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Although there are many studies on the alignment advantages when using the robotic arm–assisted (RAA) system for total knee arthroplasty (TKA), there have been questions regarding patient-reported outcomes. Therefore, the purpose of this study was to use this index to compare: (1) total, (2) physical function, and (3) pain scores for manual versus RAA patients. We compared 53 consecutive RAA to 53 consecutive manual TKAs. No differences in preoperative scores were found between the cohorts. Patients were administered a modified Western Ontario and McMaster Universities Osteoarthritis Index satisfaction survey preoperatively and at 1-year postoperatively. The results were broken down to: (1) total, (2) physical function, and (3) pain scores. Univariate analysis with independent samples t-tests was used to compare 1-year postoperative scores. Multivariate models with stepwise backward linear regression were utilized to evaluate the associations between scores and surgical technique, age, sex, as well as body mass index (BMI). Statistical analyses were performed with a p < 0.05 to determine significance. The RAA cohort had significantly improved mean total (6 ± 6 vs. 9 ± 8 points, p = 0.03) and physical function scores (4 ± 4 vs. 6 ± 5 points, p = 0.02) when compared with the manual cohort. The mean pain score for the RAA cohort (2 ± 3 points [range, 0–14 points]) was also lower than that for the manual cohort (3 ± 4 points [range, 0–11 points]) (p = 0.06). On backward linear regression analyses, RAA was found to be significantly associated with more improved total (β coefficient [β] −0.208, standard error [SE] 1.401, p < 0.05), function (β = 0.216, SE = 0.829, p < 0.05), and pain scores (β −0.181, SE = 0.623, p = 0.063). The RAA technique was found to have the strongest association with improved scores when compared with age, gender, and BMI. This study suggests that RAA patients may have short-term improvements at minimum 1-year postoperatively. However, longer term follow-up with greater sample sizes is needed to further validate these results.

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Background: We compared the following 30-day outcomes for total knee arthroplasty (TKA) and total hip arthroplasty in spinal anesthesia (SA) versus general anesthesia (GA) (1) mortality, (2) major and minor complication rates, and (3) discharge disposition. Methods: From 2011 to 2016, the American College of Surgeons National Surgical Quality Improvement Program database contained 45,871 SA total hip arthroplasties and 65,092 receiving GA. There were 80,077 SA TKAs and 103,003 GA TKAs. Adjusted multivariate logistic regression evaluated associations between anesthesia type and 30-day outcomes. Results: Anesthesia modality was not associated with 30-day mortality (P > 0.05). The GA cohorts were at a greater risk for any complication, major complications, and minor complications (P < 0.05). Patients who received GA were at an increased risk for nonhome discharge. Conclusion: Patients who undergo total joint arthroplasty with SA experience fewer 30-day complications and are less likely to have a nonhome discharge than those with GA.

Aims 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. Methods 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. Results Within the imputed datasets, the LOS (RMSE 1.161) and PROMs models (RMSE 15.775, 11.056, 21.680 for KOOS pain, function, and QOL, respectively) demonstrated good accuracy. For all models, the accuracy of predicting outcomes in a new set of patients were consistent with the cross-validation accuracy overall. Upon validation with a new patient dataset, the LOS and readmission models demonstrated high accuracy (71.5% and 65.0%, respectively). Similarly, the one-year PROMs improvement models demonstrated high accuracy in predicting ten-point improvements in KOOS pain (72.1%), function (72.9%), and QOL (70.8%) scores. Conclusion The data-driven models developed in this study offer scalable predictive tools that can accurately estimate the likelihood of improved pain, function, and quality of life one year after knee arthroplasty as well as LOS and 90 day readmission. Cite this article: Bone Joint J 2020;102-B(9):1183–1193.

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