Early and accurate prediction of hospital length-of-stay
(LOS) in patients undergoing knee replacement is important for economic
and operational reasons. Few studies have systematically developed
a multivariable model to predict LOS. We performed a retrospective
cohort study of 1609 patients aged ≥ 50 years who underwent elective,
primary total or unicompartmental knee replacements. Pre-operative
candidate predictors included patient demographics, knee function,
self-reported measures, surgical factors and discharge plans. In
order to develop the model, multivariable regression with bootstrap
internal validation was used. The median LOS for the sample was
four days (interquartile range 4 to 5). Statistically significant
predictors of longer stay included older age, greater number of comorbidities,
less knee flexion range of movement, frequent feelings of being
down and depressed, greater walking aid support required, total
(versus unicompartmental) knee replacement, bilateral
surgery, low-volume surgeon, absence of carer at home, and expectation
to receive step-down care. For ease of use, these ten variables were
used to construct a nomogram-based
This study demonstrates a significant correlation
between the American Knee Society (AKS) Clinical Rating System and
the Oxford Knee Score (OKS) and provides a validated prediction
tool to estimate score conversion. A total of 1022 patients were prospectively clinically assessed
five years after TKR and completed AKS assessments and an OKS questionnaire.
Multivariate regression analysis demonstrated significant correlations between
OKS and the AKS knee and function scores but a stronger correlation
(r = 0.68, p <
0.001) when using the sum of the AKS knee and
function scores. Addition of body mass index and age (other statistically
significant predictors of OKS) to the algorithm did not significantly
increase the predictive value. The simple regression model was used to predict the OKS in a
group of 236 patients who were clinically assessed nine to ten years
after TKR using the AKS system. The predicted OKS was compared with
actual OKS in the second group. Intra-class correlation demonstrated
excellent reliability (r = 0.81, 95% confidence intervals 0.75 to
0.85) for the combined knee and function score when used to predict
OKS. Our findings will facilitate comparison of outcome data from
studies and registries using either the OKS or the AKS scores and
may also be of value for those undertaking meta-analyses and systematic
reviews. Cite this article:
Debate remains whether the patella should be resurfaced during total knee replacement (TKR). For non-resurfaced TKRs, we estimated what the revision rate would have been if the patella had been resurfaced, and examined the risk of re-revision following secondary patellar resurfacing. A retrospective observational study of the National Joint Registry (NJR) was performed. All primary TKRs for osteoarthritis alone performed between 1 April 2003 and 31 December 2016 were eligible (n = 842,072). Patellar resurfacing during TKR was performed in 36% (n = 305,844). The primary outcome was all-cause revision surgery. Secondary outcomes were the number of excess all-cause revisions associated with using TKRs without (versus with) patellar resurfacing, and the risk of re-revision after secondary patellar resurfacing.Aims
Methods
The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC.Aims
Methods
Unicompartmental knee arthroplasty (UKA) provides improved early functional outcomes and less postoperative morbidity and pain compared with total knee arthroplasty (TKA). Opioid prescribing has increased in the last two decades, and recently states in the USA have developed online Prescription Drug Monitoring Programs to prevent overprescribing of controlled substances. This study evaluates differences in opioid requirements between patients undergoing TKA and UKA. We retrospectively reviewed 676 consecutive TKAs and 241 UKAs. Opioid prescriptions in morphine milligram equivalents (MMEs), sedatives, benzodiazepines, and stimulants were collected from State Controlled Substance Monitoring websites six months before and nine months after the initial procedures. Bivariate and multivariate analysis were performed for patients who had a second prescription and continued use.Aims
Patients and Methods
