Gupta, Puneet

Background: Hip arthroscopy has seen a significant surge in utilization, but complications remain, and optimal functional outcomes are not guaranteed. Artificial intelligence (AI) has emerged as an effective supportive decision-making tool for surgeons. The purpose of this systematic review was to characterize the outcomes, performance, and validity (generalizability) of AI-based prediction models for hip arthroscopy in current literature. Methods: Two reviewers independently completed structured searches using PubMed/MEDLINE and Embase databases on August 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (hip arthroscopy). Studies that investigated AI-based risk prediction models in hip arthroscopy were included. The primary outcomes of interest were the variable(s) predicted by the models, best model performance achieved (primarily based on area under the curve, but also accuracy, etc), and whether the model(s) had been externally validated (generalizable). Results: Seventy-seven studies were identified from the primary search. Thirteen studies were included in the final analysis. Six studies (n = 6,568) applied AI for predicting the achievement of minimal clinically important difference for various patient-reported outcome measures such as the visual analog scale and the International Hip Outcome Tool 12-Item Questionnaire, with area under a receiver-operating characteristic curve (AUC) values ranging from 0.572 to 0.94. Three studies used AI for predicting repeat hip surgery with AUC values between 0.67 and 0.848. Four studies focused on predicting other risks, such as prolonged postoperative opioid use, with AUC values ranging from 0.71 to 0.76. None of the 13 studies assessed the generalizability of their models through external validation. Conclusion: AI is being investigated for predicting clinical outcomes after hip arthroscopy. However, the performance of AI models varies widely, with AUC values ranging from 0.572 to 0.94. Critically, none of the models have undergone external validation, limiting their clinical applicability. Further research is needed to improve model performance and ensure generalizability before these tools can be reliably integrated into patient care. Level of Evidence: Level IV . See Instructions for Authors for a complete description of levels of evidence.

Introduction Revision total elbow arthroplasty (TEA) is indicated for component loosening, periprosthetic joint infection, and fractures. Previous investigations into revision TEA complications have small sample sizes and limited exploration of acute complications. This study aims to characterize the 30-day complications and associated demographics of patients undergoing revision TEA using a large national database. Methods The 2015–2020 American College of Surgeons National Surgical Quality Improvement Program database was used to identify all revision TEA patients. Results The analysis included 158 patients (average age 62.6 years). The overall complication rate was 13.9% ( n = 22). The most common 30-day complications were organ/space surgical site infection (3.8%, n = 6), unplanned readmission (3.2%, n = 5), intraoperative or postoperative transfusion (3.2%, n = 5), and unplanned reoperation (1.9%, n = 3). Patients with a history of dyspnea or steroid/immunosuppressant use for a chronic condition had significantly higher rates of bleeding requiring transfusion ( p = .029 and p = .021, respectively). Additionally, patients aged 80 years or older had a significantly higher rate of unplanned readmission ( p = .032). Conclusion Revision TEA has a 13.9% rate of complications within 30-days of surgery, with most involving surgical site infection, unplanned readmission, and blood transfusion. Level of Evidence IV.

Background Although arthroscopic procedures are generally considered safer than open procedures, they are not without complications. This study's purpose is to characterize patient demographics, medical complications, overnight admissions, and prolonged operative times for patients undergoing elective elbow arthroscopy using a national database. Methods This retrospective study used the ACS NSQIP database with data from 2015 to 2020. Patients undergoing elbow arthroscopy were identified, and those undergoing emergent surgery were excluded. Patient demographics, clinical characteristics, medical comorbidities, 30-day complications, overnight admission rates, and operative times were recorded and analyzed. Results Overall, 815 patients undergoing elective elbow arthroscopy were included. The mean age was 46.8 years. Mean BMI was 29.7 kg/m 2 , and 75.2% (n = 613) were male sex. The most common comorbidity was smoking (14.8%, n = 121). The cumulative complication rate was 2.5% (n = 20). The most common complication was surgical site infection (n = 7, 0.9%). 10.7% of patients required at least one overnight hospital stay. 20.2% of patients had a prolonged operative time ≥ 120 min. Conclusion Elbow arthroscopy is not without complications and morbidity despite being a minimally invasive procedure and advances made in surgical technique. Surgeons should use this information to facilitate shared-surgical decision making, preoperative patient counselling, and preoperative patient optimization.