Predicting patients at risk of a poor outcome would be useful in patient selection for total knee arthroplasty (TKA). Existing models to predict outcome have seen limited functional implementation. This study aims to validate a model and shared decision-making tool for both clinical utility and predictive accuracy.
A Bayesian belief network statistical model was developed using data from the Osteoarthritis Initiative. A consecutive series of consultations for osteoarthritis before and after introduction of the tool was used to evaluate the clinical impact of the tool. A data audit of postoperative outcomes of TKA patients exposed to the tool was used to evaluate the accuracy of predictions.
The tool changed consultation outcomes and identified patients at risk of limited improvement. After introduction of the tool, patients booked for surgery reported worse Knee Osteoarthritis and Injury Outcome Score pain scores (difference, 15.2; P < .001) than those not booked, with no significant difference prior. There was a 27% chance of not improving if predicted at risk, and a 1.4% chance if predicted to improve. This gives a risk ratio of 19× (P < .001) for patients not improving if predicted at risk.
For a prediction tool to be clinically useful, it needs to provide a better understanding of the likely clinical outcome of an intervention than existed without its use when the clinical decisions are made. The tool presented here has the potential to direct patients to surgical or nonsurgical pathways on a patient-specific basis, ensuring patients who will benefit most from TKA surgery are selected.