Citations

AbstractBackgroundBetter access to direct‐acting antiviral (DAA) therapy has broadened the utilization of hepatitis C virus (HCV) nucleic acid testing (NAT) positive organs with excellent outcomes. However, DAA therapy has been associated with hepatitis B virus (HBV) reactivation.AimTo determine the risk of HBV transmission or reactivation with utilization of HBV core antibody positive (HBcAb+) and HCV NAT positive (HCV+) organs, which presumably required DAA therapy.MethodsThe number of HBcAb+ donors with delineated HCV NAT status was obtained from the Organ Procurement and Transplantation Network (OPTN) database. The number of unexpected HBV infections from transplanted organs adjudicated as “proven” or “probable” transmission was obtained from the OPTN Ad Hoc Disease Transmission Advisory Committee database. A chart review of the donors of “proven” or “probable” cases was conducted.ResultsFrom January 1, 2016, to December 31, 2021, 7735 organs were procured from 3767 HBcAb+ donors and transplanted into 7469 recipients; 545 (14.5%) donors were also HCV+. HBV transmission or reactivation occurred in seven recipients. The rate is not significantly different between recipients of HCV+ (0.18%, 2/1115) and the HCV NAT negative (HCV‐) organs (0.08%, 5/6354) (p = 0.28) or between recipients of HCV+ and HCV‐ livers as well as non‐liver organs. HBV transmission or reactivation occurred within a median of 319 (range, 41–1117) days post‐transplant in the setting of missing, inadequate, or truncated prophylaxis.ConclusionHBV reactivation associated with DAA therapy for HBcAb+ HCV+ organs is less frequent than reported in the non‐transplant population, possibly due to the common use of HBV prophylaxis in the at‐risk transplant population. image

Purpose To develop a deep learning model for the detection of Segond fractures on anteroposterior (AP) knee radiographs and to compare model performance to that of trained human experts. Methods AP knee radiographs were retrieved from the Hospital for Special Surgery ACL Registry, which enrolled patients between 2009 and 2013. All images corresponded to patients who underwent anterior cruciate ligament reconstruction by 1 of 23 surgeons included in the registry data. Images were categorized into 1 of 2 classes based on radiographic evidence of a Segond fracture and manually annotated. Seventy percent of the images were used to populate the training set, while 20% and 10% were reserved for the validation and test sets, respectively. Images from the test set were used to compare model performance to that of expert human observers, including an orthopaedic surgery sports medicine fellow and a fellowship‐trained orthopaedic sports medicine surgeon with over 10 years of experience. Results A total of 324 AP knee radiographs were retrieved, of which 34 (10.4%) images demonstrated evidence of a Segond fracture. The overall mean average precision (mAP) was 0.985, and this was maintained on the Segond fracture class (mAP = 0.978, precision = 0.844, recall = 1). The model demonstrated 100% accuracy with perfect sensitivity and specificity when applied to the independent testing set and the ability to meet or exceed human sensitivity and specificity in all cases. Compared to an orthopaedic surgery sports medicine fellow, the model required 0.3% of the total time needed to evaluate and classify images in the independent test set. Conclusions A deep learning model was developed and internally validated for Segond fracture detection on AP radiographs and demonstrated perfect accuracy, sensitivity, and specificity on a small test set of radiographs with and without Segond fractures. The model demonstrated superior performance compared with expert human observers. Clinical Relevance Deep learning can be used for automated Segond fracture identification on radiographs, leading to improved diagnosis of easily missed concomitant injuries, including lateral meniscus tears. Automated identification of Segond fractures can also enable large‐scale studies on the incidence and clinical significance of these fractures, which may lead to improved management and outcomes for patients with knee injuries.

Despite the frequent use of prophylactic antibiotics in hospitals for extracorporeal membrane oxygenation (ECMO) patients, the Extracorporeal Life Support Organization (ELSO) Infectious Disease Task Force does not recommend routine antibiotic prophylaxis due to a lack of compelling evidence. We assessed the effectiveness of prophylactic antibiotics in ECMO patients. We conducted a comprehensive search of multiple databases from their inception up to September 6, 2023, on various databases using keywords like “antibiotics,” “prophylaxis,” “extracorporeal membrane oxygenation,” and “ECMO.” Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included research. We collected data using Microsoft Excel version 2016, mean and standard deviations were calculated for continuous data, while frequencies and percentages were calculated for binomial data. A total of three studies was included in the review with a total of 8,954 participants, of which 4,483 (50.06%) received antibiotic prophylaxis, and 1,131 (25.22%) were female. The administration of antibiotics prophylactically was associated with reduction in rate of mortality, the risk of infections, and complications like acute kidney injury and diarrhea. Although there have been some benefits on antibiotic prophylaxis, prospective research, and possibly the creation of tailored, ECMO-specific bundles will be needed to identify efficient ways to prevent ECMO infection.

AbstractDeep learning is a subset of artificial intelligence (AI) with enormous potential to transform orthopaedic surgery. As has already become evident with the deployment of Large Language Models (LLMs) like ChatGPT (OpenAI Inc.), deep learning can rapidly enter clinical and surgical practices. As such, it is imperative that orthopaedic surgeons acquire a deeper understanding of the technical terminology, capabilities and limitations associated with deep learning models. The focus of this series thus far has been providing surgeons with an overview of the steps needed to implement a deep learning‐based pipeline, emphasizing some of the important technical details for surgeons to understand as they encounter, evaluate or lead deep learning projects. However, this series would be remiss without providing practical examples of how deep learning models have begun to be deployed and highlighting the areas where the authors feel deep learning may have the most profound potential. While computer vision applications of deep learning were the focus of Parts I and II, due to the enormous impact that natural language processing (NLP) has had in recent months, NLP‐based deep learning models are also discussed in this final part of the series. In this review, three applications that the authors believe can be impacted the most by deep learning but with which many surgeons may not be familiar are discussed: (1) registry construction, (2) diagnostic AI and (3) data privacy. Deep learning‐based registry construction will be essential for the development of more impactful clinical applications, with diagnostic AI being one of those applications likely to augment clinical decision‐making in the near future. As the applications of deep learning continue to grow, the protection of patient information will become increasingly essential; as such, applications of deep learning to enhance data privacy are likely to become more important than ever before.Level of Evidence: Level IV.

Abstract Background: Mechanical thrombectomy (MT) has revolutionized the treatment of acute ischemic stroke (AIS) due to large vessel occlusion (LVO), but its efficacy and safety in medium vessel occlusion (MeVO) remain less explored. This multicenter, retrospective study aims to investigate the incidence and clinical outcomes of vessel perforations (confirmed by extravasation during an angiographic series) during MT for AIS caused by MeVO. Methods: Data were collected from 37 academic centers across North America, Asia, and Europe between September 2017 and July 2021. A total of 1373 AIS patients with MeVO underwent MT. Baseline characteristics, procedural details, and clinical outcomes were analyzed. Results: The incidence of vessel perforation was 4.8% (66/1373). Notably, our analysis indicates variations in perforation rates across different arterial segments: 8.9% in M3 segments, 4.3% in M2 segments, and 8.3% in A2 segments (p = 0.612). Patients with perforation had significantly worse outcomes, with lower rates of favorable angiographic outcomes (TICI 2c-3: 23% vs 58.9%, p < 0.001; TICI 2b-3: 56.5% vs 88.3%, p < 0.001). Functional outcomes were also worse in the perforation group (mRS 0–1 at 3 months: 22.7% vs 36.6%, p = 0.031; mRS 0–2 at 3 months: 28.8% vs 53.9%, p < 0.001). Mortality was higher in the perforation group (30.3% vs 16.8%, p = 0.008). Conclusion: This study reveals that while the occurrence of vessel perforation in MT for AIS due to MeVO is relatively rare, it is associated with poor functional outcomes and higher mortality. The findings highlight the need for increased caution and specialized training in performing MT for MeVO. Further prospective research is required for risk mitigation strategies.

The incidence and mortality of hepatocellular carcinoma (HCC) in Sub-Saharan Africa is projected to increase sharply by 2040 against a backdrop of limited diagnostic and therapeutic options. Two large South African-based case control studies have developed a serum-based miRNome for Hepatitis B-associated hepatocellular carcinoma (HBV-HCC), as well as identifying their gene targets and pathways. Using a combination of RNA sequencing, differential analysis and filters including a unique molecular index count (UMI) ≥ 10 and log fold change (LFC) range > 2: <−0.5 (p < 0.05), 91 dysregulated miRNAs were characterized including 30 that were upregulated and 61 were downregulated. KEGG analysis, a literature review and other bioinformatic tools identified the targeted genes and HBV-HCC pathways of the top 10 most dysregulated miRNAs. The results, which are based on differentiating miRNA expression of cases versus controls, also develop a serum-based miRNA diagnostic panel that indicates 95.9% sensitivity, 91.0% specificity and a Youden Index of 0.869. In conclusion, the results develop a comprehensive African HBV-HCC miRNome that potentially can contribute to RNA-based diagnostic and therapeutic options.

Background Communication gaps, whether incomplete or fragmented communication, have been the cause of many disasters in human civilisation. Coordination of healthcare is directly related to proper communication and handoffs among multidisciplinary teams throughout multiple shifts during a patient’s hospitalisation. Local problem Patient surveys and direct patient feedback at Mayo Clinic Health System in Mankato, Minnesota, indicated that patient communication with physicians and nurses had declined in 2017 and 2018. Viewing this as an opportunity for improvement, our leadership initiated several changes to increase physician and nurse communication with patients, which resulted in no notable improvements. Methods A systematic quality improvement approach was implemented by using Six Sigma methodology. Stakeholders from multidisciplinary teams were assembled as the project team. The five steps of Six Sigma methodology (Define, Measure, Analyse, Improve and Control) were followed to create a quality improvement intervention. Intervention We developed a standardised and easy-to-use bedside team rounding tool to improve patient communication with physicians and nurses. Results Postintervention patient satisfaction top-box scores exceeded target improvements for both physician (from 78.5% to 82.0%, p<0.01) and nurse (from 80.5% to 83.1%, p=0.04) communication domains. Physicians had a 33-point increase in percentile rank (from 41st to 74th percentile rank), and nurses had a 25-point increase in percentile rank (from 59th to 84th percentile rank). This increase in communication ranked our institution at the top of national benchmark organisations. Conclusions Overwhelmingly positive patient feedback was achieved, and postintervention employee satisfaction was primarily positive when compared with preintervention satisfaction.