Premium
A prognostic model for the patient‐reported outcome of surgical treatment of carpal tunnel syndrome
Author(s) -
Bowman Angela,
Rudolfer Stephan,
Weller Peter,
Bland Jeremy D. P.
Publication year - 2018
Publication title -
muscle and nerve
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.025
H-Index - 145
eISSN - 1097-4598
pISSN - 0148-639X
DOI - 10.1002/mus.26297
Subject(s) - carpal tunnel syndrome , medicine , logistic regression , carpal tunnel , multivariate analysis , receiver operating characteristic , multivariate statistics , surgical decompression , outcome (game theory) , surgery , median nerve , physical therapy , decompression , machine learning , mathematics , mathematical economics , computer science
Many prognostic factors have been studied in carpal tunnel decompression, but most studies consider only a subset of variables. Methods: Three thousand three hundred thirty‐two operations were used to develop prognostic models, and 885 operations were used for validation. Outcome recorded on a Likert scale was dichotomized into success or failure. Modeling was performed with both logistic regression and artificial neural networks using 87 candidate variables. Results: Both approaches produced predictive multivariate models for outcome with areas under a receiver operating characteristic curve of 0.7 in the validation data set. Patients with moderately severe nerve conduction abnormalities, night waking, a family history of carpal tunnel syndrome, a good response to corticosteroid injection, and women have better outcomes. Greater functional impairment, diabetes, hypertension, and surgery on the dominant hand are associated with poorer outcomes. Discussion: A multivariate model partially predicts the outcome of carpal tunnel surgery, aids decision making, and helps to manage patient expectations. Muscle Nerve 58 :784–789, 2018