Clinical examination of the shoulder joint has gained attention as clinicians aim to use an evidence-based examination of the biceps tendon, with the desire for a proper diagnosis while minimizing costly imaging procedures. The purpose of this study is to create a decision tree analysis that enables the development of a clinical algorithm for diagnosing long head of biceps (LHB) pathology.
A literature review of Level I and II diagnostic studies was conducted to extract characteristics of clinical tests for LHB pathology through a systematic review of PubMed, Medline, Ovid, and Cochrane Review databases. Tests were combined in series and parallel to determine sensitivities and specificities, and positive and negative likelihood ratios were determined for each combination using a subjective pretest probability. The “gold standard” for diagnosis in all included studies was arthroscopy or arthrotomy.
The optimal testing modality was use of the uppercut test combined with the tenderness to palpation of the biceps tendon test. This combination achieved a sensitivity of 88.4% when performed in parallel and a specificity of 93.8% when performed in series. These tests used in combination optimize post-test probability accuracy greater than any single individual test.
Performing the uppercut test and biceps groove tenderness to palpation test together has the highest sensitivity and specificity of known physical examinations maneuvers to aid in the diagnosis of LHB pathology compared with diagnostic arthroscopy (practical, evidence-based, comprehensive examination). A decision tree analysis aides in the practical, evidence-based, comprehensive examination diagnostic accuracy post-testing based on the ordinal scale pretest probability.