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
Arthrofibrosis is a relatively common complication after joint injuries and surgery, particularly in the knee. The present study used a previously described and validated rabbit model to assess the biomechanical, histopathological, and molecular effects of the mast cell stabilizer ketotifen on surgically induced knee joint contractures in female rabbits. A group of 12 skeletally mature rabbits were randomly divided into two groups. One group received subcutaneous (SQ) saline, and a second group received SQ ketotifen injections. Biomechanical data were collected at eight, ten, 16, and 24 weeks. At the time of necropsy, posterior capsule tissue was collected for histopathological and gene expression analyses (messenger RNA (mRNA) and protein).Aims
Methods
Wear debris released from bearing surfaces has been shown to
provoke negative immune responses in the recipient. Excessive wear
has been linked to early failure of prostheses. Analysis using coordinate
measuring machines (CMMs) can provide estimates of total volumetric
material loss of explanted prostheses and can help to understand
device failure. The accuracy of volumetric testing has been debated,
with some investigators stating that only protocols involving hundreds
of thousands of measurement points are sufficient. We looked to
examine this assumption and to apply the findings to the clinical
arena. We examined the effects on the calculated material loss from
a ceramic femoral head when different CMM scanning parameters were
used. Calculated wear volumes were compared with gold standard gravimetric
tests in a blinded study. 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
