A Prediction Model for Instability in Adult Distal Radius Fractures: Integrating Post-Reduction and Follow-Up Indicators

<b>Background/Objectives</b>: Although Lafontaine criteria are widely used to predict fracture instability for distal radius fractures (DRFs), their predictive performance remains limited. This study aimed to enhance prediction accuracy by incorporating post-reduction and one-week follow-up radiographic findings. <b>Methods</b>: This retrospective study included adults with DRFs treated with closed reduction and casting. A predictive model was developed using pre- and post-reduction radiographs through stepwise multivariable logistic regression. Simplified scores were derived to classify patients into low-, moderate- and high-risk groups, guiding follow-up or early surgical intervention. An additional predictive model based on one-week radiographs was developed for the moderate-risk group with uncertain stability. Internal validation was performed using bootstrapping, and model performance was compared with Lafontaine criteria. <b>Results</b>: Of 402 patients identified, 244 met inclusion criteria; 161 developed malalignment and 98 required surgery. The mean age was 58.5 ± 16.7 years, and 75.1% were female. In baseline model, significant predictors of instability included dorsal angulation > 20°, intra-articular fracture, ulnar variance > 3 mm, volar cortex restoration, and post-reduction volar angulation ≤ 0°. Internal validation demonstrated good performance (optimism-adjusted AUC = 0.86). Risk stratification identified 39% of patients as moderate risk, who were subsequently used to develop a one-week follow-up model, with ulnar variance > 3 mm as a key predictor for instability. The overall model outperformed Lafontaine criteria (AUC = 0.75 vs. 0.68). <b>Conclusions</b>: The proposed model effectively stratifies instability risk and supports clinical decision-making by integrating critical post-reduction and one-week radiographic parameters, offering greater accuracy than existing criteria.