Ultrasonographic cross-sectional area in classifying ulnar neuropathy at the elbow.

Objective: Ulnar neuropathy at the elbow (UNE) is a common entrapment neuropathy for which electrodiagnostic (EDX) testing is frequently used. Although multi-tiered electrophysiologic severity classifications exist, the extent to which ultrasound imaging (USI)-derived ulnar nerve cross-sectional area (CSA) provides incremental discrimination across severity categories and informs probabilistic clinical decision-making remains unclear. Methods: This retrospective analysis of prospectively collected cross-sectional data included 299 elbows from 262 patients referred for EDX testing with clinical suspicion of UNE. Elbows were classified using two multi-tiered electrophysiologic systems (Padua and Zeidman) and consolidated binary categorizations (Negative vs UNE). USI-derived ulnar nerve CSA was evaluated using receiver operating characteristic analyses and likelihood ratios to estimate post-test probability shifts across severity categories and binary classifications, accounting for clustering of bilateral elbows. Results: CSA demonstrated limited incremental discrimination between adjacent categories within multi-tiered electrophysiologic classifications, resulting in modest post-test probability shifts. In contrast, binary categorizations showed stronger discriminative performance. CSA values <6 mm Conclusions: USI-derived ulnar nerve CSA does not reliably discriminate between adjacent electrophysiologic severity categories of UNE. A simplified binary interpretive framework using CSA thresholds may offer greater clinical utility. Significance: These findings support probabilistic, rather than severity-grading, use of USI when evaluating suspected UNE in patients referred for EDX testing.