Citations

Pediatric delirium (PD) is an acute neuropsychiatric syndrome marked by fluctuating disturbances in attention and cognition, frequently observed in pediatric intensive care units (PICUs) and associated with increased morbidity, mortality, and long-term cognitive impairment. Despite its clinical significance, PD remains underdiagnosed due to challenges inherent in assessing consciousness and cognition in children at varying developmental stages. Several bedside tools have been developed and validated in recent years, including the Cornell Assessment of Pediatric Delirium (CAPD), PreSchool Confusion Assessment Method for the Intensive Care Unit (psCAM-ICU); Pediatric Confusion Assessment Method for the Intensive Care Unit (pCAM-ICU), and Sophia Observation Withdrawal Symptoms—Pediatric Delirium Scale (SOS-PD), enhancing early recognition and management of PD in critically ill children. This narrative review explores the historical background, epidemiology, risk factors, pathophysiology, clinical subtypes, diagnostic tools, and current prevention and treatment strategies for PD from newborns to 21 years old. The screening tools available and the integration of non-pharmacological interventions, such as environmental modifications and family-centered care, as well as cautious and selective pharmacological management, are emphasized in this review. Early identification and targeted interventions are essential to mitigate the adverse outcomes associated with PD.

This analysis provides a novel assessment of the relationship between the severity of right ventricular dysfunction (RVD) and blood volume expansion (BVE) on clinical outcomes in patients with chronic left-sided heart failure. Findings demonstrate that despite the high prevalence of moderate or greater RVD, persistent BVE was associated with better 1-year event-free survival relative to normal BV. Hypothesis-generating, this suggests that BVE may provide an adaptive response compensating in part for the impact of RVD in chronic HF.

Background: Leprosy, caused by Mycobacterium leprae, presents on a spectrum ranging from tuberculoid to lepromatous disease. Borderline lepromatous leprosy represents an unstable immunological state that predisposes patients to immune-mediated reactions, including erythema nodosum leprosum (ENL), a severe inflammatory complication. Case Presentation: We report a case of a 62-year-old female with borderline lepromatous leprosy who presented with recurrent facial cellulitis and later developed disseminated ENL. She was initially diagnosed following a series of facial infections and confirmatory skin biopsy. Months later, she developed systemic inflammatory lesions consistent with ENL, requiring hospitalization. She was treated with high-dose corticosteroids for ENL and methotrexate to treat type 1 reaction and continued multidrug therapy (MDT) with minocycline, rifampin, and clarithromycin for leprosy, which led to significant clinical improvement. Conclusion: This case highlights the diagnostic challenges of leprosy in the United States and the importance of recognizing ENL as a severe immunologic complication requiring prompt intervention. A multidisciplinary approach is essential for optimal patient outcomes.

ABSTRACT Recent decades have seen a rise in the incidence of tick-borne diseases in the US, along with an increased number of pathogens transmitted by ticks, and geographic expansion of tick populations. A variety of laboratory testing methodologies are available for the diagnosis of tick-borne diseases, including serology, microscopy, and molecular-based methods. The preferred approach varies by the specific disease, locally available test options, and the stage of illness at patient presentation. This mini-review focuses on updates in our understanding of the epidemiology of tick-borne diseases in the US and advances in the field of laboratory diagnostics.

ABSTRACT Clinical and Laboratory Standards Institute (CLSI) recommends a blood culture contamination (BCC) threshold of <3%, with ≤1% considered optimal. However, there is not a standardized definition of BCC, and the effect of multiple definitions on BCC rates or what definitions laboratories use remain unknown. We surveyed 52 hospitals and analyzed 362,078 blood cultures (BCx) collected 1 September 2019 to 31 August 2021 from 62 intensive care units (ICUs) and 231 wards from 48 of these hospitals. We calculated and compared BCC rates using the College of American Pathologists (CAP) or CLSI criteria (both utilize a limited number of skin commensals to define BCC) and the comprehensive National Healthcare Safety Network (NHSN) commensal list. We characterized factors associated with BCC and related outcomes (central-line associated bloodstream infection [CLABSI] and vancomycin use). BCC, BCx positivity, and single BCx rates were monitored by 100%, 39%, and 21% of hospitals, respectively. Hospitals used CAP (65%), CLSI (17%), and NHSN (17%) criteria to define BCC. Mean BCC rate by CAP (CAP-BCC) was 1.38% for ICUs and 0.96% for wards. BCC rates remained similar by CLSI criteria but increased when using NHSN list. Sharing BCC data outside of the laboratory, measuring additional BCx quality indicators, and limiting central catheter-drawn BCx were associated with lower BCC rates. BCC was associated with higher CLABSI rates in ICUs. This study demonstrated variability in laboratory practices and opportunities to optimize BCx stewardship. IMPORTANCE Blood culture contamination (BCC) is associated with patient harm and unnecessary use of healthcare resources. BCC thresholds have been established; however, multiple BCC definitions exist. There is limited data on how BCC rates differ depending on the BCC definition used, what definitions laboratories most commonly use, or their approach to other blood cultures (BCx) quality indicators such as single rates or BCx positivity. A cross-sectional multicenter survey and analysis of BCx data from intensive care unit and wards revealed that most laboratories did not track single BCx or BCx positivity rates and that there was variability in how BCC was defined. Additionally, BCC rates were influenced by the definition used. BCC was associated with increased central-line associated bloodstream infection rates.

In mammals, breathing requires an inspiratory pump to generate a negative intrathoracic pressure and thus pull air into the lungs for gas exchange. Exclusively in mammals, the diaphragm muscle (DIAm) is the major inspiratory pump, which separates the thoracic and abdominal cavities. With DIAm contraction, a negative intrathoracic and positive abdominal pressure are generated [i.e., transdiaphragmatic pressure (P di )]. During breathing, the DIAm is very active, with a duty cycle (time active vs. inactive) similar to that of the heart. Like the heart, this is a pump that cannot fail! Thus, in controlling breathing, the nervous system must activate DIAm to accomplish pump function while avoiding fatigue. The timing of DIAm activations must also be coordinated with activation of the upper airway muscles (the pipes) to avoid airway occlusion and aspiration and the intercostal and abdominal muscles to appropriately stiffen the body cavities. Similarly, neural control and coordination of the DIAm in nonventilatory behaviors (airway protection, swallowing, vocalization, and voiding) are equally complex, with coordinated activation of the DIAm and abdominal muscles. It is becoming increasingly clear that impaired nonventilatory functions underlie the pathophysiology of a variety of medical conditions. In this review, we will delve into the detailed mechanistic underpinnings of the neural control of the DIAm and the symphonic coordination of its activation during breathing and other behaviors. We will attempt to move the field from considering the DIAm only as an inspiratory pump, emphasizing neural control of airway, intercostal, and abdominal muscles that must be coordinated with DIAm activation.

Background: Data accessibility remains a significant barrier in healthcare AI due to privacy constraints and logistical challenges. Synthetic data, which mimics real patient information while remaining both realistic and non-identifiable, offers a promising solution. Large Language Models (LLMs) create new opportunities to generate high-fidelity clinical conversations between patients and physicians. However, the value of this synthetic data depends on careful evaluation of its realism, accuracy, and practical relevance. Objective: To assess the performance of four leading LLMs: ChatGPT 4.5, ChatGPT 4o, Claude 3.7 Sonnet, and Gemini Pro 2.5 in generating synthetic transcripts of patient–physician interactions in plastic surgery scenarios. Methods: Each model generated transcripts for ten plastic surgery scenarios. Transcripts were independently evaluated by three clinically trained raters using a seven-criterion rubric: Medical Accuracy, Realism, Persona Consistency, Fidelity, Empathy, Relevancy, and Usability. Raters were blinded to the model identity to reduce bias. Each was rated on a 5-point Likert scale, yielding 840 total evaluations. Descriptive statistics were computed, and a two-way repeated measures ANOVA was used to test for differences across models and metrics. In addition, transcripts were analyzed using automated linguistic and content-based metrics. Results: All models achieved strong performance, with mean ratings exceeding 4.5 across all criteria. Gemini 2.5 Pro received mean scores (5.00 ± 0.00) in Medical Accuracy, Realism, Persona Consistency, Relevancy, and Usability. Claude 3.7 Sonnet matched the scores in Persona Consistency and Relevancy and led in Empathy (4.96 ± 0.18). ChatGPT 4.5 also achieved perfect scores in Relevancy, with high scores in Empathy (4.93 ± 0.25) and Usability (4.96 ± 0.18). ChatGPT 4o demonstrated consistently strong but slightly lower performance across most dimensions. ANOVA revealed no statistically significant differences across models (F(3, 6) = 0.85, p = 0.52). Automated analysis showed substantial variation in transcript length, style, and content richness: Gemini 2.5 Pro generated the longest and most emotionally expressive dialogues, while ChatGPT 4o produced the shortest and most concise outputs. Conclusions: Leading LLMs can generate medically accurate, emotionally appropriate synthetic dialogues suitable for educational and research use. Despite high performance, demographic homogeneity in generated patients highlights the need for improved diversity and bias mitigation in model outputs. These findings support the cautious, context-aware integration of LLM-generated dialogues into medical training, simulation, and research.