Effect of Femoral Head Material, Surgeon Experience, and Assembly Technique on Simulated Head-Neck Total Hip Arthroplasty Impaction Forces.

BACKGROUND: There is no standard method for assembling the femoral head onto the femoral stem during total hip arthroplasty (THA). This study aimed to measure and record dynamic 3-dimensional (3D) THA head-neck assembly loads from residents, fellows, and attending surgeons, for metal and ceramic femoral heads. METHODS: An instrumented apparatus measured dynamic 3D forces applied through the femoral stem taper in vitro for 31 surgeons (11 attendings, 14 residents, 6 fellows) using their preferred technique (ie, number of hits or mallet strikes). Outcome variables included peak axial force, peak resultant force, impulse of the resultant force, loading rate of the resultant force, and off-axis angle. They were compared between femoral head material, surgeon experience level, and the number of hits per trial. RESULTS: Average peak axial force was 6.92 ± 2.11kN for all surgeons. No significant differences were found between femoral head material. Attendings applied forces more "on-axis" as compared to both residents and fellows. Nine surgeons assembled the head with 1 hit, 3 with 2 hits, 14 with 3 hits, 2 with 4 hits, and 3 with ≥5 hits. The first hit of multihit trials was significantly lower than single-hit trials for all outcome measures except the off-axis angle. The last hit of multihit trials had a significantly lower impulse of resultant force than single-hit trials. CONCLUSION: Differences in applied 3D force-time curve dynamic characteristics were found between surgeon experience level and single and multihit trials. No significant differences were found between femoral head material.

The Impact of Travel Distance on Patient-Reported Outcomes Following Primary Total Hip Arthroplasty.

INTRODUCTION: Total hip arthroplasties (THAs) are increasingly being performed at high-volume centers, causing some patients to travel further distances to receive care. Concerns remain that increased travel distance limits follow-up, which may impact outcomes and early return to the hospital. The purpose of this study is to evaluate the impact of travel distance on 90-day patient reported outcomes (PROs) and 90-day complication rates. METHODS: Patients undergoing inpatient primary THA at a single center by one of three surgeons between 2017 and 2021 were retrospectively reviewed. Patients whose local and distant medical records were available were included. Patients who lived > 40 miles from the location or follow-up were labeled as "travelers," and those < 40 miles were "locals." Primary outcomes included PROs as measured by Veterans Rand 12 Item Health Survey (VR-12), Hip Harris Score (HHS), and Hip Dysfunction and Osteoarthritis Outcome Score for Joint Replacement (HOOS JR). Secondary outcomes included rates of 90-day medical complications, emergency department (ED) visits, unplanned readmissions, and reoperations. RESULTS: A total of 413 patients were analyzed at a mean of 897.1 days (range, 92 to 2196) including 96 travelers. Travelers averaged 96.1 miles for follow-up (range, 40.1 to 678 miles), and locals averaged 14.1 miles for follow-up (range, 0.3 to 39.8 miles). There were no significant differences in the percentage of patients achieving minimal clinically important difference (MCID) in PROs. There was no difference in the rate of 90-day medical complications, 90-day readmissions, and reoperations. Local patients were significantly more likely to have unplanned post-operative ED visits (travelers = 0%, locals = 7.4%, P = 0.003). DISCUSSION: Travelers did not demonstrate any significant differences with respect to rates of achieving MCID in PROs or 90-day complication rates. These data suggest that increased travel distance to treatment centers does not impact outcomes following primary THA.

Evaluating Modern Spacer Options and Outcomes in Revision Hip Arthroplasty.

BACKGROUND: A 2-stage revision continues to be the standard treatment for periprosthetic joint infection (PJI) in hip arthroplasty. The use of "functional" spacers may allow patients to return to daily living while optimizing their health for revision surgery. We aimed to evaluate the clinical outcomes of different spacer types regarding infection eradication, mechanical complications, and functional outcomes. METHODS: Patients who have complete Musculoskeletal Infection Society criteria for diagnosis of PJI that underwent one-stage or 2-stage revision were queried in an institutional surgical database between 2002 and 2022. Out of 286 patients, 210 met our inclusion criteria and were retrospectively reviewed for demographics, laboratory values, functional and patient-reported outcomes, and subsequent revisions. The study population had 54.3% women, a mean age of 61 years old, and a mean follow-up of 3.7 ± 3.2 years. There was no difference between age, body mass index, or Charlson Comorbidity Index scores between each cohort. Spacers were categorized as nonfunctional static, nonfunctional articulating, or functional articulating. Functional spacers were defined as those that allowed full weight bearing with no restrictions. Delphi criteria were used to define revision success, and failure was defined as a recurrent or persistent infection following definitive surgery. RESULTS: There was a significantly lower reoperation rate after a definitive implant in the functional articulating cohort (P = .003), with a trending higher infection eradication rate and a lower rate of spacer failure compared to the nonfunctional spacer cohort. At 5 years, functional articulating spacers had a 94.1% survivorship rate, nonfunctional articulating spacers had an 81.2% survival rate, and nonfunctional static spacers had a 71.4% survival rate. In the functional articulating spacer cohort, 14.6% had yet to get reimplanted, with an average follow-up time of 1.4 years. CONCLUSIONS: Within this large cohort of similar demographics, functional articulating spacers may result in better clinical outcomes and infection eradication during 2-stage revision arthroplasty for PJI.

Revision Hip Arthroplasty Performed by Fellowship-Trained Versus Non-Fellowship-Trained Surgeons: A Comparison of Perioperative Management and Complications.

BACKGROUND: Successful revision hip arthroplasty (rTHA) requires major resource allocation and a surgical team adept at managing these complex cases. The purpose of this study was to compare the results of rTHA performed by fellowship-trained and non-fellowship-trained surgeons. METHODS: A national administrative database was utilized to identify 5,880 patients who underwent aseptic rTHA and 1,622 patients who underwent head-liner exchange for infection by fellowship-trained and non-fellowship-trained surgeons from 2010 to 2020 with a 5-year follow-up. Postoperative opioid and anticoagulant prescriptions were compared among surgeons. Patients treated by fellowship-trained and non-fellowship-trained surgeons had propensity scores matched based on age, sex, comorbidity index, and diagnosis. The 5-year surgical complications were compared using descriptive statistics. Multivariable analysis was performed to determine the odds of failure following head-liner exchange when performed by a fellowship-trained versus non-fellowship-trained surgeon. RESULTS: Aseptic rTHA patients treated by fellowship-trained surgeons received fewer opioids (132 versus 165 milligram morphine equivalents per patient) and nonaspirin anticoagulants (21.4 versus 32.0%, P < .001). Fellowship-training was associated with lower dislocation rates (9.9 versus 14.2%, P = .011), fewer postoperative infections, and fewer periprosthetic fractures and re-revisions (15.2 versus 21.3%, P < .001). Head-liner exchange for infection performed by fellowship-trained surgeons was associated with lower odds of failure (31.2 versus 45.7%, odds ratio 0.76, 95% confidence interval 0.62 to 0.91, P < .001). CONCLUSIONS: rTHA performed by adult reconstruction fellowship-trained surgeons results in fewer re-revisions in aseptic cases and head-liner exchanges. Variations in resources, volumes, and perioperative protocols may account for some of the differences.

A Systematic Approach to Revision Total Knee Arthroplasty.

With the recent increase in primary total knee arthroplasties and the associated rise in failures of the index operation, there has been growing demand for orthopaedic surgeons to perform revision procedures. The orthopaedic surgeon performing revision total knee arthroplasty should be knowledgeable about the various etiologies of primary total knee arthroplasty failure, the steps for proper patient evaluation, and important factors in the preoperative planning process. A systematic methodology for obtaining surgical exposure, strategies for reconstruction, fundamentals of soft-tissue closure, and postoperative care also should be reviewed.

Modern Description of Antibiotic Spacer Options for PJI in Revision Total Knee Arthroplasty: Clinical Outcomes and Infection Eradication Rates.

BACKGROUND: As the burden of periprosthetic joint infections (PJIs) increases, there is growing interest in understanding the efficacy and morbidity reduction of 2-stage revision and various antibiotic spacer options. This study aimed to expand the description and evaluation of spacers from solely their articulation status to include their ability to support full (functional) or partial weight-bearing (nonfunctional). METHODS: Between 2002 and 2021, 391 patients who had Musculoskeletal Infection Society criteria for PJI with 1-stage or 2-stage revision were included. Demographics, functional outcomes, and subsequent revision data were collected. The study population had a mean follow-up of 2.9 years (range, 0.05-13.0) with an average age of 67 years (range, 34.7-93.4). Spacer failure was defined by surgical intervention following definitive surgery, and infection eradication was defined by the Delphi criteria. Spacers were classified as nonfunctional static, nonfunctional dynamic, functional static, or functional dynamic. Two tailed t-tests were performed. RESULTS: There were no significant differences in infection eradication or mechanical outcomes across spacer types; notably, 97.3% of functional dynamic spacers achieved infection eradication. Functional spacers had a longer time to the second stage procedure and a greater number of patients who had not been reimplanted. There was no difference in reoperation rates in nonfunctional versus functional spacers. CONCLUSION: Within this cohort, infection eradication and spacer exchange rates were noninferior among spacers. Functional spacers may allow for earlier return to daily living given the weight-bearing capability when compared to nonfunctional, without sacrificing clinical outcome.

Has Wrought Cobalt-Chromium-Molybdenum Alloy Changed for the Worse Over Time?

BACKGROUND: Total hip arthroplasty (THA) failure due to tribocorrosion of modular junctions and resulting adverse local tissue reactions to corrosion debris have seemingly increased over the past few decades. Recent studies have found that chemically-induced column damage seen on the inner head taper is enabled by banding in the alloy microstructure of wrought cobalt-chromium-molybdenum alloy femoral heads, and is associated with more material loss than other tribocorrosion processes. It is unclear if alloy banding represents a recent phenomenon. The purpose of this study was to examine THAs implanted in the 1990s, 2000s, and 2010s to determine if alloy microstructure and implant susceptibility to severe damage has increased over time. METHODS: Five hundred and forty-five modular heads were assessed for damage severity and grouped based on decade of implantation to serve as a proxy measure for manufacturing date. A subset of heads (n = 120) was then processed for metallographic analysis to visualize alloy banding. RESULTS: We found that damage score distribution was consistent over the time periods, but the incidence of column damage significantly increased between the 1990s and 2000s. Banding also increased from the 1990s to 2000s, but both column damage and banding levels appear to recover slightly in the 2010s. CONCLUSION: Banding, which provides preferential corrosion sites enabling column damage, has increased over the last 3 decades. No difference between manufacturers was seen, which may be explained by shared suppliers of bar stock material. These findings are important as banding can be avoidable, reducing the risk of severe column damage to THA modular junctions and failure due to adverse local tissue reactions.

Development and internal validation of machine learning algorithms for predicting complications after primary total hip arthroplasty.

INTRODUCTION: Complications after total hip arthroplasty (THA) may result in readmission or reoperation and impose a significant cost on the healthcare system. Understanding which patients are at-risk for complications can potentially allow for targeted interventions to decrease complication rates through pursuing preoperative health optimization. The purpose of the current was to develop and internally validate machine learning (ML) algorithms capable of performing patient-specific predictions of all-cause complications within two years of primary THA. METHODS: This was a retrospective case-control study of clinical registry data from 616 primary THA patients from one large academic and two community hospitals. The primary outcome was all-cause complications at a minimum of 2-years after primary THA. Recursive feature elimination was applied to identify preoperative variables with the greatest predictive value. Five ML algorithms were developed on the training set using tenfold cross-validation and internally validated on the independent testing set of patients. Algorithms were assessed by discrimination, calibration, Brier score, and decision curve analysis to quantify performance. RESULTS: The observed complication rate was 16.6%. The stochastic gradient boosting model achieved the best performance with an AUC = 0.88, calibration intercept = 0.1, calibration slope = 1.22, and Brier score = 0.09. The most important factors for predicting complications were age, drug allergies, prior hip surgery, smoking, and opioid use. Individual patient-level explanations were provided for the algorithm predictions and incorporated into an open access digital application: https://sorg-apps.shinyapps.io/tha_complication/ CONCLUSIONS: The stochastic boosting gradient algorithm demonstrated good discriminatory capacity for identifying patients at high-risk of experiencing a postoperative complication and proof-of-concept for creating office-based applications from ML that can perform real-time prediction. However, this clinical utility of the current algorithm is unknown and definitions of complications broad. Further investigation on larger data sets and rigorous external validation is necessary prior to the assessment of clinical utility with respect to risk-stratification of patients undergoing primary THA. LEVEL OF EVIDENCE: III, therapeutic study.

Machine learning algorithms predict within one size of the final implant ultimately used in total knee arthroplasty with good-to-excellent accuracy.

PURPOSE: To develop a novel machine learning algorithm capable of predicting TKA implant sizes using a large, multicenter database. METHODS: A consecutive series of primary TKA patients from two independent large academic and three community medical centers between 2012 and 2020 was identified. The primary outcomes were final tibial and femoral implant sizes obtained from an automated inventory system. Five machine learning algorithms were trained using six routinely collected preoperative features (age, sex, height, weight, and body mass index). Algorithms were validated on an independent set of patients and evaluated through accuracy, mean absolute error (MAE), and root mean-squared error (RMSE). RESULTS: A total of 11,777 patients were included. The support vector machine (SVM) algorithm had the best performance for femoral component size(MAE = 0.73, RMSE = 1.06) with accuracies of 42.2%, 88.3%, and 97.6% for predicting exact size, ± one size, and ± two sizes, respectively. The elastic-net penalized linear regression (ENPLR) algorithm had the best performance for tibial component size (MAE 0.70, RMSE = 1.03) with accuracies of 43.8%, 90.0%, and 97.7% for predicting exact size, ± one size, and ± two sizes, respectively. CONCLUSION: Machine learning algorithms demonstrated good-to-excellent accuracy for predicting within one size of the final tibial and femoral components used for TKA. Patient height and sex were the most important factors for predicting femoral and tibial component size, respectively. External validation of these algorithms is imperative prior to use in clinical settings. LEVEL OF EVIDENCE: Case-control, III.

A Retrospective Review of Reimbursement in Revision Total Hip Arthroplasty: A Disparity Between Case Complexity and RVU Compensation.

BACKGROUND: Revision total hip arthroplasties (THA) are time-consuming, expensive, and technically challenging. Today's Current Procedural Terminology (CPT) codes and relative value units (RVU) may in fact disincentivize surgeons to perform revision THAs. Our study reviewed labor and time investments for each component-specific revision THA and analyzed the gap between procedural value billed and final reimbursement. METHODS: A retrospective review of 165 primary and revision THAs were validated using operative notes and billing records. We stratified revision THAs by standard CPT coding (with modifiers) as single acetabular component, single femoral component, femoral head plus polyethylene liner (head/liner) exchange, all-components, and spacer placement for infection. Operative time, RVUs, total charges, deductions, and final reimbursement data was collected. Mann-Whitney U tests studied final reimbursement per minute vs per RVU in revision and primary THAs. RESULTS: Our cohort consisted of 27 primary THAs, 26 acetabular component revisions, 32 head/liner exchanges, 26 femoral component revisions, 27 all-component revisions, and 27 spacer placements. Compared to primary THAs, every revision subgroup except for head/liner exchanges were found to reimburse less per minute and all revision subgroups reimbursed less per RVU (P < .05). CONCLUSION: Physicians face less reimbursement per minute and per RVU for revision THAs. With cuts in reimbursement set forth by Centers for Medicare and Medicaid Services (CMS) and insurers, revisions may be financially unfavorable. This ultimately will lead to an impending access to care problem in the future. Our study supports the need to re-examine the RVU allocation amongst revision THAs and evaluate changes to the Current Procedural Terminology (CPT) coding system.

A Retrospective Review of Relative Value Units in Revision Total Knee Arthroplasty: A Dichotomy Between Surgical Complexity and Reimbursement.

BACKGROUND: Revision total knee arthroplasties (TKA) are costly, time-intensive, and technically demanding procedures. There are concerns regarding the valuation of Current Procedural Terminology (CPT) codes and the assigned relative value units (RVU) as a potential disincentive to perform revision TKAs. This study evaluated the labor and time investment for each component-specific revision and assessed the disparities between procedural value billed and reimbursement. METHODS: A retrospective review of 154 primary and revision TKA cases were thoroughly vetted using operative notes and internal billing data. Revision TKAs were stratified by single femoral component, single tibial component, polyethylene liner only, all-component, and spacer placement for prosthetic infection. Operative time, RVUs billed, total charges, deductions, and reimbursements were recorded. Mann-Whitney U tests compared final reimbursement per minute and per RVU between revision and primary TKAs. RESULTS: There were 28 primary TKAs, 11 femoral component revisions, 25 tibial component revisions, 25 liner exchanges, 37 all-component revisions, and 28 spacer placements. Revisions involving the tibial component, all-components, and placement of spacers were reimbursed less dollars per minute than primary TKAs (P < .05). Controlling for RVUs, liner exchanges and all-component revisions had fewer dollars per RVU than primary TKAs (P < .05). CONCLUSION: As revision complexity increases, physicians face less reimbursement per minute and per RVU. With reductions set by CMS and private insurers, revisions may be financially unfavorable and lead to restrictions and access to care problems. Our data supports the need for reevaluating RVU allocation amongst revision procedures with potential updates to the CPT coding system.

Indication for Proximal Femoral Replacement Is Associated With Risk of Failure.

BACKGROUND: Proximal femoral replacement (PFR) is reserved as a salvage procedure after failed total hip arthroplasty (THA) or after wide margin resection of tumors involving the proximal femur. Although failure of the PFR construct remains a significant problem, indication has not previously been investigated as a risk factor for failure. METHODS: This study retrospectively evaluated patients who underwent PFR over a consecutive 15-year period for primary sarcoma or metastatic disease of the proximal femur, compared with conversion to PFR after failed THA. PFR failure was defined as recurrent prosthetic dislocations, periprosthetic fracture, aseptic loosening, or infection that ultimately resulted in revision surgery. RESULTS: Overall, 99 patients were evaluated, including 58 in the neoplasm and 41 in the failed THA cohorts. Failed THA patients were older (P < .001), with a greater proportion having comorbid hypertension (P = .008), cardiac disease (P = .014), and history of prior ipsilateral and intracapsular surgeries (P < .001). The failure rate was significantly higher in failed THA patients (39.0% vs 10.3%; P < .001) with significantly shorter implant survivorship on Kaplan-Meier analysis (P = .003). A multivariate Cox proportional hazards model showed that THA failure was the only independent predictor for PFR failure (hazard ratio: 4.26, 95% confidence interval: 1.66-10.94; P = .003). CONCLUSION: This study revealed significantly worse PFR implant survivorship in patients undergoing PFR for the indication of failed THA compared with neoplasm. Although the underlying etiology of this relationship remains to be explicitly outlined, poor bone quality and soft tissue integrity, multiple prior surgeries, and comorbid conditions are likely contributing factors.

Plasma D-dimer Does Not Anticipate the Fate of Reimplantation in Two-stage Exchange Arthroplasty for Periprosthetic Joint Infection: A Preliminary Investigation.

BACKGROUND: Inflammatory markers such as the erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) levels have always been a part of the diagnostic criteria for periprosthetic joint infection (PJI), but they perform poorly anticipating the outcome of reimplantation. D-dimer has been reported in a small series as a potential marker to measure infection control after single-stage revisions to treat PJI. Nonetheless, its use to confirm infection control and decide the proper timing of reimplantation remains uncertain. QUESTIONS/PURPOSES: (1) What is the best diagnostic threshold and accuracy values for plasma D-dimer levels compared with other inflammatory markers (ESR and CRP) or what varying combinations of these tests are associated with persistent infection after reimplantation? (2) Do D-dimer values above this threshold, ESR, CRP, and varying test combinations at the time of reimplantation indicate an increased risk of subsequent persistent infection after reimplantation? METHODS: We retrospectively studied the electronic medical records of all 53 patients who had two-stage revisions for PJI and who underwent plasma D-dimer testing before reimplantation at one of two academic institutions from November 22, 2017 to December 5, 2020. During that period, all patients undergoing two-stage revisions also had a D-dimer test drawn. The minimum follow-up duration was 1 year. We are reporting at this early interval (rather than the more typical 2-year time point) because of the poorer-than-expected performance of this diagnostic test. Of these 53 patients, 17% (9) were lost to follow-up before 1 year and could not be analyzed; the remaining 44 patients (17 hips and 27 knees) were studied here. The mean follow-up was 503 ± 135 days. Absence or persistence of infection after reimplantation were defined according to the Delphi criteria. The conditions included in these criteria were: (1) control of infection, as characterized by a healed wound without fistula, drainage, or pain; (2) no subsequent surgical intervention owing to infection after reimplantation; and (3) no occurrence of PJI-related mortality. The absence of any of the aforementioned conditions until the final follow-up examination was deemed a persistent infection after reimplantation. Baseline patient characteristics were not different between patients with persistent infection (n = 10) and those with absence of it after reimplantation (n = 34) as per the Delphi criteria. Baseline patient characteristics evaluated were age, gender, self-reported race (white/Black/other) or ethnicity (nonHispanic/Hispanic), BMI, American Society of Anesthesiologists (ASA) status, smoking status(smoker/nonsmoker), and joint type (hip/knee). The optimal D-dimer threshold to differentiate between persistence of infection or not after reimplantation was calculated using the Youden index. A receiver operating characteristic curve analysis was performed to test the accuracy of D-dimer, ESR, CRP, and their combinations to establish associations, if any, with persistent infection after reimplantation. A Kaplan-Meier survival analysis (free of infection after reimplantation) with a log-rank test was performed to investigate if D-dimer, ESR, and CRP were associated with absence of infection after reimplantation. Survival or being free of infection after reimplantation was determined as per Delphi criteria. Alpha was set at p < 0.05. RESULTS: In the receiver operating characteristic curve analysis, with an area under the curve of 0.62, D-dimer showed low accuracy and did not anticipate persistent infection after reimplantation. The optimal D-dimer threshold differentiating between persistence of infection or not after reimplantation was 3070 ng/mL. When using this threshold, D-dimer demonstrated a sensitivity of 90% (95% CI 55.5% to 99.7%) and negative predictive value of 94% (95% CI 70.7% to 99.1%), but low specificity (47% [95% CI 29.8% to 64.9%]) and positive predictive value (33% [95% CI 25.5% to 42.2%]). Although D-dimer showed the highest sensitivity, the combination of D-dimer with ESR and CRP showed the highest specificity (91% [95% CI 75.6% to 98%]) defining the persistence of infection after reimplantation. Based on plasma D-dimer levels, with the numbers available, there was no difference in survival free from infection after reimplantation (Kaplan-Meier survivorship free from infection at minimum 1 year in patients with D-dimer below 3070 ng/mL versus survivorship free from infection with D-dimer above 3070 ng/mL: 749 days [95% CI 665 to 833 days] versus 615 days [95% CI 471 to 759 days]; p = 0.052). Likewise, there were no associations between high ESR and CRP levels and persistent infection after reimplantation, but the number of events was very small, and insufficient power is a concern with this analysis. CONCLUSION: In this preliminary series, with the numbers available, D-dimer alone had poor accuracy and was not associated with survival free from infection after reimplantation in patients who underwent two-stage exchange arthroplasty. D-dimer alone might be used to establish that PJI is unlikely, and the combination of D-dimer, ESR, and CRP should be considered to confirm PJI diagnosis in the setting of reimplantation.Level of Evidence Level IV, diagnostic study.

Mid-Term Survivorship, Performance, and Predictors of Outcome in Primary Total Hip Arthroplasty With a Porous Tantalum Femoral Prosthesis.

BACKGROUND: Femoral stem subsidence is a known cause of early implant failure, increasing the risk for aseptic loosening and periprosthetic fracture. Overall survivorship and subsidence in a novel porous tantalum-coated femoral prosthesis have not been well-studied. METHODS: Consecutive patients undergoing primary total hip arthroplasty with a porous tantalum-coated femoral prosthesis between January 2008 and January 2015 with minimum 5-year follow-up were included. Clinical and radiographic data were obtained from hospital and office records. Multivariate logistic regression analyses were used to determine predictors of subsidence and clinical outcomes. Kaplan-Meier survivorship curves were performed to illustrate primary failure endpoints of (1) all-cause revision and (2) femoral prosthesis revision. RESULTS: A total of 398 patients with a mean (±standard deviation) age of 61.0 ± 11.5 years, body mass index (BMI) 32.8 ± 8.0 kg/m2, and follow-up of 6.9 (range 5.0-11.2 years) were included. Survivorship at 5 years was 94.9% for all-cause revision and 98.0% for femoral component revision. Average subsidence was 1.8 ± 1.3 mm (range 0-15.5), with 8.1% patients experiencing subsidence >5 mm. Statistically significant mean improvements were observed at latest follow-up in the Hip Disability and Osteoarthritis Outcome Score Joint Replacement (40.6 ± 11.5 vs 85.2 ± 10.1, P < .001), Harris Hip Score (38.0 ± 12.0 vs 79.5 ± 12.8, P < .001), and hip flexion (92.8° ± 15.3° vs 103.3° ± 10.3°, P < .001). Multivariate logistic regression analyses revealed that greater BMI (odds ratio [OR] 1.17, P < .001), non-white/Caucasian race (OR 2.0, P = .036), and female gender (OR 2.4; P = .005) conferred a higher likelihood of subsidence >3 mm. BMI was a statistically significant and independent predictor of subsidence >5 mm (OR 1.25, P < .001) and subsidence >7 mm (OR 1.25, P < .001). CONCLUSION: The trabecular metal taper femoral prosthesis conferred excellent clinical outcome improvement and survivorship. Increasing BMI was independently associated with an increased risk of subsidence in these patients and caution is recommended in utilizing this implant in obese, morbidly obese, and super morbidly obese populations.

The Impact of Race and Socioeconomic Status on Total Joint Arthroplasty Care.

BACKGROUND: Racial minorities and patients from lower socioeconomic backgrounds are less likely to undergo total joint arthroplasty (TJA) for degenerative joint disease (DJD). However, when these patients do present for care, little is known about the overall severity of DJD and surgical wait times. METHODS: A retrospective cohort of 407 patients (131 black and 276 white) who presented to an arthroplasty clinic and went on to receive TJA was established. Severity of osteoarthritis was assessed radiographically via Kellgren-Lawrence (KL) grade. Preoperative Knee Society Score (KSS) and Harris Hip Score (HHS) were used to measure joint pain and function. Multivariate regression modeling and analysis of covariance were used to examine racial and socioeconomic differences in KL grade, KSS, HHS, and time to surgery. RESULTS: Black patients presented with significantly greater KL scores than white patients (P = .046, odds ratio = 1.65, 95% confidence interval [1.01, 2.70]). In contrast, there were no statistically significant racial differences in the mean preoperative KSS (P = .61) or HHS (P = .69). Black patients were also found to wait, on average, 35% longer for TJA (P = .03, hazard ratio = 1.35, 95% confidence interval [1.04, 1.75]). Low income was associated with higher KL grade (P = .002), lower KSS (P = .07), and lower HHS (P = .001). CONCLUSION: Despite presenting with more advanced osteoarthritis, black patients reported similar levels of joint dysfunction and had longer surgical wait times when compared with white patients. Lower socioeconomic status was similarly associated with more severe DJD.

Validation and performance of a machine-learning derived prediction guide for total knee arthroplasty component sizing.

INTRODUCTION: Anticipation of patient-specific component sizes prior to total knee arthroplasty (TKA) is essential to avoid excessive cost associated with additional surgical trays and morbidity associated with imperfect sizing. Current methods of size prediction, including templating, are inconsistent and time-consuming. Machine learning (ML) algorithms may allow for accurate TKA component size prediction with the ability to make predictions in real-time. METHODS: Consecutive patients receiving primary TKA between 2012 and 2020 from two large tertiary academic and six community hospitals were identified. The primary outcomes were the final femoral and tibial component sizes extracted from automated inventory systems. Five ML algorithms were trained with routinely corrected demographic variables (age, height, weight, body mass index, and sex) using 80% of the study population and internally validated on an independent set of the remaining 20% of patients. Algorithm performance was evaluated through accuracy, mean absolute error (MAE), and root mean-squared error (RMSE). RESULTS: A total of 17,283 patients that received one of 9 TKA implants from independent manufacturers were included. The SGB model accuracy for predicting ± 4-mm of the true femoral anteroposterior diameter was 83.6% and for ± 1 size of the true femoral component size was 95.0%. The SGB model accuracy for predicting ± 4-mm of the true tibial medial/lateral diameter was 83.0% and for ± 1 size of the true tibial component size was 97.8%. Patient sex was the most influential feature in terms of informing the SGB model predictions for both femoral and tibial component sizing. A TKA implant sizing application was subsequently created. CONCLUSION: Novel machine learning algorithms demonstrated good to excellent performance for predicting TKA component size. Patient sex appears to contribute an important role in predicting TKA size. A web-based real-time prediction application was created capable of integrating patient specific data to predict TKA size, which will require external validation prior to clinical use.

The Modern Total Knee Arthroplasty: What to Make of All of These Options?

Total knee arthroplasty (TKA) continues to grow in number each year with over three million procedures anticipated to be performed by 2030. The success and prevalence of the procedure has led to expansion in the types of implants available for surgeons to choose from. Shifts in biomaterials, bearing surfaces, and porous surfaces have occurred recently. It is difficult to find a source to make heads or tails of the available options and what they mean for patient outcomes and satisfaction. This instructional course lecture is focused on helping surgeons decide what to make of all the options available for the modern TKA.

Development of Machine Learning Algorithms to Predict Patient Dissatisfaction After Primary Total Knee Arthroplasty.

BACKGROUND: Postoperative dissatisfaction after primary total knee arthroplasty (TKA) that requires additional care or readmission may impose a significant financial burden to healthcare systems. The purpose of the current study is to develop machine learning algorithms to predict dissatisfaction after TKA. METHODS: A retrospective review of consecutive TKA patients between 2014 and 2016 from 1 large academic and 2 community hospitals was performed. Preoperative variables considered for prediction included demographics, medical history, flexion contracture, knee flexion, and outcome scores (patient-reported health state, Knee Society Score [KSS], and KSS-Function [KSS-F]). Recursive feature elimination was used to select features that optimized algorithm performance. Five supervised machine learning algorithms were developed by training with 10-fold cross-validation 3 times. These algorithms were subsequently applied to an independent testing set of patients and assessed by discrimination, calibration, Brier score, and decision curve analysis. RESULTS: Of 430 patients, a total of 40 (9.0%) were dissatisfied with their outcome after primary TKA at a minimum of 2 years postoperatively. The random forest algorithm achieved the best performance in the independent testing set not used for algorithm development (c-statistic: 0.77, calibration intercept: 0.087, calibration slope: 0.74, Brier score: 0.082). The most important factors for predicting dissatisfaction were age, number of medical comorbidities, presence of one or more drug allergies, preoperative patient-reported health state score, and preoperative KSS. CONCLUSION: The current study developed machine learning algorithms based on partially modifiable risk factors for predicting dissatisfaction after TKA. This model demonstrates good discriminative capacity for identifying those at greatest risk for dissatisfaction and may allow for preoperative health optimization.

Wagner Cone Midterm Survivorship and Outcomes.

BACKGROUND: Total hip arthroplasty (THA) in patients with abnormal proximal femoral anatomy requires an individualized treatment approach to prevent complications. Metaphyseal engaging stems in this population risk fracture, size/offset mismatch, and aseptic loosening. The Wagner conical femoral implant is a short diaphyseal engaging femoral stem that could improve treatment success in this difficult patient population. METHODS: We identified 302 consecutive patients undergoing THA using the Wagner cone femoral prosthesis between January 2010 and January 2017. Clinical, radiographic, and patient-reported outcomes were obtained through chart review and radiographic measurements of postoperative X-rays. We used multivariate analysis to determine predictors of poor outcomes. Kaplan-Meier curves were created to demonstrate implant survivorship with reoperation and revision as endpoints. The average follow-up was 3.2 years, with a minimum of 2 years. RESULTS: The implant retention survival rate during the 3.2-year study period was 98.7%. The overall reoperation rate was 4.2%, with infection followed by fracture being the most common reasons for reoperation. No patients were revised for aseptic loosening, and no patients were revised for subsidence. The average subsidence was 1.1 mm. The Harris Hip Score improved from 48.6 ± 7.3 (range, 28-64) preoperatively to 86.1 ± 8.5 (range, 66-100) at latest follow-up. The patient-reported satisfaction rate was 98.3%. CONCLUSION: The Wagner cone femoral prosthesis demonstrated excellent clinical, radiographic, and patient-reported functional outcomes at midterm follow-up. We recommend use of the Wagner cone in THA patients with challenging proximal femoral anatomy, small femoral diameter, or poor metaphyseal bone quality.

Development of Machine Learning Algorithms to Predict Clinically Meaningful Improvement for the Patient-Reported Health State After Total Hip Arthroplasty.

BACKGROUND: Failure to achieve clinically significant outcome (CSO) improvement after total hip arthroplasty (THA) imposes a potential cost-to-risk imbalance in the context of bundle payment models. Patient perception of their health state is one component of such risk. The purpose of the current study is to develop machine learning algorithms to predict CSO for the patient-reported health state (PRHS) and build a clinical decision-making tool based on risk factors. METHODS: A retrospective review of primary THA patients between 2014 and 2017 was performed. Variables considered for prediction included demographics, medical history, preoperative PRHS, and modified Harris Hip Score. The minimal clinically important difference (MCID) for the PRHS was calculated using a distribution-based method. Five supervised machine learning algorithms were developed and assessed by discrimination, calibration, Brier score, and decision curve analysis. RESULTS: Of 616 patients, a total of 407 (69.2%) achieved the MCID for the PRHS. The random forest algorithm achieved the best performance in the independent testing set not used for algorithm development (c-statistic 0.97, calibration intercept -0.05, calibration slope 1.45, Brier score 0.054). The most important factors for achieving the MCID were preoperative PRHS, preoperative opioid use, age, and body mass index. Individual patient-level explanations were provided for the algorithm predictions and the algorithms were incorporated into an open access digital application available here: https://sorg-apps.shinyapps.io/THA_PRHS_mcid/. CONCLUSION: The current study created a clinical decision-making tool based on partially modifiable risk factors for predicting CSO after THA. The tool demonstrates excellent discriminative capacity for identifying those at greatest risk for failing to achieve CSO in their current health state and may allow for preoperative health optimization.