Plastic and reconstructive surgery · 2025
Identifying Diagnostic Criteria for Cubital Tunnel Syndrome.
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Cuneo Kevin R, Collins Devin W, Sullivan Constance M, Suh Nina, Wagner Eric R, Gottschalk Michael B
Аннотация
BACKGROUND: Cubital tunnel syndrome (CuTS) is the second most common compressive neuropathy of the upper extremity. The authors aimed to determine the importance of diagnostic criteria for CuTS.
METHODS: Expert opinion previously reached a consensus on the 6 most important criteria for CuTS diagnosis using Delphi. These criteria were used to create 64 unique CuTS vignettes. Two groups of 8 hand surgeons evaluated these vignettes. One group made a binary diagnosis of CuTS; the second group used a 10-cm visual analog scale (VAS) to rate the likelihood of CuTS. The results were used to develop a logistic regression model and a multiple linear regression model, respectively, to predict the probability of CuTS from the weighted criteria. Correlation between the logistic regression model probabilities and the panelist VAS scores was calculated. The probabilities produced by these models were correlated with the number of criteria in each case.
RESULTS: The correlation between the probabilities of CuTS predicted by the logistic regression with the panelists' VAS scores was 0.291. The correlation between the number of criteria present and the predicted probabilities of the logistic and the multiple regression models were 0.638 and 0.923, respectively.
CONCLUSIONS: This study found that there is no agreement on the relative importance of the 6 diagnostic criteria for CuTS among experts. However, the sum of the number of criteria present is a good predictor of the probability of CuTS. Furthermore, a threshold of 3 positive criteria can help clinicians diagnose CuTS with high confidence.
METHODS: Expert opinion previously reached a consensus on the 6 most important criteria for CuTS diagnosis using Delphi. These criteria were used to create 64 unique CuTS vignettes. Two groups of 8 hand surgeons evaluated these vignettes. One group made a binary diagnosis of CuTS; the second group used a 10-cm visual analog scale (VAS) to rate the likelihood of CuTS. The results were used to develop a logistic regression model and a multiple linear regression model, respectively, to predict the probability of CuTS from the weighted criteria. Correlation between the logistic regression model probabilities and the panelist VAS scores was calculated. The probabilities produced by these models were correlated with the number of criteria in each case.
RESULTS: The correlation between the probabilities of CuTS predicted by the logistic regression with the panelists' VAS scores was 0.291. The correlation between the number of criteria present and the predicted probabilities of the logistic and the multiple regression models were 0.638 and 0.923, respectively.
CONCLUSIONS: This study found that there is no agreement on the relative importance of the 6 diagnostic criteria for CuTS among experts. However, the sum of the number of criteria present is a good predictor of the probability of CuTS. Furthermore, a threshold of 3 positive criteria can help clinicians diagnose CuTS with high confidence.