High Area Deprivation Index is Associated With Not Achieving the Patient-acceptable Symptom State After TKA.

BACKGROUND: The Area Deprivation Index (ADI) approximates a patient's relative socioeconomic deprivation. The ADI has been associated with increased healthcare use after TKA, but it is unknown whether there is an association with patient-reported outcome measures (PROMs). Given that a high proportion of patients are dissatisfied with their results after TKA, and the large number of these procedures performed, knowledge of factors associated with PROMs may indicate opportunities to provide support to patients who might benefit from it. QUESTIONS/PURPOSES: (1) Is the ADI associated with achieving the minimum clinically important difference (MCID) for the Knee Injury and Osteoarthritis Outcome Score (KOOS) for pain, Joint Replacement (JR), and Physical Function (PS) short forms after TKA? (2) Is the ADI associated with achieving the patient-acceptable symptom state (PASS) thresholds for the KOOS pain, JR, and PS short forms? METHODS: This was a retrospective study of data drawn from a longitudinally maintained database. Between January 2016 and July 2021, a total of 12,239 patients underwent unilateral TKA at a tertiary healthcare center. Of these, 92% (11,213) had available baseline PROM data and were potentially eligible. An additional 21% (2400) of patients were lost before the minimum study follow-up of 1 year or had incomplete data, leaving 79% (8813) for analysis here. The MCID is the smallest change in an outcome score that a patient is likely to perceive as a clinically important improvement, and the PASS refers to the threshold beyond which patients consider their symptoms acceptable and consistent with adequate functioning and well-being. MCIDs were calculated using a distribution-based method. Multivariable logistic regression models were created to investigate the association of ADI with 1-year PROMs while controlling for patient demographic variables. ADI was stratified into quintiles based on their distribution in our sample. Achievement of MCID and PASS thresholds was determined by the improvement between preoperative and 1-year PROMs. RESULTS: After controlling for patient demographic factors, ADI was not associated with an inability to achieve the MCID for the KOOS pain, KOOS PS, or KOOS JR. A higher ADI was independently associated with an increased risk of inability to achieve the PASS for KOOS pain (for example, the odds ratio of those in the ADI category of 83 to 100 compared with those in the 1 to 32 category was 1.34 [95% confidence interval 1.13 to 1.58]) and KOOS JR (for example, the OR of those in the ADI category of 83 to 100 compared with those in the 1 the 32 category was 1.29 [95% CI 1.10 to 1.53]), but not KOOS PS (for example, the OR of those in the ADI category of 83 to 100 compared with those in the 1 the 32 category was 1.09 [95% CI 0.92 to 1.29]). CONCLUSION: Our findings suggest that social and economic factors are associated with patients' perceptions of their overall pain and function after TKA, but such factors are not associated with patients' perceptions of their improvement in symptoms. Patients from areas with higher deprivation may be an at-risk population and could benefit from targeted interventions to improve their perception of their healthcare experience, such as through referrals to nonemergent medical transportation and supporting applications to local care coordination services before proceeding with TKA. Future research should investigate the mechanisms underlying why socioeconomic disadvantage is associated with inability to achieve the PASS, but not the MCID, after TKA. LEVEL OF EVIDENCE: Level III, therapeutic study.

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.

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.

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.

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.

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.

Does Your Patient Need to Lose Weight? Weight Change Before and After Total Hip Arthroplasty Does Not Affect Health Care Utilization and Achievement of the Minimal Clinically Important Difference.

BACKGROUND: Despite the potential negative impact of preoperative obesity on total hip arthroplasty (THA) outcomes, the association between preoperative and postoperative weight change and outcomes is much less understood. Therefore, this study aimed to determine the impact of preoperative and postoperative weight change and preoperative body mass index (BMI) on health care utilization, satisfaction, and achievement of minimal clinically important difference (MCID) for Hip Disability and Osteoarthritis Outcome Score Physical Function Short-Form (HOOS PS) and HOOS Pain. METHODS: Patients who underwent primary elective unilateral THA between January 2016 and December 2019 were included (N = 2,868). Multivariable logistic regression assessed the association between BMI and preoperative and postoperative weight change on outcomes while controlling for demographic characteristics. RESULTS: There was no association between preoperative weight change and prolonged length of stay (> 3 days), 90-day readmission, nonhome discharge, patient dissatisfaction at 1 year, or achievement of HOOS Pain or HOOS PS MCID. Postoperative weight loss was an independent risk factor for patient dissatisfaction at 1 year but was not associated with achievement of either HOOS Pain or HOOS PS MCID at 1-year postoperative. Preoperative obesity classes I to III were independent risk factors for nonhome discharge. Nevertheless, preoperative obesity class I and class II were associated with an increased probability of reaching HOOS Pain MCID. Preoperative BMI was not associated with an increased risk of patient dissatisfaction. CONCLUSIONS: Preoperative weight change does not appear to influence health care utilization, satisfaction, or achievement of MCID in pain and function following THA. Postoperative weight loss may play a role as a risk factor for dissatisfaction following THA. Additionally, patients who had a higher baseline BMI may be more likely to see improvement in pain following THA. Therefore, when counseling obese patients for THA, surgeons must balance the risk of perioperative complications with the expectation of greater improvements in pain.

Lower 90-day inpatient readmission and 1-year reoperation in patients undergoing robotic versus manual total hip arthroplasty through an anterior approach.

BACKGROUND: The value of robotic-assisted total hip arthroplasty (rTHA) has yet to be determined compared to conventional manual THA (mTHA). OBJECTIVE: Evaluate 90-day inpatient readmission rates, rates of reoperation, and clinically significant improvement of patient-reported outcome measures (PROMs) at 1-year in a cohort of patients who underwent mTHA or rTHA through a direct anterior (DA) approach. METHODS: A single-surgeon, prospective institutional cohort of 362 patients who underwent primary THA for osteoarthritis via the DA approach between February 2019 and November 2020 were included. Patient demographics, surgical time, discharge disposition, length of stay, acetabular cup size, 90-day inpatient readmission, 1-year reoperation, and 1-year PROMs were collected for 148 manual and 214 robotic THAs, respectively. RESULTS: Patients undergoing rTHA had lower 90-day readmission (3.74% vs 9.46%, p= 0.04) and lower 1-year reoperation (0.93% vs 4.73% mTHA, p= 0.04). rTHA acetabular cup sizes were smaller (rTHA median 52, interquartile range [IQR] 50; 54, mTHA median 54, IQR 52; 58, p< 0.001). Surgical time was longer for rTHA (114 minutes vs 101 minutes, p< 0.001). At 1-year post-operatively, there was no difference in any of the PROMs evaluated. CONCLUSION: Robotic THA demonstrated lower 90-day readmissions and 1-year reoperation rates than manual THA via the DA approach. PROMs were not significantly different between the two groups at one year.

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

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.

High Risk of Readmission After THA Regardless of Functional Status in Patients Discharged to Skilled Nursing Facility.

BACKGROUND: The postoperative period and subsequent discharge planning are critical in our continued efforts to decrease the risk of complications after THA. Patients discharged to skilled nursing facilities (SNFs) have consistently exhibited higher readmission rates compared with those discharged to home healthcare. This elevated risk has been attributed to several factors but whether readmission is associated with patient functional status is not known. QUESTIONS/PURPOSES: After controlling for relevant confounding variables (functional status, age, gender, caregiver support available at home, diagnosis [osteoarthritis (OA) versus non-OA], Charlson comorbidity index [CCI], the Area Deprivation Index [ADI], and insurance), are the odds of 30- and 90-day hospital readmission greater among patients initially discharged to SNFs than among those treated with home healthcare after THA? METHODS: This was a retrospective, comparative study of patients undergoing THA at any of 11 hospitals in a single, large, academic healthcare system between 2017 and 2022 who were discharged to an SNF or home healthcare. During this period, 13,262 patients were included. Patients discharged to SNFs were older (73 ± 11 years versus 65 ± 11 years; p < 0.001), less independent at hospital discharge (6-click score: 16 ± 3.2 versus 22 ± 2.3; p < 0.001), more were women (71% [1279 of 1796] versus 56% [6447 of 11,466]; p < 0.001), insured by Medicare (83% [1497 of 1796] versus 52% [5974 of 11,466]; p < 0.001), living in areas with greater deprivation (30% [533 of 1796] versus 19% [2229 of 11,466]; p < 0.001), and had less assistance available from at-home caregivers (29% [527 of 1796] versus 57% [6484 of 11,466]; p < 0.001). The primary outcomes assessed in this study were 30- and 90-day hospital readmissions. Although the system automatically flags readmissions occurring within 90 days at the various facilities in the overall healthcare system, readmissions occurring outside the system would not be captured. Therefore, we were not able to account for potential differential rates of readmission to external healthcare systems between the groups. However, given the large size and broad geographic coverage of the healthcare system analyzed, we expect the readmissions data captured to be representative of the study population. The focus on a single healthcare system also ensures consistency in readmission identification and reporting across subjects. We evaluated the association between discharge disposition (home healthcare versus SNF) and readmission. Covariates evaluated included age, gender, primary payer, primary diagnosis, CCI, ADI, the availability of at-home caregivers for the patient, and the Activity Measure for Post-Acute Care (AM-PAC) 6-clicks basic mobility score in the hospital. The adjusted relative risk (ARR) of readmission within 30 and 90 days of discharge to SNF (versus home healthcare) was estimated using modified Poisson regression models. RESULTS: After adjusting for the 6-clicks mobility score, age, gender, ADI, OA versus non-OA, living environment, CCI, and insurance, patients discharged to an SNF were more likely to be readmitted within 30 and 90 days compared with home healthcare after THA (ARR 1.46 [95% CI 1.01 to 2.13]; p= 0.046 and ARR 1.57 [95% CI 1.23 to 2.01]; p < 0.001, respectively). CONCLUSION: Patients discharged to SNFs after THA had a slightly higher likelihood of hospital readmission within 30 and 90 days compared with those discharged with home healthcare. This difference persisted even after adjusting for relevant factors like functional status, home support, and social determinants of health. These results indicate that for suitable patients, direct home discharge may be a safer and more cost-effective option than SNFs. Clinicians should carefully consider these risks and benefits when making postoperative discharge plans. Policymakers could consider incentives and reforms to improve care transitions and coordination across settings. Further research using robust methods is needed to clarify the reasons for higher SNF readmission rates. Detailed analysis of patient complexity, care processes, and causes of readmission in SNFs versus home health could identify areas for quality improvement. Prospective cohorts or randomized trials would allow stronger conclusions about cause-and-effect. Importantly, no patients should be unfairly "cherry-picked" or "lemon-dropped" based only on readmission risk scores. With proper support and care coordination, even complex patients can have good outcomes. The goal should be providing excellent rehabilitation for all, while continuously improving quality, safety, and value across settings. LEVEL OF EVIDENCE: Level III, therapeutic study.

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.

Establishing patient-centered metrics for the knee injury and osteoarthritis outcome score following medial unicompartmental knee arthropalsty.

BACKGROUND: This study aimed to determine the minimal clinically important difference (MCID) and the patient acceptable symptoms state (PASS) threshold for the knee injury and osteoarthritis outcome score (KOOS) pain subscore, KOOS physical short form (PS), and KOOS joint replacement (JR) following medial unicompartmental knee arthroplasty (mUKA). METHODS: Prospectively collected data from 743 patients undergoing mUKA from a single academic institution from April 2015 through March 2020 were analyzed. Patient-reported outcome measures (PROMs) were collected both pre-operatively and 1-year post-operatively. Distribution-based and anchored-based approaches were used to estimate MCIDs and PASS, respectively. The optimal cut-off point and the percentage of patients who achieved PASS were also calculated. RESULTS: MCID for KOOS-pain, KOOS-PS, and KOOS-JR following mUKA were calculated to be 7.6, 7.3, and 6.2, respectively. The PASS threshold for KOOS pain, PS, and JR were 77.8, 70.3, and 70.7, with 68%, 66%, and 64% of patients achieving satisfactory outcomes, respectively. Cut-off values for delta KOOS pain, PS, and JR were found to be 25.7, 14.3, and 20.7 with 73%, 69%, and 68% of patients achieving satisfactory outcomes, respectively. CONCLUSION: The current study identified useful values for the MCID and PASS thresholds at 1 year following medial UKA of KOOS pain, KOOS PS, and KOOS JR scores. These values may be used as targets for surgeons when evaluating PROMS using KOOS to determine whether patients have achieved successful outcomes after their surgical intervention. Potential uses include the integration of these values into predictive models to enhance shared decision-making and guide more informed decisions to optimize patient outcomes. LEVEL OF EVIDENCE: III.

Periprosthetic joint infection and immunity: Current understanding of host-microbe interplay.

Periprosthetic joint infection (PJI) is a major complication of total joint arthroplasty. Even with current treatments, failure rates are unacceptably high with a 5-year mortality rate of 26%. Majority of the literature in the field has focused on development of better biomarkers for diagnostics and treatment strategies including innovate antibiotic delivery systems, antibiofilm agents, and bacteriophages. Nevertheless, the role of the immune system, our first line of defense during PJI, is not well understood. Evidence of infection in PJI patients is found within circulation, synovial fluid, and tissue and include numerous cytokines, metabolites, antimicrobial peptides, and soluble receptors that are part of the PJI diagnosis workup. Macrophages, neutrophils, and myeloid-derived suppressor cells (MDSCs) are initially recruited into the joint by chemokines and cytokines produced by immune cells and bacteria and are activated by pathogen-associated molecular patterns. While these cells are efficient killers of planktonic bacteria by phagocytosis, opsonization, degranulation, and recruitment of adaptive immune cells, biofilm-associated bacteria are troublesome. Biofilm is not only a physical barrier for the immune system but also elicits effector functions. Additionally, bacteria have developed mechanisms to evade the immune system by inactivating effector molecules, promoting killing or anti-inflammatory effector cell phenotypes, and intracellular persistence and dissemination. Understanding these shortcomings and the mechanisms by which bacteria can subvert the immune system may open new approaches to better prepare our own immune system to combat PJI. Furthermore, preoperative immune system assessment and screening for dysregulation may aid in developing preventative interventions to decrease PJI incidence.

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.

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).

Challenging the Status Quo: Debunking the Necessity of 5-Year to 10-Year Patient-Reported Outcome Measures in Total Hip and Knee Arthroplasties.

BACKGROUND: In the current shift toward value-based healthcare, patient-reported outcome measures (PROMs) have become essential to assess the effectiveness of medical interventions. However, elucidation of the optimal timeframe for PROMs evaluation remains crucial. This study aimed to (1) determine the proportion of patients who experienced clinically meaningful improvements in PROMs scores at each follow-up visit after total hip arthroplasty (THA) and total knee arthroplasty (TKA) and (2) calculate and apply the clinical relevance ratio (CRR) for these long-term PROM collections postoperatively. METHODS: A total of 12 independent studies reporting THA (n = 8 studies) and TKA (n = 4 studies) postoperative PROM data with up to 10 years of follow-up in Europe or the United States were aggregated. A distribution-based minimal clinically important difference threshold and CRR were used to determine which patients had clinically meaningful improvements in PROMs at 1, 5, and 10 years. RESULTS: The proportion of patients who had clinically meaningful improvements in PROM scores stabilized after 1 year following both THA and TKA. Overall, the CRR decreased over time for all PROMs, with the CRR beginning to decrease at 1-year follow-up, bringing into question the robustness and clinical relevance of long-term PROMs data. CONCLUSIONS: The present study challenges the utility of requiring PROMs with a minimum follow-up of 2 years for THA and TKA. Research efforts should be focused on registries evaluating implant survivorship at longer-term follow-up, while PROMs should be better assessed up to 1-year follow-up. Reconsidering the long-term PROMs assessment would lead to more efficient and cost-effective research in orthopedic outcomes, without compromising data quality.

What Drives the Material Costs of Total Knee Arthroplasty in the Operating Room?

INTRODUCTION: Approximately one-third of US healthcare spending is related to surgical care. Optimizing operating room (OR) spending is crucial, specifically for high-volume procedures like total knee arthroplasty (TKA). Therefore, the primary objective was to identify leading material drivers of cost for TKA procedures within the OR. MATERIALS AND METHODS: Patients who underwent a primary, elective TKA from 2018 to 2019 were included (n=8,672). Intraoperative cost details for each TKA patient were captured from the Vizient Clinical Database Resource Manager (CDB/RM) data. Each cost type was categorized into (1) implant, (2) disposables, (3) wound care, and (4) miscellaneous. RESULTS: 7,124 patients undergoing primary TKA were included. Implant-related costs accounted for 87.3% of cost, disposable materials covered 10.7%, and wound care products took 2%. The leading subcategories of implant costs were primary prosthetics (85.1%), revision prosthetics (9.9%), cement (2.8%), and implant instruments (1.7%). Within disposables, surgical products accounted for 81.3% of the cost, patient care products for 8.9%, medical apparel for 7.9%, and electrolytes for 1.8%. For an average individual TKA procedure, 86.4% (±4.4) of total cost went towards the implant, 10.7% (±3.4) towards disposable materials, and 1.6% (±1.4) to wound care products. Within the implant category, 92.5% (± 12.8) of costs were associated with primary implants, 13.3% (± 6.9) with instruments, and 2.5% (± 2.8) with cement. CONCLUSIONS: The primary operative material expense category was costs associated with the TKA prosthesis and its fixation followed by disposable materials. A large amount of variation exists in the percent of the total cost for a given TKA procedure that can be attributed to each category.

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.

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.

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.