Conversion Birmingham Hip Resurfacing in Managing Post-traumatic Arthritis With Retained Femoral Hardware: A Case Report.

CASE: A 70-year-old man with a year-long history of arthritic pain in his left hip presented to our clinic. He had a left intertrochanteric hip fracture 6 years ago, fixed with an open reduction internal fixation with a cephalomedullary nail. He underwent a conversion Birmingham Hip Resurfacing (BHR) with removal of the proximal helicoidal blade and retention of the intramedullary nail. At 7-year follow-up, the patient reported satisfactory clinical outcomes and excellent radiographic fixation. CONCLUSION: This case highlights using conversion BHR in patients with post-traumatic arthritis with retained femoral hardware as an alternative to conventional total hip arthroplasty.

Predictive Modeling of Medical- and Orthopaedic-Related 90-Day Readmissions Following Primary Total Hip Arthroplasty.

BACKGROUND: The rate of unplanned hospital readmissions following total hip arthroplasty (THA) varies from 3 to 10%, representing a major economic burden. However, it is unknown if specific factors are associated with different types of complications (ie, medical or orthopaedic-related) that lead to readmissions. Therefore, this study aimed to: (1) determine the overall, medical-related, and orthopaedic-related 90-day readmission rate; and (2) develop a predictive model for risk factors affecting overall, medical-related, and orthopaedic-related 90-day readmissions following THA. METHODS: A prospective cohort of primary unilateral THAs performed at a large tertiary academic center in the United States from 2016 to 2020 was included (n = 8,893 patients) using a validated institutional data collection system. Orthopaedic-related readmissions were specific complications affecting the prosthesis, joint, and surgical wound. Medical readmissions were due to any other cause requiring medical management. Multivariable logistic regression models were used to investigate associations between prespecified risk factors and 90-day readmissions, as well as medical and orthopaedic-related readmissions independently. RESULTS: Overall, the rate of 90-day readmissions was 5.6%. Medical readmissions (4.2%) were found to be more prevalent than orthopaedic-related readmissions (1.4%). The area under the curve for the 90-day readmission model was 0.71 (95% confidence interval: 0.69 to 0.74). Factors significantly associated with medical-related readmissions were advanced age, Black race, education, Charlson Comorbidity Index, surgical approach, opioid overdose risk score, and nonhome discharge. In contrast, risk factors linked to orthopaedic-related readmissions encompassed body mass index, patient-reported outcome measure phenotype, nonosteoarthritis indication, opioid overdose risk, and nonhome discharge. CONCLUSIONS: Of the overall 90-day readmissions following primary THA, 75% were due to medical-related complications. Our successful predictive model for complication-specific 90-day readmissions highlights how different risk factors may disproportionately influence medical versus orthopaedic-related readmissions, suggesting that patient-specific, tailored preventive measures could reduce postoperative readmissions in the current value-based health care setting.

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.

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.