Can Personalized 3D Kinematic Modeling Predict Loss of Pronation and Supination in Diaphyseal Forearm Malunions? A Clinical Validation Study.

backgroundDespite the clinical relevance of forearm fractures and malunions and the impact of a functional limitation, the link between forearm malalignment and limited pronation and supination remains poorly understood and still relies on anatomical alignment expressed as angulation. Using recently developed technologies, mechanisms that limit function can be automatically detected by modeling individual forearm kinematics using three-dimensional (3D) bone models of the radius and ulna. QUESTIONS/PURPOSES: We evaluated the accuracy of a personalized 3D kinematic model to identify limitations in forearm rotation in pronation and supination and to answer the following questions: (1) How accurately does the model-predicted ROM agree with the corresponding clinical measurements? (2) How accurately does the model classify malunited forearms according to the presence of clinically relevant functional limitations, defined as a range of pronation or supination less than 50°? (3) What is the frequency at which the model detects bone impingement and central band block during pronation and supination?

methodsThis retrospective study evaluated a diagnostic model using the preoperative CT scans of 45 patients with unilateral diaphyseal forearm malunions, all of whom underwent corrective osteotomy due to a clinically relevant limitation in pronation or supination function. In all, 53% (24) of patients were male; the mean ± SD age at the time of the CT scan was 16 ± 6 years, and the mean time since the original trauma was 6 ± 5 years. Twenty patients had a clinically relevant loss of pronation, 15 patients had a loss of supination, and 10 patients had a loss of both. We generated 3D bone models with landmarks to simulate forearm rotation in 5° steps from 100° of pronation to 100° of supination. Two mechanisms that limit function after diaphyseal malunions-bone impingement and central band blockage-were identified in the simulation, resulting in a predicted ROM. For the first study question, differences between clinical and predicted function were expressed as mean absolute error, root mean square error, and mean error to illustrate typical error size, penalize outliers, and quantify the direction of error deviation, respectively. Acceptable errors were around 15°, comparable to the range seen in clinical measurements. For question two, clinical measurements and predictions were dichotomized based on a threshold of 50°. Accuracy, sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic (ROC) curve for detecting clinically relevant limitations were calculated separately for pronation and supination. Acceptable diagnostic values should be above 60%, which is normal for angulation measurements. For question three, the blocking mechanisms detected during the simulation were counted.

resultsMean absolute errors between prediction and clinical measurement for pronation, supination, and ROM were 19°, 23°, and 22°, respectively. Root mean square errors were 22° for pronation, 28° for supination, and 28° for ROM. Mean errors were 3° for pronation, 1° for supination, and 5° for ROM. Errors were substantially higher than the clinical measurement uncertainty, with some outliers. Accuracy for finding a relevant pronation or supination limitation was 91% and 82%, respectively. Diagnostic values for detecting pronation limitations were 91% for accuracy, 87% for sensitivity, 100% for specificity, 79% for negative predictive value, and 100% for positive predictive value. For supination, the values were 82% for accuracy, 84% for sensitivity, 80% for specificity, 80% for negative predictive value, and 84% for positive predictive value. Area under the curve values were 0.97 (95% confidence interval [CI] 0.93 to 1) for detecting pronation limitations and 0.93 (95% CI 0.87 to 1) for supination limitations. These values are higher than those reported by studies using angulation thresholds. Bone impingement was mainly seen during pronation, and a central band block was the most common reason for a supination limitation.

conclusionIndividualized kinematic modeling of forearm malunions reliably detects clinically relevant limitations of forearm rotation without requiring dynamic imaging. Because of simplifications on the exact location and status of the central band and the neutral position of the forearm, exact ROM prediction is not possible. CLINICAL RELEVANCE: This study represents an important step toward functional rather than anatomical evaluation of forearm anatomy and correction of malunited forearm fractures. The next step would be to use the model in preoperative planning optimization, focusing on functional outcomes rather than purely anatomical correction. Given the model's high diagnostic accuracy, personalized 3D kinematic modeling has potential as a decision tool for determining whether a forearm fracture should undergo operative treatment or whether it can be managed nonoperatively. However, challenges regarding fracture remodeling and stability in a cast, along with low-dose 3D imaging, must be addressed.