Factors influencing poor knee function 1 year after anterior cruciate ligament reconstruction and development and validation of a nomogram prediction model

Abstract

This study aimed to investigate risk factors associated with poor knee function 1 year after anterior cruciate ligament (ACL) reconstruction, develop a prediction model, and validate it. Clinical data of patients undergoing ACL reconstruction at the General Hospital of the Western Theater Command of the People's Liberation Army, PLA, from January 2022 to December 2024 were retrospectively reviewed. Univariate and multivariate logistic regression analyses identified independent factors influencing poor knee function at 1 year postoperatively. A nomogram was developed to visualize the predictive model. The model's discriminatory performance was evaluated using receiver operating characteristic curves and area under the curve, while model fit was assessed via the Hosmer-Lemeshow test. To enhance validation reliability, internal validation was conducted using Bootstrap and 10-fold cross-validation. Clinical applicability was evaluated through calibration curves and decision curves. A total of 647 patients undergoing ACL reconstruction were included, among whom 133 developed poor knee function, yielding an incidence rate of 20.56%. Univariate and multivariate logistic regression analyses identified 5 independent risk factors for poor knee function at 1 year postoperatively: meniscus injury (OR, 2.098; 95% CI, 1.508-3.016), body mass index ≥ 25 kg/m2 (OR, 1.902; 95% CI, 1.442-2.342), poor compliance (OR, 3.618; 95% CI, 2.733-4.259), insufficient quadriceps strength recovery (OR, 2.936; 95% CI, 2.345-4.502), and graft diameter < 8 mm (OR, 2.635; 95% CI, 2.198-3.430). Receiver operating characteristic curve analysis, Hosmer-Lemeshow test, and Bootstrap combined with 10-fold cross-validation confirmed the model's excellent discriminatory ability, fitting performance, stability, and high clinical value. Poor knee function 1 year after ACL reconstruction is influenced by multiple factors. The nomogram prediction model developed in this study demonstrates high accuracy, aiding clinicians in early identification of high-risk patients for timely intervention.

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