Predicting poor wrist function recovery after internal fixation of distal radius fractures: A retrospective study based on multivariate analysis and machine learning models

Clinical Snapshot

Key Findings

• P Value: P <0.05 for variables in multivariate analysis

• Effect Size: 21.56% incidence of poor recovery (71/328 patients)

• Primary Outcome: Poor wrist function recovery (Cooney score <75) at 6 months

• Nnt Or Sensitivity: Random Forest model: AUC 0.939, accuracy 94.6%

• Confidence Interval: Not reported for individual risk factors

Clinical Application

Requires systematic collection of clinical variables and rehabilitation compliance data; prediction models need software implementation Relevant to Australian orthopaedic practice; rehabilitation compliance aligns with allied health integration in public and private systems; Cooney score widely used in Australian hand surgery units Adult patients undergoing internal fixation for distal radius fractures in orthopaedic practice

Abstract

This study aimed to identify independent risk factors associated with poor wrist function recovery 6 months after internal fixation of distal radius fractures (DRF) and develop machine learning models for early risk prediction. This retrospective study included 328 patients who underwent internal fixation for DRF at our hospital between January 2022 and June 2025. Wrist function was evaluated using the Cooney Wrist Score at 6 months postoperatively. Poor recovery was defined as a Cooney score <75. The overall incidence of poor wrist function recovery was 21.56% (71/328). Clinical variables including demographic characteristics, comorbidities, laboratory indicators, fracture type, postoperative pain scores, and rehabilitation compliance were collected. Rehabilitation compliance referred to patients' adherence to prescribed postoperative functional training and follow-up plans. Variables with P <.05 in univariate analysis were entered into multivariate logistic regression to identify independent risk factors. These variables were subsequently used to construct random forest (RF), XGBoost, and backpropagation (BP) neural network models. Model performance was evaluated using ROC curves and classification metrics. Multivariate analysis identified low rehabilitation compliance, age ≥65 years, AO type C fractures, postoperative visual analogue scale scores >3 at 1 week, and decreased serum albumin levels as independent predictors of poor wrist function recovery. Among the machine learning models, the RF model demonstrated the best performance (AUC = 0.939, accuracy = 0.946), followed by the BP neural network (AUC = 0.924). The XGBoost model showed comparatively lower predictive performance (AUC = 0.876). Poor wrist function recovery after internal fixation of DRF is influenced by multiple clinical factors. Machine learning models based on identified independent risk factors, particularly the RF model, show strong predictive ability and may assist clinicians in early risk stratification and individualized rehabilitation planning.