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
We wanted to investigate regional variations in the organisms
reported to be causing peri-prosthetic infections and to report
on prophylaxis regimens currently in use across England. Analysis of data routinely collected by Public Health England’s
(PHE) national surgical site infection database on elective primary
hip and knee arthroplasty procedures between April 2010 and March
2013 to investigate regional variations in causative organisms.
A separate national survey of 145 hospital Trusts (groups of hospitals
under local management) in England routinely performing primary
hip and/or knee arthroplasty was carried out by standard email questionnaire.Objectives
Methods
Patient function after arthroplasty should ideally quickly improve.
It is not known which peri-operative function assessments predict
length of stay (LOS) and short-term functional recovery. The objective
of this study was to identify peri-operative functions assessments
predictive of hospital LOS and short-term function after hospital discharge
in hip or knee arthroplasty patients. In total, 108 patients were assessed peri-operatively with the
timed-up-and-go (TUG), Iowa level of assistance scale, post-operative
quality of recovery scale, readiness for hospital discharge scale,
and the Western Ontario and McMaster Osteoarthritis Index (WOMAC).
The older Americans resources and services activities of daily living
(ADL) questionnaire (OARS) was used to assess function two weeks
after discharge. Objectives
Methods
