Establishing minimal clinically important differences and patient acceptable symptom state thresholds following birmingham hip resurfacing.

INTRODUCTION: Birmingham Hip Resurfacing (BHR) has emerged as a compelling and innovative alternative to total hip arthroplasty (THA), especially among young, active patients. However, the Minimal Clinically Important Difference (MCID) and the Patient Acceptable Symptom State (PASS) thresholds have not yet been determined for patients undergoing BHR. Therefore, the current study aimed to (1) determine the MCID and PASS thresholds for both the Hip disability and Osteoarthritis Outcome Score (HOOS)-Pain and HOOS physical function shortform (PS), for patients who underwent BHR; and (2) identify factors influencing the achievement of MCID and PASS for HOOS-Pain and HOOS-PS. METHODS: Prospectively collected data from patients undergoing BHR was analyzed. Patients with osteoarthritis and completed preoperative and 1-year postoperative PROMs were included. Distribution-based and anchored-based approaches were used to estimate MCID and PASS, respectively. The optimal cut-off point for PASS thresholds was calculated using the Youden index. RESULTS: MCID for HOOS-Pain and PS were calculated to be 9.2 and 9.3, respectively. The PASS threshold for HOOS-Pain and PS were ≥ 77.7 and ≥ 87.3, respectively. The current study identified several factors affecting postoperative achievement of thresholds. Baseline Mental Component Summary (MCS) scores were a predictor for achieving MCID for postoperative HOOS-Pain, achieving MCID for postoperative HOOS-PS, achieving PASS for postoperative HOOS-Pain, and achieving PASS for postoperative HOOS-PS. Furthermore, baseline HOOS-Pain was a significant predictor for achieving MCID for postoperative HOOS-PS, achieving PASS for postoperative HOOS-Pain, and achieving PASS for postoperative HOOS-PS. CONCLUSIONS: MCID and PASS thresholds were established for HOOS-Pain and PS domains following BHR with most patients achieving these clinically meaningful benchmarks. Additionally, several factors affecting achievement of MCID and PASS were identified, including modifiable risk factors that may allow clinicians to implement optimization strategies and further improve outcomes.

Recognizing the Sex Disparity in Surgeons Performing Total Knee Arthroplasty.

BACKGROUND: There is an unambiguous sex disparity in the field of orthopaedic surgery, with women making up only 7.4% of practicing orthopaedic surgeons in 2022. This study seeks to evaluate the sex distribution among orthopaedic surgeons engaged in primary total knee arthroplasty (TKA) between 2013 and 2020, as well as the procedural volume attributed to each provider. METHODS: We retrospectively queried the Medicare dataset to quantify all physicians reporting orthopaedic surgery as their specialty and performing primary TKA from 2013 to 2020. Healthcare Common Procedure Coding System codes for primary TKA procedures were used to extract associated utilization and billing provider information. Trend analyses were performed with 2-sided correlated Mann-Kendall tests to evaluate trends in the number of surgeons by sex and the women-to-men surgeon ratio. RESULTS: During the study period, 6,198 to 7,189 surgeons billed for primary TKA. Of this number, an average of 2% were women. The mean number of procedures billed for by men was 39.02/y (standard deviation: 34.54), and by women was 28.76/y (standard deviation: 20.62) (P < .001). There was no significant trend in the number of men or women surgeons who billed for primary TKA during the study period. Trend analysis of the women-to-men ratio demonstrated an increasing trend of statistical significance (P = .0187). CONCLUSIONS: There was a significant upward trend in the women-to-men ratio of surgeons who billed for primary TKA. However, there remains a colossal gender gap, as women only made up 2.4% of surgeons who billed for the procedure. The current study raises awareness of the notable discrepancy in the average number of TKAs performed by women as compared to men. The orthopaedic community should aim to determine ways to increase the number of women arthroplasty surgeons along with the opportunities that women have to perform TKAs.

Medicare's Post-Acute Care Reimbursement Models as of 2023: Past, Present, and Future.

The Centers for Medicare & Medicaid Services is continually working to mitigate unnecessary expenditures, particularly in post-acute care (PAC). Medicare reimburses for orthopaedic surgeon services in varied models, including fee-for-service, bundled payments, and merit-based incentive payment systems. The goal of these models is to improve the quality of care, reduce health-care costs, and encourage providers to adopt innovative and efficient health-care practices. This article delves into the implications of each payment model for the field of orthopaedic surgery, highlighting their unique features, incentives, and potential impact in the PAC setting. By considering the historical, current, and future Medicare reimbursement models, we hope to provide an understanding of the optimal payment model based on the specific needs of patients and providers in the PAC setting.

Patient satisfaction and patient-reported outcomes do not vary by BMI class in total hip arthroplasty.

PURPOSE: Obesity has been identified as a risk factor for postoperative complications in patients undergoing total hip arthroplasty (THA). This study aimed to investigate patient-reported outcomes, pain, and satisfaction as a function of body mass index (BMI) class in patients undergoing THA. METHODS: 1736 patients within a prospective observational study were categorized into BMI classes. Pre- and postoperative Hip disability and Osteoarthritis Outcome Score for Joint Replacement (HOOS JR), satisfaction, and pain scores were compared by BMI class using one-way ANOVA. RESULTS: Healthy weight patients reported the highest preoperative HOOS JR (56.66 ± 13.35) compared to 45.51 ± 14.45 in Class III subjects. Healthy weight and Class III patients reported the lowest (5.65 ± 2.01) and highest (7.06 ± 1.98, p < 0.0001) preoperative pain, respectively. Changes in HOOS JR scores from baseline suggest larger improvements with increasing BMI class, where Class III patients reported an increase of 33.7 ± 15.6 points at 90 days compared to 26.1 ± 17.1 in healthy weight individuals (p = 0.002). Fewer healthy weight patients achieved the minimal clinically important difference (87.4%) for HOOS JR compared to Class II (96.5%) and III (94.7%) obesity groups at 90 days postoperatively. Changes in satisfaction and pain scores were largest in the Class III patients. Overall, no functional outcomes varied by BMI class postoperatively. CONCLUSION: Patients of higher BMI class reported greater improvements following THA. While risk/benefit shared decision-making remains a personalized requirement of THA, this study highlights that utilization of BMI cutoff may not be warranted based on pain and functional improvement.

How to Raise the Bar in the Capture of Patient-Reported Outcome Measures in Total Joint Arthroplasty: Results from Active and Passive Follow-up Measures.

BACKGROUND: With the upcoming U.S. Centers for Medicare & Medicaid Services 2027 policy for mandatory reporting of patient-reported outcome measures (PROMs) for total hip or knee arthroplasty (THA or TKA), it is important to evaluate the resources required to achieve adequate PROM collection and reporting at a clinically relevant rate of follow-up. This study aimed to (1) determine follow-up rates for 1-year PROMs when the follow-up was conducted with active methods (attempted contact by staff) and passive (automated) methods, and (2) evaluate factors associated with higher odds of requiring active follow-up or being lost to follow-up following THA or TKA. METHODS: A prospective cohort of patients undergoing primary elective THA (n = 7,436) or TKA (n = 10,119) between January 2016 and December 2020 at a single institution were included. The primary outcome was the response rate achieved with active and passive follow-up methods at our institution. Patient characteristics, health-care utilization parameters, PROM values, and patient satisfaction were compared between follow-up methods. RESULTS: Passive and active measures were successful for 38% (2,859) and 40% (3,004) of the THA cohort, respectively, while 21% (1,573) were lost to follow-up. Similarly, passive and active measures were successful for 40% (4,001) and 41% (4,161) of the TKA cohort, respectively, while 20% (2,037) were lost to follow-up. Younger age, male sex, Black or another non-White race, fewer years of education, smoking, Medicare or Medicaid insurance, and specific baseline PROM phenotypes (i.e., with scores in the lower half for pain, function, and/or mental health) were associated with loss to follow-up. Older age, male sex, Black race, and a residence with a higher Area Deprivation Index were associated with requiring active follow-up. CONCLUSIONS: One of 5 patients were lost to follow-up despite active and passive measures following THA or TKA. These patients were more likely to be younger, be male, be of Black or another non-White race, have fewer years of education, be a smoker, have Medicaid insurance, and have specific baseline PROM phenotypes. Innovative strategies aimed at targeting individuals with these baseline characteristics may help raise the bar and increase follow-up while mitigating costs after total joint arthroplasty. LEVEL OF EVIDENCE: Prognostic Level II . See Instructions for Authors for a complete description of levels of evidence.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review.

¼ The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.¼ Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.¼ Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.¼ AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.¼ Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

What Are the Diagnosis-Specific Thresholds of Minimal Clinically Important Difference and Patient Acceptable Symptom State in Hip Disability and Osteoarthritis Outcome Score After Primary Total Hip Arthroplasty?

BACKGROUND: This study aimed to determine the minimal clinically important difference (MCID) and Patient Acceptable Symptom State (PASS) thresholds for Hip Disability and Osteoarthritis Outcome Score (HOOS) pain, physical short form (PS), and joint replacement (JR) 1 year after primary total hip arthroplasty stratified by preoperative diagnosis of osteoarthritis (OA) versus non-OA. METHODS: A prospective institutional cohort of 5,887 patients who underwent primary total hip arthroplasty (January 2016 to December 2018) was included. There were 4,184 patients (77.0%) who completed a one-year follow-up. Demographics, comorbidities, and baseline and one-year HOOS pain, PS, and JR scores were recorded. Patients were stratified by preoperative diagnosis: OA or non-OA. Minimal detectable change (MDC) and MCIDs were estimated using a distribution-based approach. The PASS values were estimated using an anchor-based approach, which corresponded to a response to a satisfaction question at one year post surgery. RESULTS: The MCID thresholds were slightly higher in the non-OA cohort versus OA patients. (HOOS-Pain: OA: 8.35 versus non-OA: 8.85 points; HOOS-PS: OA: 9.47 versus non-OA: 9.90 points; and HOOS-JR: OA: 7.76 versus non-OA: 8.46 points). Similarly, all MDC thresholds were consistently higher in the non-OA cohort compared to OA patients. The OA cohort exhibited similar or higher PASS thresholds compared to the non-OA cohort for HOOS-Pain (OA: ≥80.6 versus non-OA: ≥77.5 points), HOOS-PS (OA: ≥83.6 versus non-OA: ≥83.6 points), and HOOS-JR (OA: ≥76.8 versus non-OA: ≥73.5 points). A similar percentage of patients achieved MCID and PASS thresholds regardless of preoperative diagnosis. CONCLUSIONS: While MCID and MDC thresholds for all HOOS subdomains were slightly higher among non-OA than OA patients, PASS thresholds for HOOS pain and JR were slightly higher in the OA group. The absolute magnitude of the difference in these thresholds may not be sufficient to cause major clinical differences. However, these subtle differences may have a significant impact when used as indicators of operative success in a population setting.

Diagnosis-Specific Thresholds of the Minimal Clinically Important Difference and Patient Acceptable Symptom State for KOOS After Total Knee Arthroplasty.

UPDATE: This article was updated on May 1, 2024 because of a previous error, which was discovered after the preliminary version of the article was posted online. The byline that had read "Ahmed K. Emara, MD 1 *, Ignacio Pasqualini, MD 1 *, Alison K. Klika, MS 1 , Melissa N. Orr, BS 1 , Pedro J. Rullán, MD 1 , Nicolas S. Piuzzi, MD 1 , and the Cleveland Clinic Arthroplasty Group†" now reads "Ahmed K. Emara, MD 1 *, Ignacio Pasqualini, MD 1 *, Yuxuan Jin, MS 1 , Alison K. Klika, MS 1 , Melissa N. Orr, BS 1 , Pedro J. Rullán, MD 1 , Nicolas S. Piuzzi, MD 1 , and the Cleveland Clinic Arthroplasty Group†". BACKGROUND: Literature-reported minimal clinically important difference (MCID) and patient acceptable symptom state (PASS) thresholds for patient-reported outcome measures demonstrate marked variability. The purpose of this study was to determine the minimal detectable change (MDC), MCID, and PASS thresholds for the Knee injury and Osteoarthritis Outcome Score (KOOS) Pain subdomain, Physical Function Short Form (PS), and Joint Replacement (JR) among patients with osteoarthritis (OA) who underwent primary total knee arthroplasty (TKA). METHODS: A prospective cohort of 6,778 patients who underwent primary TKA was analyzed. Overall, 1-year follow-up was completed by 5,316 patients for the KOOS Pain, 5,018 patients for the KOOS PS, and 4,033 patients for the KOOS JR. A total of 5,186 patients had an OA diagnosis; this group had an average age of 67.0 years and was 59.9% female and 80.4% White. Diagnosis-specific MDCs and MCIDs were estimated with use of a distribution-based approach. PASS values were estimated with use of an anchor-based approach, which corresponded to a response to a satisfaction question at 1 year postoperatively. RESULTS: The MCID thresholds for the OA group were 7.9 for the KOOS Pain, 8.0 for the KOOS PS, and 6.7 for the KOOS JR. A high percentage of patients achieved the MCID threshold for each outcome measure (KOOS Pain, 95%; KOOS PS, 88%; and KOOS JR, 94%). The MDC 80% to 95% confidence intervals ranged from 9.1 to 14.0 for the KOOS Pain, 9.2 to 14.1 for the KOOS PS, and 7.7 to 11.8 for the KOOS JR. The PASS thresholds for the OA group were 77.7 for the KOOS Pain (achieved by 73% of patients), 70.3 for the KOOS PS (achieved by 68% of patients), and 70.7 for the KOOS JR (achieved by 70% of patients). CONCLUSIONS: The present study provided useful MCID, MDC, and PASS thresholds for the KOOS Pain, PS, and JR for patients with OA. The diagnosis-specific metrics established herein can serve as benchmarks for clinically meaningful postoperative improvement. Future research and quality assessments should utilize these OA-specific thresholds when evaluating outcomes following TKA. Doing so will enable more accurate determinations of operative success and improvements in patient-centered care. LEVEL OF EVIDENCE: Prognostic Level II . See Instructions for Authors for a complete description of levels of evidence.

The Mark Coventry Award: PhotothermAA Gel Combined With Debridement, Antibiotics, and Implant Retention Significantly Decreases Implant Biofilm Burden and Soft-Tissue Infection in a Rabbit Model of Knee Periprosthetic Joint Infection.

BACKGROUND: Chronic periprosthetic joint infection (PJI) is a major complication of total joint arthroplasty. The underlying pathogenesis often involves the formation of bacterial biofilm that protects the pathogen from both host immune responses and antibiotics. The gold standard treatment requires implant removal, a procedure that carries associated morbidity and mortality risks. Strategies to preserve the implant while treating PJI are desperately needed. Our group has developed an anti-biofilm treatment, PhotothermAA gel, which has shown complete eradication of 2-week-old mature biofilm in vitro. In this study, we tested the anti-biofilm efficacy and safety of PhotothermAA in vivo when combined with debridement, antibiotics and implant retention (DAIR) in a rabbit model of knee PJI. METHODS: New Zealand white rabbits (n = 21) underwent knee joint arthrotomy, titanium tibial implant insertion, and inoculation with Xen36 (bioluminescent Staphylococcus aureus) after capsule closure. At 2 weeks, rabbits underwent sham surgery (n = 6), DAIR (n = 6), or PhotothermAA with DAIR (n = 9) and were sacrificed 2 weeks later to measure implant biofilm burden, soft-tissue infection, and tissue necrosis. RESULTS: The combination of anti-biofilm PhotothermAA with DAIR significantly decreased implant biofilm coverage via scanning electron microscopy compared to DAIR alone (1.8 versus 81.0%; P < .0001). Periprosthetic soft-tissue cultures were significantly decreased in the PhotothermAA with DAIR treatment group (log reduction: Sham 1.6, DAIR 2.0, combination 5.6; P < .0001). Treatment-associated necrosis was absent via gross histology of tissue adjacent to the treatment area (P = .715). CONCLUSIONS: The addition of an anti-biofilm solution like PhotothermAA as a supplement to current treatments that allow implant retention may prove useful in PJI treatment.

National Inpatient Datasets May No Longer Be Appropriate for Overall Total Hip and Knee Arthroplasties Projections after Removal from Inpatient-Only Lists.

It is unknown if the National Inpatient Sample (NIS) remains suitable to conduct projections for total knee arthroplasty (TKA) and total hip arthroplasty (THA), after their removal from "inpatient-only lists" in 2018 and 2020, respectively. We aimed to: (1) quantify primary THA and TKA volume from 2008 to 2018; (2) project estimates of future volume of THA and TKA until 2050; and (3) compare projections based on NIS data from 2008 to 2018 and 2008 to 2017, respectively. We identified all primary THA and TKA performed from 2008 to 2018 from the NIS. The projected volumes of THA and TKA were modeled using negative binomial regression models while incorporating log-transformed population data from the Centers for Disease Control and Prevention. Annual volume increased by 26% for THA and 11% for TKA (2008/2018: THA: 360,891/465,559; TKA:592,352/657,294). Based on 2008 to 2018 data, THA volume is projected to grow 120%, to 1,119,942 THAs by 2050. While, based on 2008 to 2017 data, THA volume is projected to grow 136%, to 1,219,852 THAs by 2050. Based on 2008 to 2018 data, TKA volume is projected to grow 4%, to 794,852 TKAs by 2050. While, based on 2008 to 2017 data, TKA volume is projected to grow 28%, to 1,037,474 TKAs by 2050. Projections based on 2008 to 2017 data estimated up to 240,000 (23%) more annual TKAs by 2050, compared with projections based on 2008 to 2018 data. The largest discrepancy among THA projections was an 8.2% difference (99,000 THAs) for 2050. After 2018 for TKA, and potentially 2020 for THA, projections based on the NIS will have to be interpreted with caution and may only be appropriate to estimate future inpatient volume. Level of evidence is prognostic level II.

Intraincisional Pin Placement is Safe for Robotic-Assisted Total Knee Arthroplasty.

BACKGROUND: While robotic-arm assisted total knee arthroplasty (RA-TKA) has seen a major increase in its utilization, it requires bone array pins to be fixed into the femur and tibia, which intrinsically carries a risk. As it is currently off-label with some robotic platforms to place pins intraincisional, we aimed to evaluate the safety of intraincisional pin placement during RA-TKAs. METHODS: A prospective cohort of 2,343 patients who underwent RA-TKA at a North American Healthcare System between January 2018 and March 2022 was included. Primary outcomes included periprosthetic fracture or infection (eg, superficial or deep). Secondary outcomes included 1-year reoperation rate due to any cause. Cases were retrospectively reviewed to determine whether complications could be attributed to metaphyseal intraincisional pin placement (4.0 mm pins; two tibial and two femoral). The 90-day follow-up was 100% and the 1-year follow-up rate was 70.6% (n = 1,655). RESULTS: The pin-site related periprosthetic fracture incidence at 90 days was 0.09% (2 out of 2,343). The 90-day infection incidence was 1.4% (superficial: 22; deep: 13). The 1-year reoperation rate was 1.8% (29 out of 1,655). The most common causes of reoperation at 1-year were deep infection (n = 14; 0.83%), superficial infection (n = 3; 0.18%), periprosthetic fracture, mechanical symptoms, instability, and hematoma (n = 2; 0.12% for each). CONCLUSIONS: One in 1,172 patients may experience a pin-related periprosthetic fracture after RA-TKA with intraincisional bone array pin placement. There was a low 90-day infection incidence and reoperations within 1-year after RA-TKA were rare.

A Longitudinal Analysis of Weight Changes before and after Total Knee Arthroplasty: Weight Trends, Patterns, and Predictors.

Longitudinal data on patient trends in body mass index (BMI) and the proportion that gains or loses significant weight before and after total knee arthroplasty (TKA) are scarce. This study aimed to observe patients longitudinally for a 2-year period and determine (1) clinically significant BMI changes during the 1 year before and 1 year after TKA and (2) identify factors associated with clinically significant weight changes.A prospective cohort of 5,388 patients who underwent primary TKA at a tertiary health care institution between January 2016 and December 2019 was analyzed. The outcome of interests was clinically significant weight changes, defined as a ≥5% change in BMI, during the 1-year preoperative and postoperative periods, respectively. Patient-specific variables and demographics were assessed as potential predictors of weight change using multinomial logistic regression.Overall, 47% had a stable weight throughout the study period (preoperative: 17% gained, 15% lost weight; postoperative: 19% gained, 16% lost weight). Patients who were older (odds ratio [OR] = 0.95), men (OR = 0.47), overweight (OR = 0.36), and Obese Class III (OR = 0.06) were less likely to gain weight preoperatively. Preoperative weight loss was associated with postoperative weight gain 1 year after TKA (OR = 3.03). Preoperative weight gain was associated with postoperative weight loss 1 year after TKA (OR = 3.16).Most patients maintained a stable weight before and after TKA. Weight changes during the 1 year before TKA were strongly associated with reciprocal rebounds in BMI postoperatively, emphasizing the importance of ongoing weight management during TKA and the recognition of patients at higher risk for weight gain.Level of evidence II (prospective cohort study).

Preoperative and Postoperative Weight Change has Minimal Influence on Health Care Utilization and Patient-Reported Outcomes Following Total Knee Arthroplasty.

As obesity becomes more prevalent, more patients are at risk of lower extremity osteoarthritis and subsequent total knee arthroplasty (TKA). This study aimed to test (1) the association of preoperative weight change with health care utilization and (2) the association of pre- and postoperative weight changes with failure to achieve satisfaction and minimal clinically important difference (MCID) in Knee injury and Osteoarthritis Outcome Score for pain (KOOS-Pain) and function (KOOS-PS) 1 year after TKA. Prospectively collected monocentric data on patients who underwent primary TKA were retrospectively reviewed. Multivariable logistic regression assessed the influence of BMI and weight change on outcomes while controlling for confounding variables. Outcomes included prolonged length of stay (LOS >3 days), nonhome discharge, 90-day readmission rate, satisfaction, and achievement of MCID for KOOS-Pain and KOOS-PS. Preoperative weight change had no impact on prolonged LOS (gain, p = 0.173; loss, p = 0.599). Preoperative weight loss was associated with increased risk of nonhome discharge (odds ratio [OR]: 1.47, p = 0.003). There was also increased risk of 90-day readmission with preoperative weight gain (OR: 1.27, p = 0.047) and decreased risk with weight loss (OR: 0.73, p = 0.033). There was increased risk of nonhome discharge with obesity class II (OR: 1.6, p = 0.016) and III (OR: 2.21, p < 0.001). Weight change was not associated with failure to achieve satisfaction, MCID in KOOS-Pain, or MCID in KOOS-PS. Obesity class III patients had decreased risk of failure to reach MCID in KOOS-Pain (OR: 0.43, p = 0.005) and KOOS-PS (OR: 0.7, p = 0.007). Overall, pre- and postoperative weight change has little impact on the achievement of satisfaction and clinically relevant differences in pain and function at 1 year. However, preoperative weight gain was associated with a higher risk of 90-day readmissions after TKA. Furthermore, patients categorized in Class III obesity were at increased risk of nonhome discharge but experienced a greater likelihood of achieving MCID in KOOS-Pain and KOOS-PS. Our results raise awareness of the dangers of using weight changes and BMI alone as a measure of TKA eligibility.

Considering Mobility Status and Home Environment in Readmission Risk After Total Knee Arthroplasty.

BACKGROUND: Discharge disposition following total knee arthroplasty (TKA) offers varying levels of post-acute care monitoring depending on the medical status of the patient and his or her ability to function independently. Discharge disposition following TKA is associated with 30-day and 90-day hospital readmission, but prior studies have not consistently considered confounding due to mobility status after TKA, available caregiver support, and measures of home area deprivation. The purpose of this study was to examine 30-day and 90-day readmission risk for patients discharged to a skilled nursing facility (SNF) following TKA after controlling specifically for these factors, among other covariates. METHODS: This was a retrospective cohort study of patients undergoing TKA at any of 11 hospitals in a single, large, academic health-care system between January 2, 2017, and August 31, 2022, who were discharged to an SNF or home health care (HHC). The adjusted relative risk of readmission within 30 and 90 days of discharge to an SNF compared with HHC was estimated using modified Poisson regression models. RESULTS: There were 15,212 patients discharged to HHC and 1,721 patients discharged to SNFs. Readmission within 30 days was 7.1% among patients discharged to SNFs and 2.4% among patients discharged to HHC; readmission within 90 days was 12.1% for the SNF group and 4.8% for the HHC group. The adjusted relative risk after discharge to an SNF was 1.07 (95% confidence interval [CI], 0.79 to 1.46; p = 0.65) for 30-day readmission and 1.45 (95% CI, 1.16 to 1.82; p < 0.01) for 90-day readmission. CONCLUSIONS: Discharge to an SNF compared with HHC was independently associated with 90-day readmission, but not with 30-day readmission, after controlling for mobility status after TKA, available caregiver support, and home Area Deprivation Index, among other covariates. LEVEL OF EVIDENCE: Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

The Use of Oral Antibiotics After Total Joint Arthroplasty: A Critical Analysis Review.

¼ There is conflicting and insufficient evidence that extended oral antibiotic (EOA) therapy prevents infection in high-risk patients undergoing primary total joint arthroplasty (TJA), limiting recommendation for or against the practice.¼ In the case of aseptic revision TJA, the evidence is also conflicting and limited by underlying confounders, preventing recommendation for use of EOA.¼ There is fair evidence that use of EOA after debridement antibiotic therapy and implant retention of the prosthesis prolongs infection-free survival, but randomized controlled trials are needed. On the other hand, there is strong evidence that patients undergoing 2-stage revision should receive a period of suppressive oral antibiotics after the second stage.¼ The optimal duration of EOA in primary TJA, aseptic revision, and debridement antibiotic therapy and implant retention of the prosthesis is unknown. However, there is strong evidence that 3 months of EOA suppression may be appropriate after reimplantation as part of 2-stage exchange arthroplasty.¼ Complications secondary to EOA are reported to be between 0% and 13.7%, yet are inconsistently reported and poorly defined. The risks associated with antibiotic use, including development of antimicrobial resistance, must be weighed against a possible decrease in infection rate.

Return to Sports and Return to Work After Total Hip Arthroplasty: A Systematic Review and Meta-analysis.

BACKGROUND: Return to work (RTW) and sports (RTS) are critical gauges to improvement among patients after total hip arthroplasty (THA). This study aimed to determine rates, timelines, and prognostic factors associated with RTW and RTS outcomes after primary THA. METHODS: A systematic review was conducted on MEDLINE, EMBASE, and CENTRAL databases with 57 studies meeting inclusion/exclusion criteria. The risk of bias was assessed using the Risk of Bias in Non-randomized Studies of Interventions and risk of bias in randomized trials (RoB2) tools. Meta-analysis and pooled analysis were conducted, with forest plots to summarize odds ratios and 95% confidence interval (CI). RESULTS: The pooled RTW rate across all studies was 70% (95% CI, 68%-80%), with rates varying significantly from 11% to 100%. The mean time to RTW was 11.2 weeks (range 1-27). A time point analysis showed increasing RTW rates with a maximum rate at 2 years of 90%. Increased age (p < 0.001) and preoperative heavy labor (p = 0.005) were associated with lower RTW rates. The RTS rate ranged from 42% to 100%, with a pooled rate of 85% (95% CI, 74%-92%). The mean time to RTS was 16.1 weeks (range 8-26). The RTS ranged from 20% to 80% with a pooled proportion of 56% (95% CI, 42%-70%, I2 = 90%) for high-intensity sports and from 75% to 100% for low-intensity sports with a pooled proportion of 97% (95% CI, 83-99, I2 = 93%). CONCLUSION: Most patients RTW and RTS after THA in an increasing manner as time passes with rates more than 85% after 1 year. These rates may be greatly affected by various factors, most notably age, the intensity of the sport, and the type of work performed. In general, young patients, low-demand work or sports can be resumed as soon as 4 to 6 weeks after surgery, but with increased restrictions as the intensity increases. This information should be used by practitioners to manage postoperative expectations and provide appropriate recommendations to patients. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

The Arthroplasty Surgeon Growth Indicator: A Tool for Monitoring Supply and Demand Trends in the Orthopaedic Surgeon Workforce from 2020 to 2050.

BACKGROUND: Orthopaedic practices in the U.S. face a growing demand for total joint arthroplasties (TJAs), while the orthopaedic workforce size has been stagnant for decades. This study aimed to estimate annual TJA demand and orthopaedic surgeon workforce supply from 2020 to 2050, and to develop an arthroplasty surgeon growth indicator (ASGI), based on the arthroplasty-to-surgeon ratio (ASR), to gauge nationwide supply and demand trends. METHODS: National Inpatient Sample and Association of American Medical Colleges data were reviewed for individuals who underwent primary TJA and for active orthopaedic surgeons (2010 to 2020), respectively. The projected annual TJA volume and number of orthopaedic surgeons were modeled using negative binominal and linear regression, respectively. The ASR is the number of actual (or projected) annual total hip (THA) and/or knee (TKA) arthroplasties divided by the number of actual (or projected) orthopaedic surgeons. ASGI values were calculated using the 2017 ASR values as the reference, with the resulting 2017 ASGI defined as 100. RESULTS: The ASR calculation for 2017 showed an annual caseload per orthopaedic surgeon (n = 19,001) of 24.1 THAs, 41.1 TKAs, and 65.2 TJAs. By 2050, the TJA volume was projected to be 1,219,852 THAs (95% confidence interval [CI]: 464,808 to 3,201,804) and 1,037,474 TKAs (95% CI: 575,589 to 1,870,037). The number of orthopaedic surgeons was projected to decrease by 14% from 2020 to 2050 (18,834 [95% CI: 18,573 to 19,095] to 16,189 [95% CI: 14,724 to 17,655]). This would yield ASRs of 75.4 THAs (95% CI: 31.6 to 181.4), 64.1 TKAs (95% CI: 39.1 to 105.9), and 139.4 TJAs (95% CI: 70.7 to 287.3) by 2050. The TJA ASGI would double from 100 in 2017 to 213.9 (95% CI: 108.4 to 440.7) in 2050. CONCLUSIONS: Based on historical trends in TJA volumes and active orthopaedic surgeons, the average TJA caseload per orthopaedic surgeon may need to double by 2050 to meet projected U.S. demand. Further studies are needed to determine how the workforce can best meet this demand without compromising the quality of care in a value-driven health-care model. However, increasing the number of trained orthopaedic surgeons by 10% every 5 years may be a potential solution.

Demographic and Socioeconomic Determinants Are Associated with Poor Preoperative Patient-Reported Pain and Function in Primary TKA: A Cohort Study of 14,079 Patients.

BACKGROUND: Pain and function, as reflected by patient-reported outcome measures (PROMs), can influence improvement after total knee arthroplasty (TKA) and can reflect the extent of patient access to orthopaedic surgical care. We aimed to (1) categorize patients according to pain and function PROM phenotypes, (2) identify patient characteristics associated with poor preoperative pain and function, and (3) assess relationships between baseline characteristics and PROM phenotypes. METHODS: A prospective cohort of 14,079 TKAs was enrolled. Demographics, comorbidities, surgical details, and preoperative PROMs were collected. Outcomes included preoperative Knee injury and Osteoarthritis Outcome Score (KOOS) Pain (P) and Physical Function (PS) subscores, stratified by demographics. Patients were then categorized into 4 PROM phenotypes defined on the basis of the cohort medians: above or equal to the median for both pain and function scores (P+PS+), below the median for both pain and function (P-PS-), above or equal to the median for pain but below the median for function (P+PS-), and below the median for pain but above or equal to the median for function (P-PS+). Descriptive statistics and multivariable regression analyses were calculated. RESULTS: The largest PROM phenotype was P-PS- (39.4%), followed by P+PS+ (38.9%). The cohort with discordantly poor function but high pain scores (less pain) was the smallest cohort (9.9%). Preoperative KOOS-Pain and KOOS-PS scores at or below the 25th percentile were independently associated with younger age, female sex, higher body mass index (BMI), non-White race, current smoking, lower education, non-commercial insurance, and higher Charlson Comorbidity Index (CCI). Multivariate logistic regression showed that patients in the P+PS+ category were older (odds ratio [OR] = 1.56), were more likely to be male (OR = 2.00), had a lower BMI (OR = 0.67), had more education (OR = 1.63), had a lower CCI, and were less likely to be Black (OR = 0.80) or Other (OR = 0.62) race, be a current smoker (OR = 0.62), and have commercial insurance (OR = 0.74), compared with the P-PS- phenotype. CONCLUSIONS: Younger age, obesity, non-White race, female sex, current or recent smoking, non-commercial insurance, and higher CCI were associated with worse pre-TKA PROMs and poor pain-function phenotype combinations. Such a pattern may indicate barriers to TKA access among these patient populations leading to advanced levels of impairment at the time of treatment. LEVEL OF EVIDENCE: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Similar Healthcare Utilization and 1-Year Patient-Reported Outcomes between Cemented and Cementless Primary Total Knee Arthroplasty: A Propensity Score-Matched Analysis.

Cementless fixation for total knee arthroplasty (TKA) has gained traction with the advent of newer fixation technologies. This study assessed (1) healthcare utilization (length of stay (LOS), nonhome discharge, 90-day readmission, and 1-year reoperation); (2) 1-year mortality; and (3) 1-year joint-specific and global health-related patient-reported outcome measures (PROMs) among patients who received cementless versus cemented TKA. Patients who underwent cementless and cemented TKA at a single institution (July 2015-August 2018) were prospectively enrolled. A total of 424 cementless and 5,274 cemented TKAs were included. The cementless cohort was propensity score-matched to a group cemented TKAs (1:3-cementless: n = 424; cemented: n = 1,272). Within the matched cohorts, 76.9% (n = 326) cementless and 75.9% (n = 966) cementless TKAs completed 1-year PROMs. Healthcare utilization measures, mortality and the median 1-year change in knee injury and osteoarthritis outcome score (KOOS)-pain, KOOS-physical function short form (PS), KOOS-knee related quality of life (KRQOL), Veteran Rand (VR)-12 mental composite (MCS), and physical composite (PCS) scores were compared. The minimal clinically important difference (MCID) for PROMs was calculated. Cementless TKA exhibited similar rates of median LOS (p = 0.109), nonhome discharge disposition (p = 0.056), all-cause 90-day readmission (p = 0.226), 1-year reoperation (p = 0.597), and 1-year mortality (p = 0.861) when compared with cemented TKA. There was no significant difference in the median 1-year improvement in KOOS-pain (p = 0.370), KOOS-PS (p = 0.417), KOOS-KRQOL (p = 0.101), VR-12-PCS (p = 0.269), and VR-12-MCS (p = 0.191) between the cementless and cemented TKA cohorts. Rates of attaining MCID were similar in both cohorts for assessed PROMs (p > 0.05, each) except KOOS-KRQOL (cementless: n = 313 (96.0%) vs. cemented: n = 895 [92.7%]; p = 0.036). Cementless TKA provides similar healthcare-utilization, mortality, and 1-year PROM improvement versus cemented TKA. Cementless fixation in TKA may provide value through higher MCID improvement in quality of life. Future episode-of-care cost-analyses and longer-term survivorship investigations are warranted.

Can extant comorbidity indices identify patients who experience poor outcomes following total joint arthroplasty?

INTRODUCTION: It is uncertain if generic comorbidity indices commonly used in orthopedics accurately predict outcomes after total hip (THA) or knee arthroplasty (TKA). The purpose of this study was to determine the predictive ability of such comorbidity indices for: (1) 30-day mortality; (2) 30-day rate of major and minor complications; (3) discharge disposition; and (4) extended length of stay (LOS). METHODS: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was retrospectively reviewed for all patients who underwent elective THA (n = 202,488) or TKA (n = 230,823) from 2011 to 2019. The American Society of Anesthesiologists (ASA) physical status classification system score, modified Charlson Comorbidity Index (mCCI), Elixhauser Comorbidity Measure (ECM), and 5-Factor Modified Frailty Index (mFI-5) were calculated for each patient. Logistic regression models predicting 30-day mortality, discharge disposition, LOS greater than 1 day, and 30-day major and minor complications were fit for each index. RESULTS: The ASA classification (C-statistic = 0.773 for THA and TKA) and mCCI (THA: c-statistic = 0.781; TKA: C-statistic = 0.771) were good models for predicting 30-day mortality. However, ASA and mCCI were not predictive of major and minor complications, discharge disposition, or LOS. The ECM and mFI-5 did not reliably predict any outcomes of interest. CONCLUSION: ASA and mCCI are good models for predicting 30-day mortality after THA and TKA. However, similar to ECM and mFI-5, these generic comorbidity risk-assessment tools do not adequately predict 30-day postoperative outcomes or in-hospital metrics. This highlights the need for an updated, data-driven approach for standardized comorbidity reporting and risk assessment in arthroplasty.