Abstract
Introduction
Dissatisfaction rates after TKA are reported to be between 15 – 25%, with unmet outcome expectations being a key contributor. Shared decision making tools (SDMT) are designed to align a patient's and surgeon's expectations. This study demonstrates clinical validation of a patient specific shared decision making tool.
Methods
Patient reported outcome measures (PROMs) were collected in 150 patients in a pre-consultation environment of one surgeon. The data was processed into a probabilistic predictive model utilising prior data to generate a preoperative baseline and an expected outcome after TKA. The surgeon was blinded to the prediction algorithm for the first 75 patients and exposed for the following 75 patients. PROMs collected were the knee injury and osteoarthritis outcome score (KOOS) and questions on lower back pain, hip pain and falls. The patients booked and not booked before and after exposure to the prediction were collected.
The clinical validation involved 27 patients who had their outcome predicted and had their PROMs captured at 12 months after TKA. The predicted change in severity of pain and the patients actual change from pre-op to 12 month post operative KOOS pain was analysed using a Spearman's Rho correlation. Further analysis was performed by dividing the group into those predicted by the model to have improved by more than 10 percentile points and those who were predicted to improve by less than 10 percentile points.
Results
Prior to the clinical implementation of the application, the population of patients booked for TKR surgery had a preoperative KOOS pain score of 47.9 ± 17.1, while those not booked for TKR surgery had a mean KOOS pain score of 54.4 ± 21.0 points, with higher scores indicating a lower pain state. A difference of 6.5 points exists between the means. Following introduction of the application, the scores for the population of patients booked for TKR surgery were 40.0 ± 12.3, while those not booked were 55.2 ± 18.8, a significant difference of 15.2 (p<0.001).
The clinical validation showed a strong correlation between the predicted and actual pain state change (Spearman's Rho = 0.63, p<0.0001). Patients who were predicted to have a change of less than 10 points pre- operatively had a lower KOOS total score at 12 months (72.16 vs 86.97, p = 0.02).
Conclusions
We found a significant difference in the KOOS pain score of patients for whom a decision to operate was made following introduction of the application. A predictive algorithm based on PROMs may assist a surgeon to optimise their patient selection for TKR. The clinical validation showed a strong correlation between predicted and actual change in pain state before and after TKA, supporting the validity of the SDMT's prediction. Literature has shown that the change between pre TKA pain state and post TKA pain state influences patient satisfaction; those with a smaller change in reported pain being less satisfied. This concept has led to the development of a patient specific shared decision making tool that can be used by surgeons and patients in the pre TKA consultation.
