Abstract
AbstractObjectiveThis study aims to construct a clinical risk profile nomogram model for predicting early shoulder joint dysfunction (SJD) after breast cancer surgery.MethodsUsing a convenience sampling method, the clinical data of 161 breast cancer patients between February 2022 and July 2023 at Affiliated Cancer Hospital of Nanjing Medical University were selected and analyzed retrospectively. Risk factors were identified using univariate and multivariate logistic regression analyses. The R software was used to construct the risk prediction model and to plot the nomogram for early SJD post‐breast cancer surgery. The model's predictive performance was evaluated using the receiver operating characteristic (ROC) curve and the Hosmer–Lemeshow test.ResultsAmong the 161 patients, 104 (64.6%) experienced SJD. Multivariate logistic regression analysis finally included the functional exercise compliance scale for postoperative breast cancer patients (FECSPBCP), the compliance scale of physical exercise, the body mass index of patients, involved side to‐hand dominance, and the operation mode of breast and lymph nodes into the model. The area under the ROC curve (AUC) was 0.785 (95% confidence interval: 0.711–0.860), indicating a good model fit as confirmed by the Hosmer–Lemeshow test (X2 = 3.2891, p = .9149). Internal validation using the Bootstrap resampling method (n = 1000) yielded an AUC of 0.731.ConclusionsThe incidence of early SJD was high among postoperative breast cancer patients. The constructed risk prediction model can assist medical professionals in the early identification of high‐risk individuals and provide targeted interventions to prevent long‐term disabilities.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call