Identifying Aseptic Revision Total Knee Arthroplasty Diagnoses That Achieve Minimal Clinically Important Difference and Patient Acceptable Symptom State.

BACKGROUND: Minimal clinically important difference (MCID) defines a meaningful clinical change in patient-reported outcome measures. Patient acceptable symptom state (PASS) provides a patient-reported outcome measures threshold value to indicate a satisfactory clinical state. MCID and PASS for revision total knee arthroplasty (rTKA) are ill-defined. Moreover, it is unknown whether diagnosis influences the likelihood of achieving MCID or PASS. The purpose of this study was to calculate MCID for aseptic rTKA and compare the percentage of patients achieving MCID and PASS per diagnosis. METHODS: An institutional registry of rTKA was used. First-time aseptic rTKA were included. Demographics, revision diagnosis, preoperative Knee Injury and Osteoarthritis Outcome Score, Jr (KOOS Jr), and 1-year postoperative KOOS Jr were recorded. The 1-year postoperative KOOS Jr PASS score was available. MCID was calculated using distribution-based methods. Three hundred fifty eight first-time aseptic rTKAs were analyzed. The 3 most common diagnoses were aseptic loosening (n = 156), instability (n = 109), and stiffness (n = 37). RESULTS: The mean KOOS Jr 1-year postoperative MCID for rTKA was 10.3. Overall, 75.4% achieved MCID and 56.9% achieved PASS. The percentage of patients per diagnosis achieving MCID and PASS, respectively, were periprosthetic fracture (100, 44), aseptic loosening (94, 60), implant fracture (88, 63), stiffness (60, 38), instability (59, 61), polyethylene wear/osteolysis (57, 57), and metal allergy (44, 33). CONCLUSION: Aseptic rTKA MCID is 10.3 for KOOS Jr at 1 year postoperatively. rTKA outcomes vary depending on preoperative diagnosis. Even in diagnoses with a high proportion of MCID achieved, less than 2/3 of patients achieved PASS, suggesting rTKA provides noticeable improvement but may not return patients to a satisfactory state.

Utilizing ChatGPT to Streamline the Generation of Prior Authorization Letters and Enhance Clerical Workflow in Orthopedic Surgery Practice: A Case Report.

Prior authorization is a cumbersome process that requires clinicians to create an individualized letter that includes detailed information about the patient's medical condition, proposed treatment plan, and any supplemental information required to obtain approval from a patient's insurance company before any services or procedures may be provided to the patient. However, drafting authorization letters is time-consuming clerical work that not only places an increased administrative burden on orthopedic surgeons and office staff but also concurrently takes time away from patient care. Therefore, there is a need to improve this process by streamlining workflows for healthcare providers in order to prioritize direct patient care. In this report, we present a case utilizing OpenAI's ChatGPT (OpenAI, L.L.C., San Francisco, CA, USA) to draft a prior authorization request letter for the use of matrix-induced autologous chondrocyte implantation to treat a cartilage injury of the knee.

Predicting Implant Size in Total Hip Arthroplasty.

Background: Efficient resource management is becoming more important as the demand for total hip arthroplasty (THA) increases. The purpose of this study is to evaluate the ability of linear regression and Bayesian statistics in predicting implant size for THA using patient demographic variables. Material and methods: A retrospective, single-institution joint-replacement registry review was performed on patients who underwent primary THA from 2005 to 2019. Demographic information was obtained along with primary THA implant data. A total of 11,730 acetabular and 8536 femoral components were included. A multivariable regression model was created on a training cohort of 80% of the sample and applied to the validation cohort (remaining 20%). Bayesian posterior probability methods were applied to the training cohort and then tested in the validation cohort to determine the 1%, 5%, and 10% error tolerance thresholds. Results: The most predictive regression model included height, weight, and sex (cup: R2 = 0.57, all P < .001; stem mediolateral size [M/L]: R2 = 0.32, all P < .001). Removing weight had a minimal effect and resulted in a more parsimonious model (cup: R2 = 0.56, all P < .001; stem M/L: R2 = 0.32, all P < .001). Applying the posterior probability estimate to the validation cohort in the Bayesian model using height, weight, and sex demonstrated high accuracy in predicting the range of required implant sizes (95.3% cup and 90.4% stem M/L size). Conclusion: Implant size in THA is correlated with demographic variables to accurately predict implant size using Bayesian modeling. Predictive models such as linear regression and Bayesian modeling can be used to improve operating room efficiency, supply chain inventory management, and decrease costs associated with THA.

History of COVID-19 Was Not Associated With Length of Stay or In-Hospital Complications After Elective Lower Extremity Joint Replacement.

BACKGROUND: The impact of previous SARS-CoV-2 infection on the morbidity of elective total joint arthroplasty (TJA) is not fully understood. This study reports on the association between previous COVID-19 disease, hospital length of stay (LOS), and in-hospital complications after elective primary TJA. METHODS: Demographics, comorbidities, LOS, and in-hospital complications of consecutive 340 patients with a history of COVID-19 were compared with those of 5014 patients without a history of COVID-19 undergoing TJA. History of COVID-19 was defined as a positive IgG antibody test for SARS-CoV-2 before surgery. All patients were given both antibody and polymerase chain reaction tests before surgery. RESULTS: Patients with a history of COVID-19 were more likely to be obese (43.8% vs 32.4%, P < .001), Black (15.6% vs 6.8%, P < .001), or Hispanic (8.5% vs 5.4%, P = .028) than patients without a history of COVID-19. COVID-19 treatment was reported by 6.8% of patients with a history of COVID-19. Patients with a history of COVID-19 did not have a significantly longer median LOS after controlling for other factors (for hip replacements, median 2.9 h longer, 95% confidence interval = -2.0 to 7.8, P = .240; for knee replacements, median 4.1 h longer, 95% confidence interval = -2.4 to 10.5, P = .214), but a higher percentage were discharged to a post-acute care facility (4.7% vs 1.9%, P = .001). There was no significant difference in in-hospital complication rates between the 2 groups (0/340 = 0.0% vs 22/5014 = 0.44%, P = .221). CONCLUSIONS: We do not find differences in LOS or in-hospital complications between the 2 groups. However, more work is needed to confirm these findings, particularly for patients with a history of more severe COVID-19. LEVEL OF EVIDENCE: II.

Trends of Utilization and 90-Day Complication Rates for Computer-Assisted Navigation and Robotic Assistance for Total Knee Arthroplasty in the United States From 2010 to 2018.

BACKGROUND: Computer-assisted navigation (CAN) and robotic assistance (RA) for total knee arthroplasty (TKA) are gaining in popularity. The purpose of this study is to update the literature on United States technology-assisted TKA trends of national utilization, regional utilization, and 90-day complication rates requiring readmission. METHODS: Patients who underwent primary, elective TKA between 2010 and 2018 were retrospectively identified in the PearlDiver All Payer Claims Database (PearlDiver Technologies Inc.). TKAs were classified as conventional, CAN, or RA based on International Classification of Diseases nineth or tenth revision and Current Procedural Technology codes. Annual rates and regional trends of each type of TKA were reported. Ninety-day complications requiring readmission for each group were captured. Multivariable logistic regression was used to identify odds ratios (OR) for all-cause readmission based on TKA modality. RESULTS: Of the 1,307,411 elective TKAs performed from 2010 to 2018, 92.8% were conventional, and 7.7% were technology-assisted (95.2% CAN and 4.9% RA). RA-TKA had the greatest increase in utilization (+2204%). The Western region had the highest utilization of technologies for TKA, while the Midwestern region had the lowest. Ninety-day postoperative complications requiring readmission were highest for conventional TKA and lowest for RA-TKA. RA-TKA (OR 0.68; 97.5% confidence interval 0.56-0.83, P < .001) and CAN-TKA (OR 0.93; 97.5% confidence interval 0.88-0.97, P < .05) had significantly lower odds of all-cause 90-day complications requiring readmission than conventional TKA. CONCLUSION: Utilization of RA-TKA and CAN-TKA continues to rise across the United States. The use of these technologies is associated with a lower OR of readmission within 90 days postoperatively.

Intraoperative Complications and Early Return to the Operating Room in Total Hip Arthroplasty Performed Through the Direct Anterior and Posterior Approaches. An Institutional Experience of Surgeons After Their Learning Curve.

BACKGROUND: Proponents of the direct anterior approach (DAA) for total hip arthroplasty (THA) claim a faster recovery, whereas critics claim an increased risk of early femoral complications. This study analyzed intraoperative and postoperative complications requiring reoperation within one year after THA through the DAA and posterior approach (PA). METHODS: A total of 2348 elective, unilateral DAA THAs in patients with osteoarthritis performed between 2016 and 2019 were matched 1:1 for age (±5 years), gender, body mass index (±5), and femoral fixation with 2348 patients who underwent PA THA during the same period. Mixed-effects logistic regression was used. Odds ratios were reported for the occurrence of intraoperative femoral fracture, postoperative femoral fracture, infection, dislocation, and other etiologies requiring reoperation within one year. RESULTS: Intraoperative femoral fracture occurred in 12 DAA (0.5%) and 14 PA (0.6%) patients. Twenty-five patients (1.06%) in the DAA and 28 (1.19%) in the PA group underwent reoperation within the first year. Reoperations were due to periprosthetic fracture (40%), infection (28%), dislocation (23%), and other (9%). Regression analysis revealed no difference in intraoperative femoral fracture (odds ratio (OR): 0.86, 95% confidence interval (CI): 0.40-1.86, P = .69), postoperative femoral fracture (OR: 1.10, 95% CI: 0.47-2.60, P = .83), infection (OR: 1.50, 95% CI: 0.53-5.23, P = .44), or reoperation within one year for other reasons (OR: 1.50, 95% CI: 0.25-9.00, P = .65). DAA had fewer dislocations requiring reoperation (OR: 0.20, 95% CI: 0.04-0.91, P = .02). CONCLUSION: This comparative study did not find differences in intraoperative or postoperative fracture or infection between DAA and PA. DAA was associated with a lower likelihood of reoperation for dislocation within one year of surgery.