Patient perspectives and self-rated knowledge of nerve transfer surgery for restoring upper limb function in spinal cord injury

Nerve Transfer After Cervical Spinal Cord Injury: Who Has a "Time Sensitive" Injury Based on Electrodiagnostic Findings?

OBJECTIVE

To use the ulnar compound muscle action potential (CMAP) to abductor digiti minimi (ADM) to identify the proportion of individuals with cervical spinal cord injury (SCI) who have lower motor neuron (LMN) abnormalities involving the C8-T1 spinal nerve roots, within 3-6 months, and thus may influence the response to nerve transfer surgery.

DESIGN

Retrospective analysis of prospectively collected data. Data were analyzed from European Multicenter Study About SCI database.

SETTING

Multi-center, academic hospitals.

PARTICIPANTS

We included 79 subjects (age=41.4±17.7, range:16-75; 59 men; N=79), who were classified as cervical level injuries 2 weeks after injury and who had manual muscle strength examinations that would warrant consideration for nerve transfer (C5≥4, C8<3).

INTERVENTIONS

None.

MAIN OUTCOME MEASURES

The ulnar nerve CMAP amplitude to ADM was used as a proxy measure for C8-T1 spinal segment health. CMAP amplitude was stratified into very abnormal (<1.0 mV), sub-normal (1.0-5.9 mV), and normal (>6.0 mV). Analysis took place at 3 (n=148 limbs) and 6 months (n=145 limbs).

RESULTS

At 3- and 6-month post-injury, 33.1% and 28.3% of limbs had very abnormal CMAP amplitudes, respectively, while in 54.1% and 51.7%, CMAPs were sub-normal. Median change in amplitude from 3 to 6 months was 0.0 mV for very abnormal and 1.0 mV for subnormal groups. A 3-month ulnar CMAP <1 mV had a positive predictive value of 0.73 (95% CI 0.69-0.76) and 0.78 (95% CI 0.75-0.80) for C8 and T1 muscle strength of 0 vs 1 or 2.

CONCLUSION

A high proportion of individuals have ulnar CMAPs below the lower limit of normal 3- and 6-month post cervical SCI and may also have intercurrent LMN injury. Failure to identify individuals with LMN denervation could result in a lost opportunity to improve hand function through timely nerve transfer surgeries.

Artificial Intelligence Image Recognition System for Preventing Wrong-Site Upper Limb Surgery

Our image recognition system employs a deep learning model to differentiate between the left and right upper limbs in images, allowing doctors to determine the correct surgical position. From the experimental results, it was found that the precision rate and the recall rate of the intelligent image recognition system for preventing wrong-site upper limb surgery proposed in this paper could reach 98% and 93%, respectively. The results proved that our Artificial Intelligence Image Recognition System (AIIRS) could indeed assist orthopedic surgeons in preventing the occurrence of wrong-site left and right upper limb surgery. At the same time, in future, we will apply for an IRB based on our prototype experimental results and we will conduct the second phase of human trials. The results of this research paper are of great benefit and research value to upper limb orthopedic surgery.

Clinical electrodiagnostic evaluation for nerve transfer surgery in spinal cord injury: a new indication and clinical pearls

In this review, we highlight the important role of the clinical electrodiagnostic (EDX) evaluation after cervical spinal cord injury (SCI). Our discussion focuses on the need for timely, frequent, and accurate EDX evaluations in the context of nerve transfer surgery to restore critical upper limb functions, including elbow extension, hand opening, and hand closing. The EDX evaluation is crucial to define the extent of lower motor neuron lesions and determine candidacy for surgery. We also discuss the important role of the postoperative EDX evaluation in determining prognosis and supporting rehabilitation. We propose a practical framework for EDX evaluation in this clinical setting.