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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: 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.

Robotic-assisted total knee arthroplasty (RA-TKA) was introduced to improve limb alignment, component positioning, soft-tissue balance and to minimize surgical outliers. This study investigates perioperative outcomes, complications, and early patient-reported outcome measures (PROMs) of one imageless RA-TKA system compared to conventional method TKA (CM-TKA) at 24-month follow-up. This multi-surgeon retrospective cohort analysis compared 111 imageless RA-TKA patients to 110 CM-TKA patients (n = 221). Basic demographic information, intraoperative and postoperative data, and PROMs, including the functional score of the Knee Society Score (KSS-FS), The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the Short Form 12 Mental and Physical scores (SF-12M and P), were collected and recorded preoperatively, at 3-, 12- and 24-months postoperatively. Range of motion (ROM), estimated blood loss (EBL), surgical duration, and complications were also collected. There were no baseline patient demographic differences between groups. EBL (240 vs. 190 mL, p

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.

This study aimed to develop machine learning (ML) models to predict hospital admission (overnight stay) as well as short-term complications and readmission rates following anterior cruciate ligament reconstruction (ACLR). Furthermore, we sought to compare the ML models with logistic regression models in predicting ACLR outcomes.The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent elective ACLR from 2012 to 2018. Artificial neural network ML and logistic regression models were developed to predict overnight stay, 30-day postoperative complications, and ACL-related readmission, and model performance was compared using the area under the receiver operating characteristic curve. Regression analyses were used to identify variables that were significantly associated with the predicted outcomes.A total of 21,636 elective ACLR cases met inclusion criteria. Variables associated with hospital admission included White race, obesity, hypertension, and American Society of Anesthesiologists classification 3 and greater, anesthesia other than general, prolonged operative time, and inpatient setting. The incidence of hospital admission (overnight stay) was 10.2%, 30-day complications was 1.3%, and 30-day readmission for ACLR-related causes was 0.9%. Compared with logistic regression models, artificial neural network models reported superior area under the receiver operating characteristic curve values in predicting overnight stay (0.835 vs 0.589), 30-day complications (0.742 vs 0.590), reoperation (0.842 vs 0.601), ACLR-related readmission (0.872 vs 0.606), deep-vein thrombosis (0.804 vs 0.608), and surgical-site infection (0.818 vs 0.596).The ML models developed in this study demonstrate an application of ML in which data from a national surgical patient registry was used to predict hospital admission and 30-day postoperative complications after elective ACLR. ML models developed performed well, outperforming regression models in predicting hospital admission and short-term complications following elective ACLR. ML models performed best when predicting ACLR-related readmissions and reoperations, followed by overnight stay.IV, retrospective comparative prognostic trial.

Background: Ulnar collateral ligament (UCL) reconstruction has received a unique level of attention in the press and social media. There has also been an increasing use of the internet by patients to seek medical information. Concern exists regarding the quality and comprehensibility of online information when used for patient education. Purpose: To evaluate the quality and comprehensibility of the most-viewed YouTube videos related to the diagnosis and management of UCL injuries. Based on our new evidence-based scoring rubrics, we hypothesized that the quality and comprehensibility of these videos would be poor. Study Design: Cross-sectional study. Methods: The YouTube platform was searched on September 7, 2021, with the terms “UCL injury,” “ulnar collateral ligament injury,” “UCL surgery,” “ulnar collateral ligament surgery,” and “Tommy John surgery,” and the 50 most-viewed videos from each search were compiled, yielding 250 videos. After removal of duplicates and application of exclusion criteria, the 100 most-viewed videos remained. Basic attributes, including duration of video and number of views, were recorded. Each video was then analyzed by 2 independent reviewers and evaluated for 4 key parameters (quality of diagnostic content [QAR-D], quality of treatment content [QAR-T], presence of inaccurate information, and comprehensibility) and graded on a novel scale from 1 to 4 (4 being the most appropriate for patient education). Results: The mean QAR-D was 4.83 ± 3.41 (fair quality), and the mean QAR-T was 2.76 ± 3.26 (poor quality). Physician-led educational videos had both the highest mean QAR-D (6.37) and the highest mean QAR-T (4.34). No correlation was observed between video quality and views/likes. A total of 12 videos included ≥1 inaccuracy. The mean comprehensibility score was 2.66 ± 1.12, with 39 videos falling below the acceptable comprehensibility threshold (score Conclusion: The overall quality of UCL injury–related YouTube content was low. In addition, the absence of correlation between video quality and views/likes suggests that patients are not preferentially utilizing the limited high-quality content that does exist on the YouTube platform. In addition, inaccurate videos were prevalent (12%), and almost half of all videos were deemed inappropriate for patient education in terms of comprehensibility, as defined by our comprehensibility parameter.

» The optimal management of the patella during total knee arthroplasty (TKA) remains a controversial topic with no consensus. » Patellar management techniques during TKA include patellar retention or resurfacing with or without patellaplasty, as well as circumferential denervation and partial lateral facetectomy (PLF). » Special considerations such as patient age, etiology of disease, implant design, and surgeon preference should be accounted for when determining optimal management. » We recommend that most patellae be circumferentially denervated, regardless of whether they are resurfaced, as the potential benefits outweigh the small associated risks. » Evidence demonstrates improved functional outcomes with the addition of PLF to nonresurfaced patellae. There is currently a paucity of evidence of this technique with respect to resurfaced patellae. » Patellar resurfacing adds additional costs and health-care resources that should be considered in the decision-making process.

Robotic-assisted total knee arthroplasty (RA-TKA) was introduced to improve limb alignment, component positioning, soft-tissue balance and to minimize surgical outliers. This study investigates perioperative outcomes, complications, and early patient-reported outcome measures (PROMs) of one imageless RA-TKA system compared to conventional method TKA (CM-TKA) at 24-month follow-up.This multi-surgeon retrospective cohort analysis compared 111 imageless RA-TKA patients to 110 CM-TKA patients (n = 221). Basic demographic information, intraoperative and postoperative data, and PROMs, including the functional score of the Knee Society Score (KSS-FS), The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the Short Form 12 Mental and Physical scores (SF-12M and P), were collected and recorded preoperatively, at 3-, 12- and 24-months postoperatively. Range of motion (ROM), estimated blood loss (EBL), surgical duration, and complications were also collected.There were no baseline patient demographic differences between groups. EBL (240 vs. 190 mL, p 0.001) and surgical duration (123 vs. 107 min, p 0.001) were significantly greater in RA-TKA. There were no significant differences in postoperative complications, ROM, length of stay (LOS), and PROMs between cohorts at 3-, 12-, 24-months postoperatively.Imageless RA-TKA is associated with greater EBL and surgical duration compared to CM-TKA. However, at 24-month follow-up, there were no significant differences in ROM, LOS, complications and PROMs between cohorts. Imageless robotic surgery leads to similar 24-month clinical outcomes as compared to CM-TKA.III.