Dermatomyositis-like skin eruptions under hydroxyurea therapy conceal TP53-mutated atypical keratinocytes: A histopathologic and molecular pathologic case series.

Hydroxyurea is an antimetabolite that inhibits DNA synthesis and is used as a treatment option in chronic myeloproliferative disorders. Rarely, "dermatomyositis (DM)-like" skin lesions are observed after long-term therapy. In this case series, five skin biopsies of four patients were evaluated by histology, immunohistochemistry, and next-generation sequencing of the TP53 gene locus. All biopsies showed focal basal pleomorphic keratinocytes and suprabasal aberrant p53 expression as well as sparse to severe vacuolar interface dermatitis. Histopathologically, "DM-like" skin lesions can be clearly distinguished from DM by marked subepidermal fibrosis, vascular proliferation, and the absence of dermal mucin deposits. In 75% of the specimens multiple, partly inactivating and/or pathogenic point mutations of TP53 were found in low frequencies. "DM-like" skin eruptions as a long-term consequence of hydroxyurea therapy are possibly not chemotherapy-associated benign toxic changes, but rather inflammatory reactions to complex keratinocyte alterations that clinically mimic the picture of DM. Synergistic mutagenic effects of hydroxyurea and sunlight might be responsible for this unique drug side effect and could provide a pathogenic link to the known increased risk of skin cancer in these patients.

The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.

BACKGROUND: Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. OBJECTIVE: Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. METHODS: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. RESULTS: A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. CONCLUSIONS: LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

Real-Time Electronic Patient Portal Use Among Emergency Department Patients.

Importance: Patients with inequitable access to patient portals frequently present to emergency departments (EDs) for care. Little is known about portal use patterns among ED patients. Objectives: To describe real-time patient portal usage trends among ED patients and compare demographic and clinical characteristics between portal users and nonusers. Design, Setting, and Participants: In this cross-sectional study of 12 teaching and 24 academic-affiliated EDs from 8 health systems in California, Connecticut, Massachusetts, Ohio, Tennessee, Texas, and Washington, patient portal access and usage data were evaluated for all ED patients 18 years or older between April 5, 2021, and April 4, 2022. Exposure: Use of the patient portal during ED visit. Main Outcomes and Measures: The primary outcomes were the weekly proportions of ED patients who logged into the portal, viewed test results, and viewed clinical notes in real time. Pooled random-effects models were used to evaluate temporal trends and demographic and clinical characteristics associated with real-time portal use. Results: The study included 1 280 924 unique patient encounters (53.5% female; 0.6% American Indian or Alaska Native, 3.7% Asian, 18.0% Black, 10.7% Hispanic, 0.4% Native Hawaiian or Pacific Islander, 66.5% White, 10.0% other race, and 4.0% with missing race or ethnicity; 91.2% English-speaking patients; mean [SD] age, 51.9 [19.2] years). During the study, 17.4% of patients logged into the portal while in the ED, whereas 14.1% viewed test results and 2.5% viewed clinical notes. The odds of accessing the portal (odds ratio [OR], 1.36; 95% CI, 1.19-1.56), viewing test results (OR, 1.63; 95% CI, 1.30-2.04), and viewing clinical notes (OR, 1.60; 95% CI, 1.19-2.15) were higher at the end of the study vs the beginning. Patients with active portal accounts at ED arrival had a higher odds of logging into the portal (OR, 17.73; 95% CI, 9.37-33.56), viewing test results (OR, 18.50; 95% CI, 9.62-35.57), and viewing clinical notes (OR, 18.40; 95% CI, 10.31-32.86). Patients who were male, Black, or without commercial insurance had lower odds of logging into the portal, viewing results, and viewing clinical notes. Conclusions and Relevance: These findings suggest that real-time patient portal use during ED encounters has increased over time, but disparities exist in portal access that mirror trends in portal usage more generally. Given emergency medicine's role in caring for medically underserved patients, there are opportunities for EDs to enroll and train patients in using patient portals to promote engagement during and after their visits.

Interhemispheric Vertical Hemispherotomy: Technique, Outcome, and Pitfalls-A Bicentric Retrospective Case Series of 39 Cases.

BACKGROUND AND OBJECTIVES: When seizure onset affects a whole hemisphere, hemispheric disconnections are efficient and safe procedures. However, both lateral peri-insular hemispherotomy and vertical paramedian hemispherotomy approaches report a failure rate around 20%, which can be explained by residual connections giving rise to persistent seizures. In this study, we present the interhemispheric vertical hemispherotomy (IVH), a technical variation of the vertical paramedian hemispherotomy approach, that aims to increase seizure control avoiding residual connections while exposing the corpus callosum. METHODS: This is a retrospective study of IVH in two centers, with analysis of clinical and MRI data and outcomes. A detailed description of the technique is provided with a video. RESULTS: IVH was performed in 39 children. The mean age at surgery was 7.2 years, and etiologies were as follows: malformations of cortical development (n = 14), Rasmussen's encephalitis (n = 10), stroke (n = 10), post-traumatic (3), and Sturge-Weber Syndrome (2). Hemispheric disconnection was complete on postoperative MRI in 34 cases. There was no mortality, hydrocephalus occurred in one case, and subdural collection occurred in four cases. A second surgery was performed in four cases because of seizure relapse (n = 3) and/or incomplete disconnection on MRI (n = 4). With a mean follow-up of 3.2 years, International League Against Epilepsy class I epilepsy outcome was obtained for 37/39 patients. CONCLUSION: IVH is a safe and effective variation of the vertical approaches for hemispheric disconnection. It allows a good exposure and anatomic control of the corpus callosum, which is a frequent site of incomplete disconnection. IVH may be limited by the thalamic volume and the ventricular size, notably in hemimegalencephaly cases.

A Conference (Missingness in Action) to Address Missingness in Data and AI in Health Care: Qualitative Thematic Analysis.

BACKGROUND: Missingness in health care data poses significant challenges in the development and implementation of artificial intelligence (AI) and machine learning solutions. Identifying and addressing these challenges is critical to ensuring the continued growth and accuracy of these models as well as their equitable and effective use in health care settings. OBJECTIVE: This study aims to explore the challenges, opportunities, and potential solutions related to missingness in health care data for AI applications through the conduct of a digital conference and thematic analysis of conference proceedings. METHODS: A digital conference was held in September 2022, attracting 861 registered participants, with 164 (19%) attending the live event. The conference featured presentations and panel discussions by experts in AI, machine learning, and health care. Transcripts of the event were analyzed using the stepwise framework of Braun and Clark to identify key themes related to missingness in health care data. RESULTS: Three principal themes-data quality and bias, human input in model development, and trust and privacy-emerged from the analysis. Topics included the accuracy of predictive models, lack of inclusion of underrepresented communities, partnership with physicians and other populations, challenges with sensitive health care data, and fostering trust with patients and the health care community. CONCLUSIONS: Addressing the challenges of data quality, human input, and trust is vital when devising and using machine learning algorithms in health care. Recommendations include expanding data collection efforts to reduce gaps and biases, involving medical professionals in the development and implementation of AI models, and developing clear ethical guidelines to safeguard patient privacy. Further research and ongoing discussions are needed to ensure these conclusions remain relevant as health care and AI continue to evolve.

Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review.

BACKGROUND: Generative artificial intelligence (AI) technologies are increasingly being utilized across various fields, with considerable interest and concern regarding their potential application in medical education. These technologies, such as Chat GPT and Bard, can generate new content and have a wide range of possible applications. OBJECTIVE: This study aimed to synthesize the potential opportunities and limitations of generative AI in medical education. It sought to identify prevalent themes within recent literature regarding potential applications and challenges of generative AI in medical education and use these to guide future areas for exploration. METHODS: We conducted a scoping review, following the framework by Arksey and O'Malley, of English language articles published from 2022 onward that discussed generative AI in the context of medical education. A literature search was performed using PubMed, Web of Science, and Google Scholar databases. We screened articles for inclusion, extracted data from relevant studies, and completed a quantitative and qualitative synthesis of the data. RESULTS: Thematic analysis revealed diverse potential applications for generative AI in medical education, including self-directed learning, simulation scenarios, and writing assistance. However, the literature also highlighted significant challenges, such as issues with academic integrity, data accuracy, and potential detriments to learning. Based on these themes and the current state of the literature, we propose the following 3 key areas for investigation: developing learners' skills to evaluate AI critically, rethinking assessment methodology, and studying human-AI interactions. CONCLUSIONS: The integration of generative AI in medical education presents exciting opportunities, alongside considerable challenges. There is a need to develop new skills and competencies related to AI as well as thoughtful, nuanced approaches to examine the growing use of generative AI in medical education.

Characterization of genetic variants in the EGLN1/PHD2 gene identified in a European collection of patients with erythrocytosis.

Hereditary erythrocytosis is a rare hematologic disorder characterized by an excess of red blood cell production. Here we describe a European collaborative study involving a collection of 2,160 patients with erythrocytosis sequenced in ten different laboratories. We focused our study on the EGLN1 gene and identified 39 germline missense variants including one gene deletion in 47 probands. EGLN1 encodes the PHD2 prolyl 4-hydroxylase, a major inhibitor of hypoxia-inducible factor. We performed a comprehensive study to evaluate the causal role of the identified PHD2 variants: (i) in silico studies of localization, conservation, and deleterious effects; (ii) analysis of hematologic parameters of carriers identified in the UK Biobank; (iii) functional studies of the protein activity and stability; and (iv) a comprehensive study of PHD2 splicing. Altogether, these studies allowed the classification of 16 pathogenic or likely pathogenic mutants in a total of 48 patients and relatives. The in silico studies extended to the variants described in the literature showed that a minority of PHD2 variants can be classified as pathogenic (36/96), without any differences from the variants of unknown significance regarding the severity of the developed disease (hematologic parameters and complications). Here, we demonstrated the great value of federating laboratories working on such rare disorders in order to implement the criteria required for genetic classification, a strategy that should be extended to all hereditary hematologic diseases.

Case report: Schnitzler-like syndrome without monoclonal gammopathy.

Schnitzler syndrome is a rare autoinflammatory disorder characterized by urticarial rash, joint pain, recurrent fever, leucocytosis, elevated C-reactive protein (CRP) and serum amyloid A (SAA), and monoclonal IgM or IgG gammopathy. According to the Strasbourg criteria, both urticarial rash and gammopathy are mandatorily required for the diagnosis of Schnitzler's syndrome. However, incomplete variants lacking either skin symptoms or monoclonal gammopathy have also been described. Here, we report a case in which the diagnosis of Schnitzler-like syndrome was made despite the absence of gammopathy, based on neutrophilic dermal inflammation, episodic and excessive increase in inflammatory parameters, and prompt response to anakinra, a soluble IL1 receptor antagonist (sIL-1RA). In addition, we detected neutrophil epitheliotropism, which is highly suggestive of autoinflammatory disease. Using whole-exome sequencing, we were unable to find a causative pathogenic mutation but did find several mutations possibly related to the inflammatory processes in this patient. This and other cases highlight that the existing Strasbourg criteria are too strict to capture Schnitzler-like syndromes that may respond well and rapidly to IL1 inhibition. Recurrent episodes of disease with normalization of inflammatory symptoms in the interval, rapid response to anakinra, and neutrophilic epitheliotropism in a lesional skin biopsy may help confirm the diagnosis of Schnitzler-like syndrome.

Assessment of Puberty and Hypothalamic-Pituitary-Gonadal Axis Function After Childhood Brain Tumor Treatment.

CONTEXT: Endocrine complications are common in pediatric brain tumor patients. OBJECTIVE: To describe hypothalamic-pituitary-gonadal axis (HPGA) function in patients treated in childhood for a primary brain tumor more than 5 years earlier, in order to identify risk factors for HPGA impairment. METHODS: We retrospectively included 204 patients diagnosed with a primary brain tumor before 18 years of age and monitored at the pediatric endocrinology unit of the Necker Enfants-Malades University Hospital (Paris, France) between January 2010 and December 2015. Patients with pituitary adenoma or untreated glioma were excluded. RESULTS: Among patients with suprasellar glioma not treated by radiotherapy, the prevalence of advanced puberty was 65% overall and 70% when the diagnosis occurred before 5 years of age. Medulloblastoma chemotherapy caused gonadal toxicity in 70% of all patients and in 87.5% of those younger than 5 years at diagnosis. In the group with craniopharyngioma, 70% of patients had hypogonadotropic hypogonadism, which was consistently accompanied by growth hormone deficiency. CONCLUSION: Tumor type, location, and treatment were the risk main factors for HPGA impairment. Awareness that onset can be delayed is essential to guide information of parents and patients, patient monitoring, and timely hormone replacement therapy.

Recurrence of a Cellular Blue Nevus with Satellitosis-A Diagnostic Pitfall with Clinical Consequences.

Blue nevus is a benign melanocytic lesion, typically asymptomatic and of unknown etiology. Several histologic and clinical variants have been distinguished, the most frequent being common blue nevus, cellular blue nevus, and combined blue nevus. Although melanocytic nevi with a satellite lesion are usually suggestive of locally advanced malignant melanoma, very few cases of blue nevi with satellite lesions have been reported. The diagnosis of common or cellular blue nevi is generally straightforward; however, the presence of structures such as irregular edges or satellitosis are highly suggestive for malignancy, and differential diagnoses such as locally advanced malignant melanoma and malignant blue nevus should be considered. Recurrent blue nevi can display atypical features not seen in the primary lesion, such as pleomorphism and mitotic activity. They usually tend to follow a benign course; however, in some cases, recurrence may indicate malignant transformation. We here report the unique case of a 64-year-old woman with a recurrent cellular blue nevus accompanied by satellite lesions. Such a biological behavior resulting in a clinical presentation as a melanoma-like lesion is a rarity in blue nevus and has not been described before.

Team is brain: leveraging EHR audit log data for new insights into acute care processes.

OBJECTIVE: To determine whether novel measures of contextual factors from multi-site electronic health record (EHR) audit log data can explain variation in clinical process outcomes. MATERIALS AND METHODS: We selected one widely-used process outcome: emergency department (ED)-based team time to deliver tissue plasminogen activator (tPA) to patients with acute ischemic stroke (AIS). We evaluated Epic audit log data (that tracks EHR user-interactions) for 3052 AIS patients aged 18+ who received tPA after presenting to an ED at three Northern California health systems (Stanford Health Care, UCSF Health, and Kaiser Permanente Northern California). Our primary outcome was door-to-needle time (DNT) and we assessed bivariate and multivariate relationships with six audit log-derived measures of treatment team busyness and prior team experience. RESULTS: Prior team experience was consistently associated with shorter DNT; teams with greater prior experience specifically on AIS cases had shorter DNT (minutes) across all sites: (Site 1: -94.73, 95% CI: -129.53 to 59.92; Site 2: -80.93, 95% CI: -130.43 to 31.43; Site 3: -42.95, 95% CI: -62.73 to 23.17). Teams with greater prior experience across all types of cases also had shorter DNT at two sites: (Site 1: -6.96, 95% CI: -14.56 to 0.65; Site 2: -19.16, 95% CI: -36.15 to 2.16; Site 3: -11.07, 95% CI: -17.39 to 4.74). Team busyness was not consistently associated with DNT across study sites. CONCLUSIONS: EHR audit log data offers a novel, scalable approach to measure key contextual factors relevant to clinical process outcomes across multiple sites. Audit log-based measures of team experience were associated with better process outcomes for AIS care, suggesting opportunities to study underlying mechanisms and improve care through deliberate training, team-building, and scheduling to maximize team experience.

Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images.

BACKGROUND: Onychomycosis numbers among the most common fungal infections in humans affecting finger- or toenails. Histology remains a frequently applied screening technique to diagnose onychomycosis. Screening slides for fungal elements can be time-consuming for pathologists, and sensitivity in cases with low amounts of fungi remains a concern. Convolutional neural networks (CNNs) have revolutionized image classification in recent years. The goal of our project was to evaluate if a U-NET-based segmentation approach as a subcategory of CNNs can be applied to detect fungal elements on digitized histologic sections of human nail specimens and to compare it with the performance of 11 board-certified dermatopathologists. METHODS: In total, 664 corresponding H&E- and PAS-stained histologic whole-slide images (WSIs) of human nail plates from four different laboratories were digitized. Histologic structures were manually annotated. A U-NET image segmentation model was trained for binary segmentation on the dataset generated by annotated slides. RESULTS: The U-NET algorithm detected 90.5% of WSIs with fungi, demonstrating a comparable sensitivity with that of the 11 board-certified dermatopathologists (sensitivity of 89.2%). CONCLUSIONS: Our results demonstrate that machine-learning-based algorithms applied to real-world clinical cases can produce comparable sensitivities to human pathologists. Our established U-NET may be used as a supportive diagnostic tool to preselect possible slides with fungal elements. Slides where fungal elements are indicated by our U-NET should be reevaluated by the pathologist to confirm or refute the diagnosis of onychomycosis.