% and specificity, 93.4%. Model performance was very good-ROC area under the curve of 0.917. A KB score of 3 or greater represents high risk of coincident cubital tunnel compression. The variables involved are routinely used to assess the cubital tunnel, and all component factors of the KB score were of equivalent clinical weight in assessing patients with potential coincident compression neuropathy.
同时存在的腕管综合征和肘管综合征带来了诊断挑战，而用于确诊肘管综合征的神经传导研究（NCS）的局限性又加剧了这一挑战。本研究开发了一种诊断评分系统，即科-本海姆（KB）评分，以识别患有合并压迫性神经病变的患者。对515例行腕管和/或肘管松解手术治疗的患者进行了回顾性研究。这些患者被分为单纯腕管综合征患者（n = 337）或同时患有腕管和肘管综合征的患者（n = 178），然后根据人口统计学、病史、体格检查和NCS结果进行特征描述。单因素和多因素逻辑回归分析确定了合并神经病变的预测因素。通过对预测因素的回归系数进行整数化构建临床评分。为评分的每次迭代生成受试者工作特征（ROC）曲线。计算敏感性、特异性以及阳性和阴性预测值，以确定最佳临界值。选择了内在肌力量下降、尺侧感觉减退、肘部屈曲试验阳性、肘管Tinel征阳性和NCS结果异常。合并压迫高风险的临界值为3分：阳性预测值为82.9%，特异性为93.4%。模型性能非常好——曲线下面积（ROC）为0.917。KB评分为3分或更高表示合并肘管受压的高风险。所涉及的变量通常用于评估肘管，并且KB评分的所有组成因素在评估潜在合并压迫性神经病变的患者时具有同等的临床权重。
