Citations

Background: Forehead contouring can be a component of facial feminization surgery (FFS). Its complications have been rarely reported on and are often described as “hypothetical.” Methods: A case report of complications from forehead contouring resulting in frontal osteomyelitis and sinusitis. Results: A female patient presented with frontal osteomyelitis, sinusitis, and forehead contour deformity after a type III forehead contouring surgery. She had failed prior treatment including oral antibiotics, IV antibiotics, revision sinus surgery, and revision nasal surgery. For definitive treatment, she underwent an anterior table resection, sinus obliteration with bony contouring, and pericranial flap. Conclusions: Forehead contouring represents a recent significant advancement in FFS and gender-affirming therapy. Descriptions of complications and their management are important when novel therapies such as FFS are introduced. This case demonstrates complications from type III forehead contouring including osteomyelitis, frontal sinusitis, and forehead deformity.

Abstract Background and Aims Racial and ethnic disparities exist in the treatment of IBD. These disparities exist in adult vaccine uptake among the general population and may extend to patients with IBD. The primary aim of this study was to determine whether racial, ethnic, or geographic disparities existed in influenza vaccine uptake among patients with IBD. Methods We performed a multicenter, retrospective cohort study evaluating adult vaccine uptake among patients with IBD seen at two tertiary referral centers between September 2019 and February 2020. The primary outcome was to determine if racial/ethnic and geographic disparities existed in influenza vaccine uptake for the two prior seasons. Our secondary outcomes were to determine if disparities existed for pneumococcal, zoster, or hepatitis B vaccines. Results Among the 2453 patients who met the inclusion criteria, most identified as non-Hispanic White (89.9%), were on immunosuppressive therapy (74.5%), and received the influenza vaccine in both seasons (56.0%). Older age (prevalence ratio (PR) 0.98; 95% confidence interval (95%CI) 0.98-0.99; P < .001) and non-Hispanic White patients (PR 0.76, 95%CI 0.59–0.98, P < 0.03) were significantly more likely to be immunized. Black patients (PR 1.37; 95%CI 1.18–1.59; P < .001) and those living in underserved geographic areas (PR 1.35; 95%CI 1.17–1.56; P < 0.001) were less likely to be immunized. Racial/ethnic and geographic disparities were identified for pneumococcal, zoster, and hepatitis B vaccine uptake. Conclusions Racial and ethnic vaccination uptake disparities exist among patients with IBD; patients from medically underserved areas are also vulnerable to these disparities Studies identifying patient, provider, and system-level opportunities to address these disparities are needed.

Abstract Objectives Inpatients with language barriers and complex medical needs suffer disparities in quality of care, safety, and health outcomes. Although in-person interpreters are particularly beneficial for these patients, they are underused. We plan to use machine learning predictive analytics to reliably identify patients with language barriers and complex medical needs to prioritize them for in-person interpreters. Materials and methods This qualitative study used stakeholder engagement through semi-structured interviews to understand the perceived risks and benefits of artificial intelligence (AI) in this domain. Stakeholders included clinicians, interpreters, and personnel involved in caring for these patients or for organizing interpreters. Data were coded and analyzed using NVIVO software. Results We completed 49 interviews. Key perceived risks included concerns about transparency, accuracy, redundancy, privacy, perceived stigmatization among patients, alert fatigue, and supply–demand issues. Key perceived benefits included increased awareness of in-person interpreters, improved standard of care and prioritization for interpreter utilization; a streamlined process for accessing interpreters, empowered clinicians, and potential to overcome clinician bias. Discussion This is the first study that elicits stakeholder perspectives on the use of AI with the goal of improved clinical care for patients with language barriers. Perceived benefits and risks related to the use of AI in this domain, overlapped with known hazards and values of AI but some benefits were unique for addressing challenges with providing interpreter services to patients with language barriers. Conclusion Artificial intelligence to identify and prioritize patients for interpreter services has the potential to improve standard of care and address healthcare disparities among patients with language barriers.

Background: Posthepatectomy liver failure (PHLF) represents a life-threatening complication with limited therapeutic options. Neutrophils play a critical and dynamic role during regeneratory processes, but their role in human liver regeneration is incompletely understood, especially as underlying liver disease, detectable in the majority of patients, critically affects hepatic regeneration. Here we explored intrahepatic neutrophil accumulation and neutrophil extracellular traps (NETs) in patients with PHLF and validated the functional relevance of NETs in a murine partial hepatectomy (PHx) model. Methods: We investigated the influx of neutrophils, macrophages, eosinophils, and mast cells and the presence of their respective extracellular traps in liver biopsies of 35 patients undergoing hepatectomy (10 patients with PHLF) before and after the initiation of liver regeneration by fluorescence microscopy. In addition, NET formation and neutrophil activation were confirmed by plasma analysis of 99 patients (24 patients with PHLF) before and up to 5 days after surgery. Furthermore, we inhibited NETs via DNase I in a murine PHx model of mice with metabolically induced liver disease. Results: We detected rapid intrahepatic neutrophil accumulation, elevated levels of myeloperoxidase release, and NET formation in regenerating human livers, with a significantly higher increase of infiltrating neutrophils and NETs in patients with PHLF. Circulating markers of neutrophil activation, including elastase, myeloperoxidase, and citrullinated histone H3, correlated with markers of liver injury. In a murine PHx model, we showed that the inhibition of NET accelerated hepatocyte proliferation and liver regeneration. Conclusions: Patients with PHLF showed accelerated intrahepatic neutrophil infiltration and NET formation, which were associated with liver damage. Further, we identified postsurgical myeloperoxidase levels as predictive markers for adverse outcomes and observed that blocking NETs in a murine PHx model accelerated tissue regeneration.

Although there is consensus on the management of patients with Brugada Syndrome with high risk for sudden cardiac arrest, asymptomatic or intermediate-risk patients present clinical management challenges. This document explores the management opinions of experts throughout the world for patients with Brugada Syndrome who do not fit guideline recommendations. Four real-world clinical scenarios were presented with commentary from small expert groups for each case. All authors voted on case-specific questions to evaluate the level of consensus among the entire group in nuanced diagnostic and management decisions relevant to each case. Points of agreement, points of controversy, and gaps in knowledge are highlighted.

ABSTRACT Crohn's disease is a chronic inflammatory disorder of the gastrointestinal tract, frequently presenting with extraintestinal manifestations. Granulomatosis with polyangiitis is a systemic vasculitis primarily affecting the respiratory tract and kidneys. Extraintestinal Crohn's disease and granulomatosis with polyangiitis may have similar clinical presentations and, in rare occurrences, can coexist. This case report highlights the diagnostic and therapeutic complexities of this uncommon overlap syndrome.

AbstractPurposeTo demonstrate a novel MR elastography (MRE) technique, termed here wavelet MRE. With this technique, broadband motion sensitivity is achievable. Moreover, the true tissue displacement can be reconstructed with a simple inverse transform.MethodsA wavelet MRE sequence was developed with motion‐encoding gradients based on Haar wavelets. From the phase images' displacement was estimated using an inverse transform. Simulations were performed using a frequency sweep and a transient as ground‐truth motions. A PVC phantom was scanned using wavelet MRE and standard MRE with both transient (one and 10 cycles of 90‐Hz motion) and steady‐state dual‐frequency motion (30 and 60 Hz) for comparison. The technique was tested in a human brain, and motion trajectories were estimated for each voxel.ResultsIn simulation, the displacement information estimated from wavelet MRE closely matched the true motion. In the phantom test, the MRE phase data generated from the displacement information derived from wavelet MRE agreed well with standard MRE data. Testing of wavelet MRE to assess transient motion waveforms in the brain was successful, and the tissue motion observed was consistent with a previous study.ConclusionThe uniform and broadband frequency response of wavelet MRE makes it a promising method for imaging transient, multifrequency motion, or motion with unknown frequency content. One potential application is measuring the response of brain tissue undergoing low‐amplitude, transient vibrations as a model for the study of traumatic brain injury.

Background: An increased posterior tibial slope (PTS) corresponds with an increased risk of graft failure after anterior cruciate ligament (ACL) reconstruction (ACLR). Validated methods of manual PTS measurements are subject to potential interobserver variability and can be inefficient on large datasets. Purpose/Hypothesis: To develop a deep learning artificial intelligence technique for automated PTS measurement from standard lateral knee radiographs. It was hypothesized that this deep learning tool would be able to measure the PTS on a high volume of radiographs expeditiously and that these measurements would be similar to previously validated manual measurements. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A deep learning U-Net model was developed on a cohort of 300 postoperative short-leg lateral radiographs from patients who underwent ACLR to segment the tibial shaft, tibial joint surface, and tibial tuberosity. The model was trained via a random split after an 80 to 20 train-validation scheme. Masks for training images were manually segmented, and the model was trained for 400 epochs. An image processing pipeline was then deployed to annotate and measure the PTS using the predicted segmentation masks. Finally, the performance of this combined pipeline was compared with human measurements performed by 2 study personnel using a previously validated manual technique for measuring the PTS on short-leg lateral radiographs on an independent test set consisting of both pre- and postoperative images. Results: The U-Net semantic segmentation model achieved a mean Dice similarity coefficient of 0.885 on the validation cohort. The mean difference between the human-made and computer-vision measurements was 1.92° (σ = 2.81° [ P = .24]). Extreme disagreements between the human and machine measurements, as defined by ≥5° differences, occurred <5% of the time. The model was incorporated into a web-based digital application front-end for demonstration purposes, which can measure a single uploaded image in Portable Network Graphics format in a mean time of 5 seconds. Conclusion: We developed an efficient and reliable deep learning computer vision algorithm to automate the PTS measurement on short-leg lateral knee radiographs. This tool, which demonstrated good agreement with human annotations, represents an effective clinical adjunct for measuring the PTS as part of the preoperative assessment of patients with ACL injuries.