Wei Li1,2, Yan-Cheng Liu3, Chen-Fan Zheng4, Jun Miao3, Hui Chen1,2, Hai-Ying Quan1,2, Song-Hua Yan1,2, Kuan Zhang1. 1. School of Biomedical Engineering, Capital Medical University, Beijing, China. 2. Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China. 3. Department of Spinal Surgery, Tianjin Hospital, Tianjin, China. 4. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Abstract
OBJECTIVE: To establish a logistic regression model using surface electromyography (SEMG) parameters for diagnosing the compressed nerve root at L5 or S1 level in patients with lumbar disc herniation (LDH). METHODS: This study recruited 24 patients with L5 nerve root compression and 23 patients with S1 nerve root compression caused by LDH from May 2014 to May 2016. SEMG signals from the bilateral tibialis anterior and lateral gastrocnemius were measured. The root mean square (RMS), the RMS peak time, the mean power frequency (MPF), and the median frequency (MF) were analyzed. The accuracy, sensitivity, and specificity values were calculated separately. The areas under the curve (AUC) of the receiver-operating characteristic (ROC) curve and the kappa value were used to evaluate the accuracy of the SEMG diagnostic model. RESULTS: The accuracy of the SEMG model ranged from 85.71% to 100%, with an average of 93.57%. The sensitivity, specificity, AUC, and kappa value of the logistic regression model were 0.98 ± 0.05, 0.92 ± 0.09, 0.95 ± 0.04 (P = 0.006), and 0.87 ± 0.11, respectively (P = 0.001). The final diagnostic model was: P=1-11+ey; y = 10.76 - (5.95 × TA_RMS Ratio) - (0.38 × TA_RMS Peak Time Ratio) - (5.44 × 44 × LG_RMS Peak Time Ratio). L5 nerve root compression is diagnosed when P < 0.5 and S1 nerve root compression when P ≥ 0.5. CONCLUSIONS: The logistic regression model developed in this study showed high diagnostic accuracy in detecting the compressed nerve root (L5 and S1 ) in these patients with LDH.
RCT Entities:
OBJECTIVE: To establish a logistic regression model using surface electromyography (SEMG) parameters for diagnosing the compressed nerve root at L5 or S1 level in patients with lumbar disc herniation (LDH). METHODS: This study recruited 24 patients with L5 nerve root compression and 23 patients with S1 nerve root compression caused by LDH from May 2014 to May 2016. SEMG signals from the bilateral tibialis anterior and lateral gastrocnemius were measured. The root mean square (RMS), the RMS peak time, the mean power frequency (MPF), and the median frequency (MF) were analyzed. The accuracy, sensitivity, and specificity values were calculated separately. The areas under the curve (AUC) of the receiver-operating characteristic (ROC) curve and the kappa value were used to evaluate the accuracy of the SEMG diagnostic model. RESULTS: The accuracy of the SEMG model ranged from 85.71% to 100%, with an average of 93.57%. The sensitivity, specificity, AUC, and kappa value of the logistic regression model were 0.98 ± 0.05, 0.92 ± 0.09, 0.95 ± 0.04 (P = 0.006), and 0.87 ± 0.11, respectively (P = 0.001). The final diagnostic model was: P=1-11+ey; y = 10.76 - (5.95 × TA_RMS Ratio) - (0.38 × TA_RMS Peak Time Ratio) - (5.44 × 44 × LG_RMS Peak Time Ratio). L5 nerve root compression is diagnosed when P < 0.5 and S1 nerve root compression when P ≥ 0.5. CONCLUSIONS: The logistic regression model developed in this study showed high diagnostic accuracy in detecting the compressed nerve root (L5 and S1 ) in these patients with LDH.
Authors: Ryan S Wexler; Devon J Fox; Hannah Edmond; Johnny Lemau; Danielle ZuZero; Melissa Bollen; Diane Montenegro; Anand Parikshak; Austin R Thompson; Nels L Carlson; Hans L Carlson; Anna E Wentz; Ryan Bradley; Douglas A Hanes; Heather Zwickey; Courtney K Pickworth Journal: Contemp Clin Trials Commun Date: 2022-07-03