Development of a physical mobility prediction model to guide prosthetic rehabilitation

Background:Prosthetic rehabilitation decisions depend on estimating a patient's mobility potential. However, no validated prediction models of mobility outcomes exist for people with lower-limb amputation (LLA).Objectives:To develop and test predictions for self-reported mobility after LLA, using the Prosthetic Limb Users Survey of Mobility (PLUS-M).Study Design:This is a retrospective cohort analysis.Methods:Eight hundred thirty-one patient records (1,860 PLUS-M observations) were used to develop and test a neighbors-based prediction model, using previous patient data to predict the 6-month PLUS-M T-score trajectory for a new patient (based on matching characteristics). The prediction model was developed in a training data set (n = 552 patients) and tested in an out-of-sample data set of 279 patients with later visit dates. Prediction performance was assessed using bias, coverage, and precision. Prediction calibration was also assessed.Results:The average prediction bias for the model was 0.01 SDs, average coverage was 0.498 (ideal proportion within the 50% prediction interval = 0.5), and prediction interval was 8.4 PLUS-M T-score points (40% improvement over population-level estimates). Predictions were well calibrated, with the median predicted scores falling within the standard error of the median of observed scores, across all deciles of the data.Conclusions:This neighbors-based prediction approach allows for accurate estimates of PLUS-M T-score trajectories for people with LLA.