Faculty Bibliography

INTRODUCTION/BACKGROUND:The integration of computer-assisted navigation systems (CASs) in total knee arthroplasty (TKA) procedures has gained popularity in recent years. However, additional validation of the accuracy of CAS feedback is necessary. We used short-length and full-length postoperative radiographs to quantify the differences between alignment parameters measured by a novel imageless CAS and alignment outcomes as evidenced on postoperative radiographs. MATERIALS AND METHODS/METHODS:A retrospective analysis was conducted on prospectively collected data from a cohort of patients undergoing navigated primary TKA. Fifty-eight patients had met inclusion criteria, and intraoperative CAS measurements were obtained from device recordings. Alignment parameters were measured digitally and included femorotibial angle on short-length films and hip-knee-ankle axis, mechanical lateral distal femoral angle (mLDFA), and mechanical medial proximal tibial angle (mMPTA) on full-length films. These were compared between CAS and radiograph measurements using a 2-tailed t test. RESULTS:The mean mLDFA measured by the CAS was 0.7° ± 1.1°, compared with 1.3° ± 1.4° as measured on full-body radiographs (P = .1). The mean mMPTA measured by the CAS was 0.2° ± 1.0°, compared with 0.9° ± 1.4° as measured on full-body radiographs (P = .06). On average, radiograph and CAS measurements differed by 0.5° ± 1.5° for mLDFA and 0.7° ± 1.5° for mMPTA. The average postoperative hip-knee-ankle axis was 177.6° ± 2.1°, and the average femorotibial angle was 176.0° ± 9.6° as measured on radiographs. CONCLUSION/CONCLUSIONS:No significant differences in either average or individual measured values for mLDFA or mMPTA were observed between the intraoperative CAS measurements and alignment outcomes postoperatively. Our data highlight the clinical utility of CASs to accurately achieve intended TKA alignment objectives.

Genetics is a burgeoning field within adult reconstructive surgery. Genome-wide sequencing has identified genetic variants found to be associated with not only the development of osteoarthritis but also arthroplasty-related complications, such as aseptic loosening, prosthetic joint infection, arthrofibrosis, and postoperative pain. Examples include newer technology, such as next-generation sequencing, in diagnosing culture-negative prosthetic joint infection. Genetics drives new therapeutic technologies, such as gene therapy, gene-editing, and bacteriophage treatment. Although still rare, a handful of phase 3 clinical trials of gene therapy for osteoarthritis have begun to demonstrate efficacy with low-risk profiles. As the field continues to grow, public and professional buy-in as well as cost present challenges.

BACKGROUND:There is a growing number of patients who undergo multiple primary hip and knee joint arthroplasties during their lifetime. Whether patients who have multiple replaced joints are at an increased long-term risk of periprosthetic joint infection (PJI) is not known. The purpose of this study was to compare rates of PJI in patients who have more than one primary arthroplasty. METHODS:We reviewed 36,129 patients who underwent primary total joint arthroplasty at a single institution from 2011 to 2024. Patients were categorized as having one to four primary hip or knee arthroplasties. The PJI incidence was compared using Chi-square testing and binary logistic regressions, and multivariate models adjusted for sex, body mass index, diabetes, renal disease, smoking status, and Charlson Comorbidity Index (CCI). Sub-analyses compared patients who had one versus two, three, and four arthroplasties. RESULTS:When comparing patients who had one, two, three, or four primary joint arthroplasties, there was no significant difference in the rates of PJI between groups (P = 0.112). Multivariate analyses showed no statistically significant association between the number of arthroplasties and PJI (adjusted odds ratio (OR) for two, three, and four arthroplasties versus one: 1.34, 95% confidence interval (CI) 1.02 to 1.74, P = 0.083; 1.98, 95% CI 0.77 to 4.12, P = 0.105; 1.57, 95% CI 0.09 to 7.24, P = 0.657, respectively). Sub-analyses comparing one versus three and one versus four arthroplasties showed no significant differences. CONCLUSION/CONCLUSIONS:In this single-institution cohort, additional primary hip or knee arthroplasties did not appear to substantially increase PJI risk. These findings suggest a potential trend that requires confirmation with larger, prospective, multicenter, or registry-based studies. Nevertheless, these results provide preliminary evidence to inform patient counseling and guide future research on the risks of multiple arthroplasties.

BACKGROUND:Obesity is a known risk factor for complications after total hip arthroplasty (THA), with societal guidelines recommending surgical delay for patients above body mass index (BMI) targets. Consequently, patients are motivated to reach BMI targets before the office visit, discussing surgical booking. Our study investigates BMI fluctuations between surgical booking and the surgery date and whether these fluctuations have implications for peri- and postoperative outcomes. METHODS:We retrospectively reviewed our institutional database for elective, primary, unilateral THA from 2015 to 2024 with a minimum 90-day follow-up. The cohort was stratified into three groups by percent BMI change from booking date to THA date: Group 1, decrease in BMI; Group 2, 0 to 5% increase in BMI; and Group 3, > 5% increase in BMI. Baseline demographic factors, as well as peri- and postoperative outcomes, were compared. A multivariate regression analysis evaluated risk factors for interval change in BMI. We reviewed 10,400 THA patients who had an average 62-day delay between booking and surgery dates, where 44.2, 42.2, and 13.6% were allocated to Groups 1, 2, and 3, respectively. RESULTS:Operative time, length of stay, and discharge to a rehab facility were significantly higher in Group 3 compared to the others. There were no significant differences seen in 90-day outcomes, including emergency department (ED) visits, readmissions, or revision rates. Multivariate regression analyses identified that elevated BMI at the time of surgery predicted increased septic revision incidence (OR [odds ratio]: 1.1, P < 0.001). Surgical delay and BMI change between booking and surgery did not influence all-cause and septic revision rates. CONCLUSION/CONCLUSIONS:Our study is the first to evaluate preoperative BMI fluctuations between the booking date and surgery date. We found that most patients (55.8%) gained weight, with 13.6% increasing > 5% BMI. Importantly, these weight changes do not impact short-term complications or revision rates, comforting THA surgeons who see interval weight gain on the day of surgery.

BACKGROUND:Return to sports is a concern for many patients undergoing total hip arthroplasty (THA). As younger, active patients increasingly undergo THA, identifying factors that influence athletic recovery is critical. Although prior work has reported favorable outcomes, large-scale studies comparing return rates across both surgical approaches and sport types remain limited. METHODS:We retrospectively analyzed 1,115 athletically participant THA patients and compared rates of returning to sports up to one year postoperatively. Survey data captured sport type, participation at four time points, frequency, exertion, and perceived recovery. Patients were stratified by surgical approach: posterior (PA) (n = 519), anterior (AA) (n = 556), and lateral (LA) (n = 50). RESULTS:Preoperatively, 45.2% of patients were active in at least one sport versus 43.7% at one year postoperatively; 73.2% of preoperative athletes returned, and 23.3% of those previously inactive took up sports. Return-to-sport rates were 68.1, 77.0, and 81.8% for PA, AA, and LA, respectively (Chi-square = 3.42, P = 0.181). There were no significant differences between AA and PA, and the lateral approach was not statistically comparable due to a smaller sample size. Low-impact sports had significantly higher return rates than high-impact sports (72.4 versus 50.0%; P < 0.001). At survey completion (> one year postoperatively), satisfaction rates were high across all sports, and activity restrictions were reported by about one in five patients, regardless of approach. Sport-specific outcomes showed that most patients maintained or improved performance. CONCLUSION/CONCLUSIONS:Patients undergoing THA can expect high return rates and satisfaction with athletic endeavors. Surgical approach generally does not affect return-to-sport outcomes after THA. Most patients, regardless of approach, resumed or exceeded preoperative activity. In addition to high return rates among previously active patients, many previously inactive patients participated in sports after THA, highlighting the role of THA in enabling sport resumption and participation.

BACKGROUND/UNASSIGNED:Electronic medical record portals enable real-time communication between patients and surgeons after total knee arthroplasty (TKA). This study evaluated the impact of message timing and frequency on postoperative outcomes and patient-reported outcome measures (PROMs). MATERIALS AND METHODS/UNASSIGNED:We retrospectively reviewed 9,353 primary TKAs performed at a single academic institution. Of these, 1,219 patients sent messages within 2 weeks of surgery (early), 507 sent messages between 2 and 8 weeks (late), and 7,627 did not message. RESULTS/UNASSIGNED:< .001). There was no difference in PROMs regardless of message timing, and there was no association between the number of messages sent and perioperative outcomes or PROMs. CONCLUSION/UNASSIGNED:Older age and longer LOS were associated with less patient-initiated contact after TKA. Older patients may be less familiar with digital platforms and less likely to send messages. Early messaging may reflect heightened recognition of postoperative issues, enabling counseling or intervention and reducing readmissions. These findings underscore the importance of preoperative education and equitable access, though long-term effects of messaging warrant further study.

BACKGROUND/UNASSIGNED:Technological advancements in total hip arthroplasty (THA), including robotic-assisted (RA-THA) and navigation-assisted (NA-THA) techniques, aim to improve outcomes. However, impact on recovery timing remains unclear. This study examined whether these technologies reduce the time to reach the minimal clinically important difference (MCID) on the Hip Disability and Osteoarthritis Outcome Score for Joint Replacement compared with conventional THA. METHODS/UNASSIGNED:This retrospective study analyzed osteoarthritic THA patients (01/2020-04/2023) who completed preoperative and postoperative Hip Disability and Osteoarthritis Outcome Score for Joint Replacement questionnaires. The exclusion criteria included bilateral procedures or revision within 1 year. MCID was defined using anchor-based (23 points) and distribution-based thresholds (7.6 points). Multivariable interval-censored accelerated failure time models assessed time to MCID. RESULTS/UNASSIGNED:= .140). CONCLUSIONS/UNASSIGNED:Anchor-based MCID demonstrated comparable recovery times across RA, NA, and conventional THA, suggesting no patient-perceived advantage with technology. Distribution-based thresholds indicated RA-THA achieved faster statistically significant improvement, though the relevance remains uncertain.

INTRODUCTION/BACKGROUND:Social support improves outcomes after total hip and knee arthroplasty (THA/TKA), but the demands on informal caregivers, especially as surgeries transition to outpatient care, are understudied. This study strived to assess caregiver burden, predictors, and implications following joint arthroplasty. METHODS:This prospective cohort study enrolled 185 patient-caregiver dyads undergoing primary THA or TKA for osteoarthritis at a tertiary academic center. Caregivers completed assessments at four weeks postoperatively, including the Caregiver Strain Index (CSI) and Appraisal of Caregiving Scale (ACS), which measures perceived benefit, threat, and stress. Demographic, socioeconomic, and caregiving-related variables were collected. Multivariate linear regression identified factors associated with caregiver strain and experiences. Caregivers had a mean age of 64 years (range, 52.3 to 76.3), and 60% were women. Most (72.4%) were spouses, and 46.5% were retired. RESULTS:The CSI scores showed considerable strain, especially among women caregivers (β = 1.29, P = 0.001), those who had higher daily time commitment postoperatively, and those who had lower preoperative preparedness. Regarding employment status, 7% worked part-time, and 3.2% were homemakers. Among ACS subscales, non-White race (β = 0.31, P = 0.035) and homemaker status (β = 0.65, P = 0.049) were positively associated with perceived benefit, while lower preparedness predicted higher perceived threat. Caregiver age (β = 0.0217, P = 0.004), higher initial social support (β = 0.013, P = 0.001), and preparedness (β = 0.19, P = 0.016) predicted higher stress appraisals. Part-time employment was associated with lower stress (β = -0.47, P = 0.042). CONCLUSION/CONCLUSIONS:Caregiver burden after joint arthroplasty is substantial and influenced by sex, age, race, employment, social support, and preparedness. Targeted interventions addressing these factors are needed to mitigate strain in at-risk caregivers and improve surgical recovery for patients.

INTRODUCTION/BACKGROUND:While the number of absolute and relative contraindications to total joint arthroplasty (TJA) has gradually decreased, active cancer patients have traditionally been challenging surgical candidates. We sought to compare perioperative and two-year postoperative clinical outcomes of patients with and without active cancer. METHODS:Patients over 18 years with active cancer undergoing primary, unilateral TJA from 2017 to 2023 at a single urban academic center were reviewed for a minimum two-year follow-up. Cancer status, type, and stage were confirmed manually. 68 cancer patients were propensity-matched 3:1 to 204 non-cancer patients from a pool of 9,382 based on age, sex, BMI, smoking status, race, and ASA class. Demographic, perioperative, and clinical data were analyzed using t-tests, Chi-square, and ANOVA. Subgroup analyses compared cancer patients receiving active therapy versus those not on treatment. RESULTS:There were no significant demographic differences between groups, except Charlson Comorbidity Index (P < 0.001). The most common cancers were breast (22%) and prostate (20%). There were no differences in discharge disposition (P = 0.20), operative time (P = 0.87), or length of stay (P = 0.29). The all-cause revision rate (including infection) was higher in patients with active cancer (7.4% vs. 2.5%), though not statistically significant (P = 0.15; Power = 46.5%). Of the various causes for revision, infection was significantly more likely in cancer patients than other causes compared (4.4% vs. 0%, P = 0.003). When analyzing only the active cancer group, those receiving cancer therapy had higher revision rates, though this was not statistically significant (11.1% vs. 6.0%, P = 0.487). DISCUSSION/CONCLUSION/CONCLUSIONS:Despite often being excluded from arthroplasty studies, active cancer patients demonstrated comparable overall outcomes after primary TJA. Although infection-related revisions were more common, they were effectively treated. With proper preoperative optimization and multidisciplinary care, TJA can be safely performed in selected active cancer patients.

INTRODUCTION/BACKGROUND:There is increased awareness of socioeconomic disparities among total hip arthroplasty (THA) patients. Most studies contain small sample sizes and few control for confounding variables. This study aims to evaluate postoperative outcomes and survivorship after THA in patients of different races/ethnicities. METHODS:Patients who underwent a primary THA were identified in the New York Statewide Planning and Research Cooperative System (SPARCS) database. Patients were stratified into 4 groups: White, Black, Hispanic, or Asian. Categorical variables and continuous variables were compared, and revision free survival was calculated using Kaplan Meier survival analysis. Multivariate Cox proportional hazard regression was used to calculate revision hazard ratios. RESULTS: = 0.021). There were no significant differences in the cumulative revision event rate up to 10 years postoperatively. CONCLUSIONS:Non-White patients are significantly more likely to have worse short-term outcomes and complications. However, overall rates of revision at 1 year are similar across these groups, with a slightly lower rate of revision in Black patients after controlling for confounding factors.