Background: To investigate the association between American Society of Anesthesiologists (ASA) physical status classification and rates of postoperative complications in patients undergoing facial fracture repair.
Methods: Patients were divided into 2 cohorts based on the ASA classification system: Class I/II and Class III/IV. Chi-square and Fisher's exact tests were used for univariate analyses. Multivariate logistic regressions were used to assess the independent associations of covariates on postoperative complication rates.
Results: A total of 3575 patients who underwent facial fracture repair with known ASA classification were identified. Class III/IV patients had higher rates of deep surgical site infection ( = .012) as well as bleeding, readmission, reoperation, surgical, medical, and overall postoperative complications ( < .001). Multivariate regression analysis found that Class III/IV was significantly associated with increased length of stay ( < .001) and risk of overall complications ( = .032). Specifically, ASA Class III/IV was associated with increased rates of deep surgical site infection ( = .049), postoperative bleeding ( = .036), and failure to wean off ventilator ( = .027).
Conclusions: Higher ASA class is associated with increased length of hospital stay and odds of deep surgical site infection, bleeding, and failure to wean off of ventilator following facial fracture repair. Surgeons should be aware of the increased risk for postoperative complications when performing facial fracture repair in patients with high ASA classification.
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http://dx.doi.org/10.1177/00034894211059599 | DOI Listing |
J Orthop Trauma
December 2024
Department of Orthopaedic Surgery, Regions Hospital, St. Paul, MN.
As the operative management of acute, chest wall, skeletal injury escalates throughout the world, it has become commonplace for patients with posttraumatic conditions to present with clinical reconstructive challenges as well. In addition, it is becoming clear that rib nonunions are not rare, likely more than 5% of rib fractures. No subspecialty is better equipped to address such painful conditions than orthopaedic surgery.
View Article and Find Full Text PDFIntroduction: Patients undergoing hip fracture surgery face notable risks of postoperative morbidity and mortality, and racial and socioeconomic disparities in outcomes exist. This study examined the effect of social vulnerability on outcomes after hip fracture surgery using the CDC's Social Vulnerability Index (SVI).
Methods: A retrospective study of 464 patients undergoing hip fracture surgery at a single institution from July 2020 to June 2023 was conducted.
Oral Maxillofac Surg
January 2025
Coastal Ear, Nose & Throat LLC, Neptune, NJ, USA.
Objective: This systematic review and meta-analysis compares the efficacy and complication rate of absorbable versus non-absorbable 3D-printed, patient-customized, maxillofacial implants in facial trauma patients.
Data Sources: A comprehensive search of four databases (PubMed, Scopus, Web of Science, and Cochrane) was conducted.
Methods: A systematic review and single-proportion meta-analysis was conducted employing PRISMA guidelines.
Cureus
December 2024
Oral Medicine and Radiology, SRM Dental College Ramapuram, SRM Institute of Science and Technology (SRMIST), Chennai, IND.
Facial bone fractures are a common occurrence in trauma cases, particularly in India where road traffic accidents contribute significantly. Over the past few years, artificial intelligence (AI) has become a potent instrument to help medical professionals diagnose and treat facial fractures. This study aims to perform a bibliometric analysis, that is, a quantitative and qualitative analysis, of publications focusing on the role of AI in detecting facial bone fractures.
View Article and Find Full Text PDFBioinformatics
January 2025
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
Motivation: Ensuring connectivity and preventing fractures in tubular object segmentation are critical for downstream analyses. Despite advancements in deep neural networks (DNNs) that have significantly improved tubular object segmentation, existing methods still face limitations. They often rely heavily on precise annotations, hindering their scalability to large-scale unlabeled image datasets.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!