Diagnostic Tool Predicting Results of Electrodiagnostic Tests in Patients Presenting With Musculoskeletal Symptoms.

Q3 Medicine

Journal of Clinical Neuromuscular Disease Pub Date : 2026-06-01 Epub Date: 2026-06-03 DOI:10.1097/CND.0000000000000529

Najwa Al-Bustani, Zulfiqar Hussain, Kim Byrn, Rehmat Karim

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引用次数: 0

Abstract

Objectives: Patients presenting with musculoskeletal symptoms are fairly common in any neurologist practice, their presentation raises concern of underlying radiculopathy as a cause of their symptoms. The role of electrodiagnostic study has been variable in such patients. Creating a diagnostic tool will be of most help to provide physicians of predictive value of such tests before ordering and help guide them in finding the most appropriate test.

Methods: Retrospective chart review study of 444 patients older than 18 years. The patients were referred to the electrodiagnostic laboratory with musculoskeletal symptoms such as neck, back, or limb pain; extremity numbness; or to rule out radiculopathy.

Results: A total of 308 patients met predefined inclusion criteria. Variables such as age, extremity numbness, back pain, and other symptoms were identified as significant predictors for abnormal results. A diagnostic tool was developed using these variables, scoring each as normal (0) or abnormal (1), with age as the only numerical input. For nerve conduction study (NCS) prediction, significant factors included age [P < 0.01, odds ratio (OR) 0.95], extremity numbness (P = 0.03, OR 0.42), other symptoms (P = 0.04, OR 0.29), and normal magnetic resonance imaging (P = 0.02, OR 2.99). For electromyography (EMG) prediction, age (P < 0.01, OR 0.95), back pain (P = 0.02, OR 0.43), and other symptoms (P = 0.03, OR 0.29) were significant. The logistic regression models for predicting abnormal NCS and EMG exhibited areas under the receiver operating curves of 0.807 and 0.777, respectively. With 95% specificity, the NCS model had a sensitivity of 24.10%, positive predictive value of 68.97%, and negative predictive value of 72.84%, while the EMG model had a sensitivity of 32.14%, positive predictive value of 75%, and negative predictive value of 74.67%.

Conclusions: This study highlights the potential for using a scoring system to predict NCS and EMG outcomes based on key clinical variables.

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预测肌肉骨骼症状患者电诊断试验结果的诊断工具

目的：在任何神经科医生的实践中，以肌肉骨骼症状为表现的患者是相当常见的，他们的表现引起了对潜在神经根病作为其症状原因的关注。在这类患者中，电诊断研究的作用是可变的。创建一个诊断工具将是最有帮助的，为医生提供这些测试的预测价值，然后再订购，并帮助指导他们找到最合适的测试。方法：对444例18岁以上患者进行回顾性图表复习研究。有肌肉骨骼症状（如颈部、背部或肢体疼痛）的患者被转介到电诊断实验室；肢体麻木;或者排除神经根病。结果：308例患者符合预定的纳入标准。年龄、四肢麻木、背痛和其他症状等变量被确定为异常结果的重要预测因素。使用这些变量开发了一种诊断工具，将每个变量分为正常(0)或异常(1)，年龄是唯一的数字输入。对于神经传导研究（NCS）的预测，显著因素包括年龄[P < 0.01，优势比（OR） 0.95]、四肢麻木（P = 0.03, OR 0.42）、其他症状（P = 0.04, OR 0.29）、磁共振成像正常（P = 0.02, OR 2.99）。对于肌电图（EMG）预测，年龄（P < 0.01, OR 0.95）、背部疼痛（P = 0.02, OR 0.43）和其他症状（P = 0.03, OR 0.29）具有显著性。预测NCS和EMG异常的logistic回归模型在受试者操作曲线下的面积分别为0.807和0.777。NCS模型的敏感性为24.10%，阳性预测值为68.97%，阴性预测值为72.84%，特异性为95%；EMG模型的敏感性为32.14%，阳性预测值为75%，阴性预测值为74.67%。结论：本研究强调了基于关键临床变量使用评分系统预测NCS和EMG结果的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Clinical Neuromuscular Disease Medicine-Medicine (all)

CiteScore

1.60

自引率

0.00%

发文量

期刊介绍： Journal of Clinical Neuromuscular Disease provides original articles of interest to physicians who treat patients with neuromuscular diseases, including disorders of the motor neuron, peripheral nerves, neuromuscular junction, muscle, and autonomic nervous system. Each issue highlights the most advanced and successful approaches to diagnosis, functional assessment, surgical intervention, pharmacologic treatment, rehabilitation, and more.