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
Many patients undergoing total knee arthroplasty (TKA) have severe osteoarthritis (OA) in both knees and may consider either simultaneous or staged bilateral TKA. The implications of simultaneous versus staged bilateral TKA for return to work are not well understood. We hypothesized that employed patients who underwent simultaneous bilateral TKA would have significantly fewer days missed from work compared with the sum of days missed from each operation for patients who underwent staged bilateral TKA. The prospective arthroplasty registry at the Hospital for Special Surgery was used. Baseline characteristics and patient-reported outcome scores were evaluated. We used a linear regression model, adjusting for potential confounding variables including age, sex, preoperative BMI, and type of work (sedentary, moderate, high activity, or strenuous), to analyze time lost from work after simultaneous compared with staged bilateral TKA.Aims
Methods
Surgeons commonly resect additional distal femur during primary total knee arthroplasty (TKA) to correct a flexion contracture, which leads to femoral joint line elevation. There is a paucity of data describing the effect of joint line elevation on mid-flexion stability and knee kinematics. Thus, the goal of this study was to quantify the effect of joint line elevation on mid-flexion laxity. Six computational knee models with cadaver-specific capsular and collateral ligament properties were implanted with a posterior-stabilized (PS) TKA. A 10° flexion contracture was created in each model to simulate a capsular contracture. Distal femoral resections of + 2 mm and + 4 mm were then simulated for each knee. The knee models were then extended under a standard moment. Subsequently, varus and valgus moments of 10 Nm were applied as the knee was flexed from 0° to 90° at baseline and repeated after each of the two distal resections. Coronal laxity (the sum of varus and valgus angulation with respective maximum moments) was measured throughout flexion.Aims
Methods
The outcomes of total knee arthroplasty (TKA) depend on many factors. The impact of implant design on patient-reported outcomes is unknown. Our goal was to evaluate the patient-reported outcomes and satisfaction after primary TKA in patients with osteoarthritis undergoing primary TKA using five different brands of posterior-stabilized implant. Using our institutional registry, we identified 4135 patients who underwent TKA using one of the five most common brands of implant. These included Biomet Vanguard (Zimmer Biomet, Warsaw, Indiana) in 211 patients, DePuy/Johnson & Johnson Sigma (DePuy Synthes, Raynham, Massachusetts) in 222, Exactech Optetrak Logic (Exactech, Gainesville, Florida) in 1508, Smith & Nephew Genesis II (Smith & Nephew, London, United Kingdom) in 1415, and Zimmer NexGen (Zimmer Biomet) in 779 patients. Patients were evaluated preoperatively using the Knee Injury and Osteoarthritis Outcome Score (KOOS), Lower Extremity Activity Scale (LEAS), and 12-Item Short-Form Health Survey questionnaire (SF-12). Demographics including age, body mass index, Charlson Comorbidity Index, American Society of Anethesiologists status, sex, and smoking status were collected. Postoperatively, two-year KOOS, LEAS, SF-12, and satisfaction scores were compared between groups.Aims
Patients and Methods
