Nerve Conduction Studies in Surgical Cubital Tunnel Syndrome Patients

Cubital Tunnel Syndrome: Does a Consensus Exist for Diagnosis?

PURPOSE

Cubital tunnel syndrome (CuTS) is the second most common compressive neuropathy of the upper extremity. We aimed to determine a consensus among experts using the Delphi method for clinical criteria that could be validated further for the diagnosis of CuTS.

METHODS

The Delphi method was used for establishing a consensus among a group of expert panelists, comprising 12 hand and upper-extremity surgeons, who ranked the diagnostic clinical importance of 55 items related to CuTS on a scale from 1 (least important) to 10 (most important). The average and SDs of each item were calculated, and Cronbach α was used to assess homogeneity among the panelist-ranked items.

RESULTS

All panelists answered the 55-item questionnaire. A Cronbach α value of 0.963 was obtained on the first iteration. The top criteria that were considered most clinically relevant to the diagnosis of CuTS among the group were determined based on the most highly ranked and correlated items among the expert panelist group. The criteria based on which there was agreement were as follows: (1) paresthesias in ulnar nerve distribution, (2) symptoms precipitated by increased elbow flexion/positive elbow flexion tests, (3) positive Tinel sign at the medial elbow, (4) atrophy/weakness/ late findings (eg, claw hand of the ring/small finger and Wartenberg or Froment sign) of ulnar nerve-innervated muscles of the hand, (5) loss of two-point discrimination in ulnar nerve distribution, and (6) similar symptoms on the involved side after successful treatment on the contralateral side.

CONCLUSIONS

Our study demonstrated a consensus among an expert panelist group of hand and upper-extremity surgeons on potential diagnostic criteria for CuTS. This consensus on diagnostic criteria may help clinicians readily diagnose CuTS in a standardized form; however, further weighting and validation are necessary prior to the development of a formal diagnostic scale.

CLINICAL RELEVANCE

This study is the first step in producing a consensus on how to diagnose CuTS.

Modern Treatment of Cubital Tunnel Syndrome: Evidence and Controversy

Cubital tunnel syndrome is the second most common peripheral mononeuropathy in the upper extremity. However, the diagnosis and treatment of cubital tunnel syndrome remains controversial without a standard algorithm. Although diagnosis can often be made from the patient's history and physical examination alone, electrodiagnostic studies, ultrasound, computed tomography (CT), and magnetic resonance image (MRI) can also be useful in diagnosing the disease and selecting the most appropriate treatment option. Treatment options include conservative nonoperative techniques as well as various surgical options, including in situ decompression with or without transposition, medial epicondylectomy, and nerve transfer in advanced disease. The purpose of this review is to summarize the most up-to-date literature regarding cubital tunnel syndrome and propose a treatment algorithm to provide clarity about the challenges of treating this complex patient population.

Diagnosis of Cubital Tunnel Syndrome Using Deep Learning on Ultrasonographic Images

Although electromyography is the routine diagnostic method for cubital tunnel syndrome (CuTS), imaging diagnosis by measuring cross-sectional area (CSA) with ultrasonography (US) has also been attempted in recent years. In this study, deep learning (DL), an artificial intelligence (AI) method, was used on US images, and its diagnostic performance for detecting CuTS was investigated. Elbow images of 30 healthy volunteers and 30 patients diagnosed with CuTS were used. Three thousand US images were prepared per each group to visualize the short axis of the ulnar nerve. Transfer learning was performed on 5000 randomly selected training images using three pre-trained models, and the remaining images were used for testing. The model was evaluated by analyzing a confusion matrix and the area under the receiver operating characteristic curve. Occlusion sensitivity and locally interpretable model-agnostic explanations were used to visualize the features deemed important by the AI. The highest score had an accuracy of 0.90, a precision of 0.86, a recall of 1.00, and an F-measure of 0.92. Visualization results show that the DL models focused on the epineurium of the ulnar nerve and the surrounding soft tissue. The proposed technique enables the accurate prediction of CuTS without the need to measure CSA.

Management of Recalcitrant Cubital Tunnel Syndrome

Cubital tunnel syndrome is a common upper extremity compressive neuropathy. Recalcitrant cubital tunnel syndrome poses diagnostic and treatment challenges. Potential etiologies of persistent or recurrent symptoms after surgical treatment include an inaccurate preoperative diagnosis, incomplete nerve decompression, iatrogenic injury, postsurgical perineural adhesions, irreversible nerve pathology, or conditions associated with secondary nerve compression. Confirmation of recalcitrant ulnar nerve pathology relies on a thorough history to consider symptoms and chronology, careful examination to quantify nerve function and to assess for focal nerve provocation, and objective testing to highlight a possible nerve lesion such as ultrasonography and electrodiagnostic testing. Conservative treatment may provide symptomatic relief; however, surgical management such as revision neuroplasty, neurolysis, nerve reconstruction, and/or anterior transposition may be indicated. Optimizing the biology of the local nerve environment is critical. No surgical treatment procedure has shown superiority over another; however, individualized treatment is emphasized to improve symptoms and maximize nerve recovery potential.