Normal Values of Central, Peripheral, and Root Motor Conduction Times in a Healthy Korean Population.

PURPOSE: Central, peripheral, and root motor conduction times (CMCTs, PMCTs, and RMCTs, respectively) are valuable diagnostic tools for spinal cord and motor nerve root lesions. We investigated the normal values and the effects of age and height on each motor conduction time. METHODS: This study included 190 healthy Korean subjects who underwent magnetic stimulation of the cortex and spinous processes at the C7 and L1 levels. Recording muscles were abductor pollicis brevis and abductor digiti minimi in the unilateral upper limb and extensor digitorum brevis and abductor hallucis in the contralateral lower limb. F-wave and compound motor nerve action potentials were also recorded. Central motor conduction time was evaluated as the difference between cortical motor evoked potential onset latency and PMCT using calculation and spinal stimulation methods. Root motor conduction time was computed as the difference between spinal stimulated and calculated CMCTs. RESULTS: The average age and height of the participants were 41.21 ± 14.39 years and 164.64 ± 8.27 cm, respectively; 39.5% (75/190) patients were men. In the linear regression analyses, upper limb CMCTs showed a significant and weak positive relationship with height. Lower limb CMCTs demonstrated a significant and weak positive relationship with age and height. Peripheral motor conduction times were significantly and positively correlated with age and height. Root motor conduction times showed no significant relationship with age and height, except for abductor pollicis brevis-RMCT, which had a weak negative correlation with height. CONCLUSIONS: This study provides normal values of CMCTs, PMCTs, and RCMTs, which have potential clinical applications. When interpreting CMCTs, age and height should be considered.

Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms.

Background: Therapeutic decisions for degenerative cervical myelopathy (DCM) are complex and should consider various factors. We aimed to develop machine learning (ML) models for classifying expert-level therapeutic decisions in patients with DCM. Methods: This retrospective cross-sectional study included patients diagnosed with DCM, and the diagnosis of DCM was confirmed clinically and radiologically. The target outcomes were defined as conservative treatment, anterior surgical approaches (ASA), and posterior surgical approaches (PSA). We performed the following classifications using ML algorithms: multiclass, one-versus-rest, and one-versus-one. Two ensemble ML algorithms were used: random forest (RF) and extreme gradient boosting (XGB). The area under the receiver operating characteristic curve (AUC-ROC) was the primary metric. We also identified the variable importance for each classification. Results: In total, 304 patients were included (109 conservative, 66 ASA, 125 PSA, and 4 combined surgeries). For multiclass classification, the AUC-ROC of RF and XGB models were 0.91 and 0.92, respectively. In addition, ML models showed AUC-ROC values of >0.9 for all types of binary classifications. Variable importance analysis revealed that the modified Japanese Orthopaedic Association score and central motor conduction time were the two most important variables for distinguishing between conservative and surgical treatments. When classifying ASA and PSA, the number of involved levels, age, and body mass index were important contributing factors. Conclusion: ML-based classification of DCM therapeutic options is valid and feasible. This study can be a basis for establishing generalizable ML-based surgical decision models for DCM. Further studies are needed with a large multicenter database.