In this study, we evaluated the diagnostic value of symptoms of chronic polyneuropathy and to construct and validate a simple questionnaire that can help diagnose chronic polyneuropathy. In a multi‐step procedure, we initially compiled a 12‐item questionnaire concerning polyneuropathy symptoms. The questionnaire was completed by 117 polyneuropathy patients and 188 controls (headache, transient ischemic attack, multiple sclerosis). First, we calculated sensitivity, specificity and likelihood ratios of each symptom. Next, we used multi‐variable logistic regression to create a model that could discriminate patients from controls, using only the most informative symptoms and their frequency of occurrence. Based on the regression coefficients, we developed a simple scoring system (Erasmus Polyneuropathy Symptom Score, E‐PSS), which was externally validated in 140 cases with chronic idiopathic axonal polyneuropathy and 96 controls without polyneuropathy. We assessed performance with discrimination (area under the curve, AUC) and calibration analyses. Numb and tingling feet were most frequently reported by polyneuropathy patients and had the highest sensitivity. Walking on cotton wool and allodynia had the highest specificity. Logistic regression yielded a model that contained these four symptoms, complemented with balance problems and tingling hands. Based on this analysis, the E‐PSS was created, ranging from 0 to 14. The E‐PSS had a good performance (AUC = 0.92) in the derivation set and proved to be valid in the external population (AUC = 0.95). In conclusion, the Erasmus Polyneuropathy Symptom Score (E‐PSS) is a simple, validated six‐item score that takes the presence and frequency of six different symptoms into account and it may be a helpful tool to screen individuals for the presence of chronic polyneuropathy.

中文翻译：

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症状在慢性多发性神经病中的诊断价值：伊拉斯mus多发性神经病症状评分。

在这项研究中，我们评估了慢性多发性神经病的症状的诊断价值，并构建和验证了可以帮助诊断慢性多发性神经病的简单问卷。在多步骤过程中，我们最初编制了有关多发性神经病症状的12项问卷。问卷由117名多发性神经病患者和188名对照（头痛，短暂性脑缺血发作，多发性硬化症）完成。首先，我们计算了每种症状的敏感性，特异性和可能性比。接下来，我们使用多变量logistic回归创建仅需提供最多信息的症状及其发生频率即可将患者与对照组区分开的模型。根据回归系数，我们开发了一个简单的评分系统（Erasmus多发性神经病症状评分，E-PSS），外部验证了140例慢性特发性轴索性多发性神经病和96例无多发性神经病的对照。我们通过辨别力（曲线下面积，AUC）和校准分析评估了性能。多发性神经病患者最常报告麻木和刺痛，其敏感性最高。在棉绒上行走和异常性疼痛具有最高的特异性。Logistic回归得出一个包含这四个症状的模型，并辅之以平衡问题和刺痛感。基于此分析，创建了E-PSS，范围从0到14。E-PSS在派生集中具有良好的性能（AUC = 0.92），并在外部人群中有效（AUC = 0.95）。总之，伊拉斯mus多神经病症状评分（E-PSS）很简单，