Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis

Taku Suzuki, Takuji Iwamoto, Kanae Shizu, Katsuji Suzuki, Harumoto Yamada, Kazuki Sato

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Background This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Methods Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Results Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Conclusions Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis.

Original languageEnglish
Pages (from-to)453-456
Number of pages4
JournalJournal of Orthopaedic Science
Issue number3
Publication statusPublished - 01-05-2017

All Science Journal Classification (ASJC) codes

  • Surgery
  • Orthopedics and Sports Medicine


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