Abstract
Abstract
Objectives
Conventional approaches (including Tobit) do not accurately account for ceiling effects in PROMs nor give uncertainty estimates. Here, a classifier neural network was used to estimate postoperative PROMs prior to surgery and compared with conventional methods. The Oxford Knee Score (OKS) and the Oxford Hip Score (OHS) were estimated with separate models.
Methods
English NJR data from 2009 to 2018 was used, with 278.655 knee and 249.634 hip replacements. For both OKS and OHS estimations, the input variables included age, BMI, surgery date, sex, ASA, thromboprophylaxis, anaesthetic and preoperative PROMs responses. Bearing, fixation, head size and approach were also included for OHS and knee type for OKS estimation. A classifier neural network (NN) was compared with linear or Tobit regression, XGB and regression NN. The performance metrics were the root mean square error (RMSE), maximum absolute error (MAE) and area under curve (AUC). 95% confidence intervals were computed using 5-fold cross-validation.
Results
The classifier NN and regression NN had the best RMSE, both with the same scores of 8.59±0.04 for knee and 7.88±0.04 for hip. The classifier NN had the best MAE, with 6.73±0.03 for knee and 5.73±0.03 for hip. The Tobit model was second, with 6.86±0.03 for knee and 6.00±0.01 for hip. The classifier NN had the best AUC, with (68.7±0.4)% for knee and (73.9±0.3)% for hip. The regression NN was second, with (67.1±0.3)% for knee and (71.1±0.4)% for hip. The Tobit model had the best AUC among conventional approaches, with (66.8±0.3)% for knee and (71.0±0.4)% for hip.
Conclusions
The proposed model resulted in an improvement from the current state-of-the-art. Additionally, it estimates the full probability distribution of the postoperative PROMs, making it possible to know not only the estimated value but also its uncertainty.
