Citations

Abstract Clinicians struggle to accurately classify biliary strictures as benign or malignant. Current endoscopic retrograde cholangiopancreatography (ERCP)-based sampling modalities including brush cytology and forceps biopsy have poor sensitivity for pathologic confirmation of malignancy. Cholangioscopy allows for direct visualization and sampling of biliary pathology; however, this technology is also associated with inaccurate classification of biliary disease. Previously, an artificial intelligence (AI) system that analyzes cholangioscopy footage was found to be more accurate in diagnosing biliary malignancy than ERCP sampling techniques. The aim of this study was to validate this AI system on a new series of examinations. Three academic centers collected all available unedited cholangioscopy recordings. The videos were processed by the cholangioscopy AI system. After analyzing videos, the AI system provided predictions as to whether malignancy was present. AI performance in classifying strictures was compared with the performance of brush cytology and forceps biopsy. 112 cholangioscopy examinations (containing 4 817 081 images) were generated from 99 patients. Of those examinations, 61 (54.5%) were for investigation of biliary strictures (31 [50.8%] benign, 30 [49.2%] malignant). For the correct classification of strictures, the AI system was 80.0% sensitive and 90.3% specific. It was also significantly more accurate for stricture classification (85.2%) than brush cytology (52.5%; P<0.001), forceps biopsy (68.2%; P=0.04), and the combination of brush cytology and forceps biopsy (66.7%; P=0.02). A previously developed cholangioscopy AI system was found to continually outperform standard ERCP sampling modalities for accurate identification of malignancy, without additional retraining, in a multicenter validation cohort.

Objectives: To compare thresholds and accuracies of FIB-4, vibration-controlled transient elastography (VCTE), point shear wave elastography (pSWE), 2D shear wave elastography (2D-SWE), and MR elastography (MRE) for detecting hepatic fibrosis in patients with MASLD. Materials and Methods: Systematic searching of MEDLINE, EMBASE, Cochrane Library, Scopus, and the gray literature from inception to March 2024 was performed. Studies evaluating accuracies of FIB-4, VCTE, 2D-SWE, pSWE, and/or MRE for detecting significant (≥F2) and/or advanced (≥F3) hepatic fibrosis in MASLD patients compared to histology were identified. Full-text review and data extraction were performed independently by two reviewers. Multivariate meta-analysis and subgroup analyses were performed using index test and fibrosis grading. Risk of bias was assessed using QUADAS-2. Results: 207 studies with over 80,000 patient investigations were included. FIB-4 1.3 threshold sensitivity was 71% (95% CI 66–75%) for detecting advanced hepatic fibrosis, which improved to 88% (85–91%) using a <0.75 threshold. FIB-4 specificity using a 2.67 threshold was 96% (94–97%). Sensitivities of 88–91% were achieved using thresholds of 3.2 kPa for pSWE, 4.92 kPa for 2D-SWE, 7.18 kPa for VCTE, and 2.32 kPa for MRE. No significant differences were identified for sensitivities in subgroup analysis with thresholds between 7 and 9 kPa. Most imaging-based studies were high risk of bias for the index test. Conclusions: A FIB-4 threshold of <0.75 and modality-dependent thresholds (VCTE < 7 kPa; pSWE <3 kPa; 2D-SWE <5 kPa; and MRE <2.5 kPa) would achieve sensitivities of around 90% when defining low-risk MASLD in population screening. A modified two-tier algorithm aligning with existing Society of Radiologists in Ultrasound guidelines would improve risk stratification accuracies compared to existing guidelines by European and American liver societies.

Objective Motor neuron disease (MND) is a progressive neurological disorder with no known cure that damages motor neurons. The purpose of this analysis is to examine the place of death for MND patients in the United States during the coronavirus disease 2019 (COVID-19) pandemic and to investigate the extent of specific comorbidities. Methods We obtained death certificate and associated comorbidities data for all U.S. MND deaths from 2018 to 2021 and conducted a population-based cross-sectional analysis of the deaths pre-COVID-19 (2018-2019) and during COVID-19 (2020-2021). We hypothesized that place of death and comorbidities associated with place of death for MND patients in the United States were altered during the COVID-19 pandemic in comparison to the 2 years period before the pandemic. Results We analyzed 30 066 MND deaths (14 562 pre-COVID-19 and 15 504 during COVID-19) aged 20 years and older. During COVID-19, MND deaths at home increased (54.4% vs 45.5% pre-COVID). Hispanic individuals had an increased likelihood of dying at home compared to a nursing home or hospice (OR = 1.57, 95%CI: 1.22-2.02), but a decreased likelihood compared to a hospital (OR = 0.61, 95% CI: 0.51-0.72). Among the top comorbidities listed, there was a 27.8% increase in diabetes mellitus and a 20.2% increase in essential hypertension during COVID-19. During COVID-19, diabetes mellitus was more commonly reported as a comorbidity for deaths occurring in hospitals (OR = 1.40, 95%CI: 1.03-1.89) or at home (OR = 1.26, 95%CI: 1.03-1.55), while essential hypertension was more commonly reported with deaths at home (OR = 1.17, 95%CI: 1.01-1.36). Conclusion Our analysis showed an increase in at-home MND deaths as well as certain comorbidities during the COVID-19 pandemic, suggesting MND patients had a higher likelihood of death from non-COVID-19 comorbidities.

ImportanceThere exists substantial heterogeneity in outcomes within T stages for patients with cutaneous squamous cell carcinoma (cSCC).ObjectiveTo determine whether a customized generative pretrained transformer model, trained on a comprehensive dataset with more than 1 trillion parameters and equipped with relevant focused context and retrieval augmented generation (RAG), could excel in aggregating and interpreting vast quantities of data to develop a novel class-based risk stratification system that outperforms the current standards.Design, Setting, and ParticipantsTo build the RAG knowledge base, a systematic review of the literature was conducted that addressed risk factors for poor outcomes in cSCC. Using the RAG-enabled generative pretrained transformer (GPT) model, we developed a novel class-based risk stratification system that assigned point values for risk factors, culminating in a GPT-based prognostication system called the artificial intelligence–derived risk score (AIRIS). The system’s performance was validated on a combined prospective and retrospective cohort of 2379 primary cSCC tumors (1996-2023) with at least 36 months of follow-up, against Brigham and Women’s Hospital (BWH) and American Joint Committee on Cancer Staging Manual, eighth edition (AJCC8) systems in stratifying risk for locoregional recurrence (LR), nodal metastasis (NM), distant metastasis (DM), and disease-specific death (DSD).Main Outcomes and MeasuresPerformance metrics evaluated included distinctiveness, homogeneity, and monotonicity, as defined by the AJCC8, as well as sensitivity, specificity, positive predictive value, negative predictive value, accuracy, the area under the receiver operating characteristic curve, and concordance.ResultsThe median age at diagnosis was 73 (IQR, 64-81) years, with 38.5% female patients and 61.5% male patients. The AIRIS prognostication system demonstrated superior sensitivity across all outcomes (LR, 49.1%; NM, 73.7%; DM, 82.5%; and DSD, 72.2%) and the highest area under the receiver operating characteristic curve values (LR, 0.69; NM, 0.81; DM, 0.85; and DSD, 0.80), indicating significantly enhanced discriminative capability compared with the BWH and AJCC8 systems. While all systems were comparably distinctive, the AIRIS prognostication system consistently demonstrated the lowest proportion of tumors exhibiting poor outcomes in low-risk categories, suggesting its improved homogeneity and monotonicity.Conclusions and RelevanceThe results of this diagnostic study suggest that the AIRIS system outperforms the existing BWH and AJCC8 prognostication systems, potentially providing a more effective tool for predicting poor outcomes in cSCC. This study illustrates the potential of large language models in refining prognostic tools, offering implications for treating patients with cancer.

Parkinson’s disease (PD) is the second-most common neurodegenerative disease. With more than 20,000 new diagnosed cases each year, PD affects millions of individuals worldwide and is most prevalent in the elderly population. The current clinical methods for the diagnosis and severity assessment of PD rely on the visual and physical examination of subjects and identifying key disease motor signs and symptoms such as bradykinesia, rigidity, tremor, and postural instability. In the present study, we developed a method for the diagnosis and severity assessment of PD using Gramian Angular Fields (GAFs) in combination with deep Convolutional Neural Networks (CNNs). Our model was applied to PD gait signals captured using pressure sensors embedded into insoles. Our results indicated an accuracy of 98.6%, a true positive rate (TPR) of 99.2%, and a true negative rate (TNR) of 98.5%, showcasing superior classification performance for PD diagnosis compared to the methods used in recent studies in the literature. The estimation of disease severity scores using gait signals showed a high accuracy for the Hoehn and Yahr score as well as the Timed Up and Go (TUG) test score (R2 > 0.8), while we achieved a lower prediction performance for the Unified Parkinson’s Disease Rating Scale (UPDRS) and its motor component (UPDRSM) scores (R2 < 0.2). These results were achieved using gait signals recorded in time windows as small as 10 s, which may pave the way for shorter, more accessible assessment tools for diagnosis and severity assessment of PD.

Patients receiving opioid analgesics may experience constipation [ie, opioid-induced constipation (OIC)], require treatment to induce laxation, and may be at risk for gastrointestinal perforation, an uncommon but potentially life-threatening condition. Management of OIC includes treatment with over-the-counter laxatives and peripherally acting μ-opioid receptor antagonists (PAMORAs; methylnaltrexone, naloxegol, naldemedine). In patients receiving treatment for OIC, gastrointestinal perforation may result from the laxation process, causing disruption of the gastrointestinal lining that may already have compromised integrity. A PubMed literature review and a search of the US Food and Drug Administration Adverse Event Reporting System database identified several cases of gastrointestinal perforation (life-threatening or with mortality) across the range of agents administered for the treatment of OIC or other constipation types. Methylnaltrexone in the subcutaneous form was the first PAMORA approved for OIC. Its real-world use in the ∼6 years before the availability of another OIC-indicated PAMORA helped establish the adverse-event profile of the class, and experience has been gained in identifying and treating appropriate patient populations. Class labeling of PAMORAs includes a contraindication in patients with known or suspected gastrointestinal obstruction or increased risk of recurrent obstruction. Appropriate patient selection during laxation therapy for OIC, regardless of treatment plan, involves consideration of the overall risk versus benefit in patients at increased risk of perforation due to comorbid medical conditions, concurrent medications, or recent gastrointestinal procedures. After initiating treatment for OIC, clinicians should assess the effectiveness of laxation therapy and carefully monitor for signs of gastrointestinal perforation.

ABSTRACT Background Obesity is an important risk factor associated with atrial fibrillation (AF). The impact of glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) on recurrent AF among patients undergoing catheter ablation is not well described. Objective This study aimed to evaluate the effect of GLP‐1 RA on the recurrence of AF among obese patients after catheter ablation. Methods Utilizing the TriNetX research network, we identified patients aged over 18 with obesity (BMI ≥ 30) who underwent AF ablation from January 1, 2015, to December 1, 2022, using Current Procedural Terminology codes. Patients were categorized based on their baseline GLP‐1 RA use. Through propensity‐score matching, each cohort comprised 1558 patients. The primary outcome encompassed a composite of cardioversion, initiation of new antiarrhythmic drug (AAD) therapy, or redo AF ablation. Additional outcomes during the 12‐month follow‐up included AF readmission, heart failure readmissions, readmissions due to ischemic stroke, and mortality, with a separate analysis done for patients with BMI > 40. Results GLP‐1 RA use in patients with obesity undergoing AF ablation was linked to a significantly reduced risk of cardioversion, new AAD therapy, and the need for redo AF ablation (hazard ratio [HR] 0.72 [0.65–0.80]; p < 0.001). At 12 months, individuals on GLP‐1 agonists exhibited a lower likelihood of mortality (HR 0.61 [0.40–0.93]; p < 0.001). Although all other secondary outcomes were lower in the GLP‐1 RA group, there was no significant difference in ischemic stroke between the groups. These results were consistent among patients with BMI > 40, although apart from ischemic stroke, there was no difference in mortality between the two groups. Conclusion The utilization of GLP‐1 RA in individuals with obesity is linked to a decreased likelihood of arrhythmia recurrence following AF ablation, leading to a reduced requirement for cardioversion, AAD therapy, or redo AF ablation.

Background Artificial intelligence (AI) and large language models (LLMs), are potential tools for enhancing healthcare delivery and clinical research. Presently, there is a scarcity of research regarding the viewpoints of medical students toward LLMs. This study aims to explore the perceptions of LLMs and their applications among this demographic. Methods A cross-sectional study was done using an online survey. It was designed using Google Forms and circulated from July 2023 to August 2023. The target population included medical students from Ecuador, Ethiopia, India, Mauritius, Pakistan, United Arab Emirates (UAE), and the United States (USA). Results A total of 1180 responses were collected from 10 medical colleges across 7 countries. The UAE had the largest number of responses (31.7%), followed by Ecuador (17.6%), and Mauritius (13.7%). And 77.4% respondents were already aware about LLMs before attempting the survey, most popularly, ChatGPT. Participants were most familiar with the specific use of LLMs in research (46%), and the lowest familiarity was seen in the use of LLMs in healthcare (37%). More than half of the participants (52%) support the use of LLMs healthcare, yet a significant number of them remained neutral (37%) or disagreed (31%) on LLMs being safe in this context. Conclusion While most of the medical students are aware of LLMs and their applications, the multinational survey demonstrated hesitancy among medical students in fully adopting LLMs into healthcare practice and clinical research. This study prompts further exploration of medical students’ attitudes and acceptance concerning the integration of LLMs for research methodologies, ethical considerations, and healthcare practice.