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Background: A desire to control cost and improve patient outcomes following arthroplasty led to the introduction of the Center for Medicare and Medicaid Service Comprehensive Care for Joint Replacement Program. Hemi and total hip arthroplasty for femoral neck fracture has been shown to have worse outcomes than those for osteoarthritis. However, little has been studied about the effect of comorbidities on costs associated with arthroplasty for femoral neck fracture. This study investigates how the number of comorbidities influence 90-day outcomes and cost following hemi or total hip arthroplasty for displaced femoral neck fracture in patients covered by the Comprehensive Care for Joint Replacement bundle. Methods: We reviewed all Medicare hip fracture patients undergoing hemi or total hip arthroplasty at our institution between April 2016 and November 2020. Basic demographic and perioperative information was collected. The primary outcome was hospital readmission within 90 days. The data set captured 90-day readmission to any institution, not just within our system. Secondary outcomes included 90-day reoperation and outpatient complications. Multiple logistic regression was used to examine the influence of number of comorbidities on the primary and secondary outcomes while controlling for other variables. Results: The cohort comprised 378 patients (72% female), mean age 82 (±9) years, mean body mass index 23.4 (±4.7) kg/m2. For every additional comorbidity, the odds of related readmission without reoperation increased by 1.261 (95% confidence interval [1.055-1.507], P = .011). Odds of reoperation and odds of outpatient complication did not show statistical significance with the available numbers. Conclusions: Increasing preoperative comorbidities results in a higher odd of readmission within 90 days following arthroplasty for femoral neck fracture in this Medicare population.

Reconstruction of the hip joint in the setting of metastatic lesions of the acetabulum is particularly challenging and can carry significant morbidity for patients who are already medically frail. Novel techniques to minimize morbidity and optimize function warrant exploration for these patients. Here, we present a 50-year-old woman was unable to walk secondary to metastatic breast cancer involving the acetabulum with articular disruption. A primary reconstruction technique was used that combined percutaneous stabilization of the acetabulum and cemented total hip arthroplasty using primary components. Existing reconstructive techniques for metastatic lesions of the acetabulum often require extensive open surgical approaches and revision components. Percutaneous acetabular stabilization combined with cemented total hip replacement may be a less-morbid and equally durable option.

Background: The Fragility Index (FI) and Reverse Fragility Index are powerful tools to supplement the P value in evaluation of randomized clinical trial (RCT) outcomes. These metrics are defined as the number of patients needed to change the significance level of an outcome. The purpose of this study was to calculate these metrics for published RCTs in total joint arthroplasty (TJA). Methods: We performed a systematic review of RCTs in TJA over the last decade. For each study, we calculated the FI (for statistically significant outcomes) or Reverse Fragility Index (for nonstatistically significant outcomes) for all dichotomous, categorical outcomes. We also used the Pearson correlation coefficient to evaluate publication-level variables. Results: We included 104 studies with 473 outcomes; 92 were significant, and 381 were nonstatistically significant. The median FI was 6 overall and 4 and 7 for significant and nonsignificant outcomes, respectively. There was a positive correlation between FI and sample size (R = 0.14, P = .002) and between FI and P values (R = 0.197, P = .000012). Conclusions: This study is the largest evaluation of FI in orthopedics literature to date. We found a median FI that was comparable to or higher than FIs calculated in other orthopedic subspecialties. Although the mean and median FIs were greater than the 2 recommended by the American Academy of Orthopaedic Surgeons Clinical Practice Guidelines to demonstrate strong evidence, a large percentage of studies have an FI < 2. This suggests that the TJA literature is on par or slightly better than other subspecialties, but improvements must be made. Level of Evidence: Level I; Systematic Review.

Background: Artificial intelligence (AI) and machine learning (ML) modeling in hip and knee arthroplasty (total joint arthroplasty [TJA]) is becoming more commonplace. This systematic review aims to quantify the accuracy of current AI- and ML-based application for cognitive support and decision-making in TJA. Methods: A comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Analysis of variance testing with post-hoc Tukey test was applied to compare the area under the curve (AUC) of the models. Results: After application of inclusion and exclusion criteria, 49 studies were included in this review. The application of AI/ML-based models and average AUC is as follows: cost prediction-0.77, LOS and discharges-0.78, readmissions and reoperations-0.66, preoperative patient selection/planning-0.79, adverse events and other postoperative complications-0.84, postoperative pain-0.83, postoperative patient-reported outcomes measures and functional outcome-0.81. Significant variability in model AUC across the different decision support applications was found (P < .001) with the AUC for readmission and reoperation models being significantly lower than that of the other decision support categories. Conclusions: AI/ML-based applications in TJA continue to expand and have the potential to optimize patient selection and accurately predict postoperative outcomes, complications, and associated costs. On average, the AI/ML models performed best in predicting postoperative complications, pain, and patient-reported outcomes and were less accurate in predicting hospital readmissions and reoperations.

Background: Robot-assisted surgery was developed to improve accuracy and outcomes in total knee arthroplasty (TKA). One important determinant of TKA success is a well-balanced knee throughout the range of motion. The purpose of this study is to determine if robot-assisted TKA (RA-TKA) results in improved intracompartmental ligament balance compared with conventional jig-based instrumentation (CM-TKA). Methods: This retrospective cohort study included 2 cohorts—a CM-TKA (n = 49) vs RA-TKA (n = 37) cohort. Demographic and intraoperative data, including intraoperative compartment loads, were measured after final implant implantation in extension (10°), mid-flexion (45°), and full flexion (90°), using an intraoperative compartment pressure sensor. An a priori power analysis revealed our study exhibited >80% power in detecting a 5-pound (lb) difference in compartment loads in the 2 cohorts. Results: There was no difference between medial and lateral compartment loads in extension, mid-flexion, and full flexion for the conventional (15.1 lbs, 15.9 lbs, and 13.4 lbs, respectively) vs RA-TKA (14.2 lbs, 15.1 lbs, and 10.3 lbs, respectively). The percentage of patients with high load compartment pressure in flexion (>40 lbs) by the conclusion of the surgery was significantly greater for the conventional (18%) vs the robotic TKA cohort (3%, P = .025). The percentage of patients with unbalanced knees (>20 lbs differential between medial and lateral compartments) in flexion was significantly greater in the conventional (24%) vs robotic TKA cohort (5%, P = .018). Conclusions: In this series, RA-TKA resulted in improved intraoperative compartment balancing in flexion with no observed difference in mid-flexion and extension compared with CM-TKA.