Patient-reported outcome measures (PROMs) are
increasingly being used to assess functional outcome and patient satisfaction.
They provide a framework for comparisons between surgical units,
and individual surgeons for benchmarking and financial remuneration.
Better performance may bring the reward of more customers as patients and
commissioners seek out high performers for their
This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay.Aims
Methods
To estimate the measurement properties for the Oxford Knee Score (OKS) in patients undergoing revision knee arthroplasty (responsiveness, minimal detectable change (MDC-90), minimal important change (MIC), minimal important difference (MID), internal consistency, construct validity, and interpretability). Secondary data analysis was performed for 10,727 patients undergoing revision knee arthroplasty between 2013 to 2019 using a UK national patient-reported outcome measure (PROM) dataset. Outcome data were collected before revision and at six months postoperatively, using the OKS and EuroQol five-dimension score (EQ-5D). Measurement properties were assessed according to COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) guidelines.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
The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset.Aims
Methods
We reviewed 5086 patients with a mean age of
30 years (9 to 69) undergoing primary reconstruction of the anterior cruciate
ligament (ACL) in order to determine the incidence of secondary
pathology with respect to the time between injury and reconstruction.
There was an increasing incidence of medial meniscal tears and chondral damage,
but not lateral meniscal tears, with increasing intervals before
surgery. The chances of requiring medial meniscal surgery was increased
by a factor of two if ACL reconstruction was delayed more than five
months, and increased by a factor of six if surgery was delayed
by >
12 months. The effect of delaying surgery on medial meniscal injury
was also pronounced in the patients aged <
17 years, where a
delay of five to 12 months doubled the odds of medial meniscal surgery
(odds ratio (OR) 2.0, p = 0.001) and a delay of >
12 months quadrupled
the odds (OR 4.3, p = 0.001). Increasing age was associated with
a greater odds of chondral damage (OR 4.6, p = 0.001) and medial meniscal
injury (OR 2.9, p = 0.001), but not lateral meniscal injury. The
gender split (3251 men, 1835 women) revealed that males had a greater
incidence of both lateral (34% (n = 1114) Cite this article:
