Dataset Information

Etiology, Diagnosis, and Treatment of Dynamic Nerve Compression Syndromes of the Elbow Among High-Level Pitchers: A Review of 7 Cases.

ABSTRACT:

Background

Dynamic compressive neuropathies around the elbow are a rare entity described by a relatively small body of literature, mostly consisting of single-case reports. No standardized diagnostic protocols have been described to date. To the authors' knowledge, this study represents the largest case series of dynamic compressive neuropathies in the upper extremity.

Purpose

To identify various etiologies of dynamic compressive neuropathies around the elbow, devise a systematic diagnostic protocol, and review treatment options.

Study design

Case series; Level of evidence, 4.

Methods

A retrospective review was conducted of patients who presented to a single practice between 2013 and 2017 and were diagnosed with a dynamic compressive neuropathy around the elbow.

Results

A total of 7 patients were identified, with a mean follow-up of 2 years. All patients were high-level pitchers. One patient was a minor league pitcher; 4 patients were National Collegiate Athletic Association athletes; and 2 patients were high school athletes. All patients underwent a systematic diagnostic workup. The diagnosis was established with dynamic nerve conduction testing. Three etiologies for dynamic nerve compression around the elbow were identified: 1 case of lateral antebrachial cutaneous nerve compression by the biceps tendon, 3 cases of ulnar nerve compression by an anconeus epitrochlearis muscle, and 3 cases of posterior interosseous nerve compression at the arcade of Frohse with hypertrophic extensor carpi radialis brevis and extensor digitorum communis muscles. Two patients were treated conservatively, while 5 patients required surgery. All patients were able to return to pitching.

Conclusion

Dynamic compressive neuropathies around the elbow are rare entities that present unique diagnostic challenges to the treating clinician. In this cohort, all patients were young throwing athletes. Physical examination of the patient frequently lacks typical findings of chronic nerve entrapment syndromes. Dynamic nerve conduction studies establish the diagnosis, and treatment often requires surgical decompression to achieve complete resolution of symptoms.

SUBMITTER: von Bergen TN

PROVIDER: S-EPMC6247494 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundProximal median nerve entrapment (PMNE, or pronator syndrome) at the elbow has traditionally been considered an elusive and rare diagnosis, as it is seldom detectable using electrophysiological techniques. In this paper, the clinical manifestations, physical diagnosis, surgical technique, and results of surgical treatment of PMNE are presented, with accompanying instructional video.Patients/methodsDuring 2011, 44 patients with PMNE were surgically released and followed prospectively, 22 women/22 men, mean age 48.8 (range 25-66). The patients were equally distributed between right/left hands (23/21) and the dominant hand was treated in 56 % of cases. The diagnosis was based on: (1) weakness in median innervated muscles distal to the lacertus fibrosus; (2) pain upon pressure over the median nerve at the level of the lacertus fibrosus; and (3) positive scratch collapse test. A minimally invasive surgical treatment using only local anesthesia with lidocaine-epinephrine and no tourniquet was used, and direct perioperative return of strength in median innervated muscles was seen in all subjects.ResultsThe average preoperative quick DASH was 35.4 (range 6.8-77.2); work DASH, 44.3 (6.25-100); and activity DASH, 61.6 (12.5-100). There were 71.1 % patients who completed the 6-month follow-up, and the average postoperative quick DASH was 12.7 (range 0-43.1), which is a statistically significant reduction (p?<?0.0001; Student's paired t test). Similarly, the work and activity DASH was significantly reduced (p?<?0.001) to 12.5 (0-75) and 6.25 (0-50), respectively.ConclusionsPMNE at the level of the lacertus fibrosus should be called lacertus tunnel syndrome to distinguish it from other levels of median nerve entrapment. It is a clinical diagnosis based on three distinct clinical findings: weakness, pain over point of compression, and positive scratch collapse test. Surgical release in local anesthesia allows for a safe, ambulatory, and cost-efficient procedure with low morbidity.

Project description:Aim Accurate severity grading of lumbar spine disease by magnetic resonance images (MRIs) plays an important role in selecting appropriate treatment for the disease. However, interpreting these complex MRIs is a repetitive and time-consuming workload for clinicians, especially radiologists. Here, we aim to develop a multi-task classification model based on artificial intelligence for automated grading of lumbar disc herniation (LDH), lumbar central canal stenosis (LCCS) and lumbar nerve roots compression (LNRC) at lumbar axial MRIs. Methods Total 15254 lumbar axial T2W MRIs as the internal dataset obtained from the Fifth Affiliated Hospital of Sun Yat-sen University from January 2015 to May 2019 and 1273 axial T2W MRIs as the external test dataset obtained from the Third Affiliated Hospital of Southern Medical University from June 2016 to December 2017 were analyzed in this retrospective study. Two clinicians annotated and graded all MRIs using the three international classification systems. In agreement, these results served as the reference standard; In disagreement, outcomes were adjudicated by an expert surgeon to establish the reference standard. The internal dataset was randomly split into an internal training set (70%), validation set (15%) and test set (15%). The multi-task classification model based on ResNet-50 consists of a backbone network for feature extraction and three fully-connected (FC) networks for classification and performs the classification tasks of LDH, LCCS, and LNRC at lumbar MRIs. Precision, accuracy, sensitivity, specificity, F1 scores, confusion matrices, receiver-operating characteristics and interrater agreement (Gwet k) were utilized to assess the model’s performance on the internal test dataset and external test datasets. Results A total of 1115 patients, including 1015 patients from the internal dataset and 100 patients from the external test dataset [mean age, 49 years ± 15 (standard deviation); 543 women], were evaluated in this study. The overall accuracies of grading for LDH, LCCS and LNRC were 84.17% (74.16%), 86.99% (79.65%) and 81.21% (74.16%) respectively on the internal (external) test dataset. Internal and external testing of three spinal diseases showed substantial to the almost perfect agreement (k, 0.67 - 0.85) for the multi-task classification model. Conclusion The multi-task classification model has achieved promising performance in the automated grading of LDH, LCCS and LNRC at lumbar axial T2W MRIs.