To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models.Aims
Methods
Limb alignment in total knee arthroplasty (TKA) influences periarticular soft-tissue tension, biomechanics through knee flexion, and implant survival. Despite this, there is no uniform consensus on the optimal alignment technique for TKA. Neutral mechanical alignment facilitates knee flexion and symmetrical component wear but forces the limb into an unnatural position that alters native knee kinematics through the arc of knee flexion. Kinematic alignment aims to restore native limb alignment, but the safe ranges with this technique remain uncertain and the effects of this alignment technique on component survivorship remain unknown. Anatomical alignment aims to restore predisease limb alignment and knee geometry, but existing studies using this technique are based on cadaveric specimens or clinical trials with limited follow-up times. Functional alignment aims to restore the native plane and obliquity of the joint by manipulating implant positioning while limiting soft tissue releases, but the results of high-quality studies with long-term outcomes are still awaited. The drawbacks of existing studies on alignment include the use of surgical techniques with limited accuracy and reproducibility of achieving the planned alignment, poor correlation of intraoperative data to long-term functional outcomes and implant survivorship, and a paucity of studies on the safe ranges of limb alignment. Further studies on alignment in TKA should use surgical adjuncts (e.g. robotic technology) to help execute the planned alignment with improved accuracy, include intraoperative assessments of knee biomechanics and periarticular soft-tissue tension, and correlate alignment to long-term functional outcomes and survivorship.
Little biomechanical information is available about kinematically aligned (KA) total knee arthroplasty (TKA). The purpose of this study was to simulate the kinematics and kinetics after KA TKA and mechanically aligned (MA) TKA with four different limb alignments. Bone models were constructed from one volunteer (normal) and three patients with three different knee deformities (slight, moderate and severe varus). A dynamic musculoskeletal modelling system was used to analyse the kinematics and the tibiofemoral contact force. The contact stress on the tibial insert, and the stress to the resection surface and medial tibial cortex were examined by using finite element analysis.Objectives
Materials and Methods
There is a large amount of evidence available
about the relative merits of unicompartmental and total knee arthroplasty
(UKA and TKA). Based on the same evidence, different people draw
different conclusions and as a result, there is great variability
in the usage of UKA. The revision rate of UKA is much higher than TKA and so some
surgeons conclude that UKA should not be performed. Other surgeons
believe that the main reason for the high revision rate is that
UKA is easy to revise and, therefore, the threshold for revision
is low. They also believe that UKA has many advantages over TKA
such as a faster recovery, lower morbidity and mortality and better
function. They therefore conclude that UKA should be undertaken
whenever appropriate. The solution to this argument is to minimise the revision rate
of UKA, thereby addressing the main disadvantage of UKA. The evidence
suggests that this will be achieved if surgeons use UKA for at least
20% of their knee arthroplasties and use implants that are appropriate
for these broad indications. Cite this article:
