A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance. MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS).Aims
Methods
The Oxford hip score (OHS) is a 12-item questionnaire designed
and developed to assess function and pain from the perspective of
patients who are undergoing total hip replacement (THR). The OHS
has been shown to be consistent, reliable, valid and sensitive to
clinical change following THR. It has been translated into different
languages, but no adequately translated, adapted and validated Danish
language version exists. The OHS was translated and cross-culturally adapted into Danish
from the original English version, using methods based on best-practice
guidelines. The translation was tested for psychometric quality
in patients drawn from a cohort from the Danish Hip Arthroplasty
Register (DHR).Objectives
Methods
