Aug 2026· Frontiers in Surgery· Vol 13· 0 citations· 54 references
Medicine
TL;DR
This study identified six variables associated with AOFAS-defined poor postoperative functional recovery in patients with CLAI and developed an internally validated nomogram for individualized risk estimation.
Abstract
Objective This study aimed to identify predictors of postoperative recovery in chronic lateral ankle instability (CLAI) and construct a risk-estimation nomogram for individualized risk assessment. Methods Between January 2021 and December 2023, 132 patients with CLAI who underwent arthroscopic Broström repair with suture-tape augmentation were retrospectively included and classified into good-recovery [American Orthopaedic Foot and Ankle Society (AOFAS) ankle-hindfoot score ≥75, n = 73] and poor-recovery groups (AOFAS <75, n = 59). Candidate predictors were identified using univariate analysis, LASSO regression, and multivariable logistic regression. A nomogram was developed and internally validated using 1,000 bootstrap resamples, and model performance was assessed by ROC curve analysis, calibration analysis, and decision curve analysis (DCA). Results Six variables were identified as independently associated with poor postoperative recovery: sprain count (OR = 1.439, P = 0.043), osteophytes (OR = 5.951, P = 0.004), syndesmosis injury (OR = 5.272, P = 0.007), Outerbridge grade II cartilage damage (OR = 23.928, P = 0.017), thin or absent ATFL remnant <1.0 mm (OR = 5.256, P = 0.029), and CFL/ATFL angle <70° (OR = 11.598, P = 0.003). The nomogram showed acceptable discrimination, with an apparent AUC of 0.907 and an optimism-corrected AUC of 0.876 after 1,000 bootstrap resamples. Calibration demonstrated acceptable agreement between predicted and observed outcomes, and DCA suggested potential clinical utility. Conclusion This study identified six variables associated with AOFAS-defined poor postoperative functional recovery in patients with CLAI and developed an internally validated nomogram for individualized risk estimation.
The constructed nomogram model shows ideal discrimination efficiency and calibration in the training set and the test set, and there is no sign of overfitting, which has good clinical applicability and decision support value.
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