Clinical Resources Research Articles

Self-Medication Practice and Associated Factors Among Health Professionals in Spain

(1) Background: Self-medication, defined as the use of medications without professional supervision, is a common practice that presents both potential benefits and significant risks. This study analyzes the prevalence, patterns, and determinants of self-medication among health professionals in Spain. (2) Methods: A cross-sectional descriptive design was employed with 438 health professionals, predominantly women (81.1%), with a median age of 42 years. The majority of the healthcare workers were nurses (45%). (3) Results: The results revealed a high prevalence of self-medication (59.4%). Analgesics and anti-inflammatory drugs were the most commonly used. Age and professional knowledge emerged as significant factors influencing this behavior. The main reasons for self-medication included the mildness of symptoms, easy access to medications, and previous successful experiences. Digital sources, especially websites, were the most consulted. Confidence in artificial intelligence tools as a clinical resource was moderate, with 18% of participants consulting AI tools, a rate comparable to the 19.5% for scientific databases. Logistic regression analysis identified age, knowledge of recommended doses, and perceived efficacy as significant predictors, while concern about risks acted as a protective factor. (4) Conclusions: This study highlights the need for educational interventions aimed at promoting responsible self-medication practices and mitigating associated risks among healthcare professionals.

Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases

Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human, financial, and clinical resources in healthcare systems worldwide. This cohort study evaluated three machine learning algorithms—XGBoost, Elastic Net logistic regression, and an Artificial Neural Network—to develop a prediction model for three outcomes: mortality, hospitalization, and emergency department visits. The objective was to build a clinical decision support system for patients with noncommunicable diseases treated at the Alma Mater Hospital complex in Medellín, Colombia. We collected 4845 electronic medical record entries from 5000 patients included in the study. The median age was 71.83 years, with 63.8% women and 29.7% receiving home care. The most prevalent medical conditions were diabetes (52.9%), hypertension (67.2%), dyslipidemia (57.3%), and COPD (19.4%). For mortality prediction, the Elastic Net logistic regression model achieved an AUCROC of 0.883 (95% CI: 0.848–0.917), the XGBoost model reached an AUCROC of 0.896 (95% CI: 0.865–0.927), and the Neural Network achieved 0.886 (95% CI: 0.853–0.916). For hospitalization, the Elastic Net model had an AUCROC of 0.952 (95% CI: 0.937–0.965), the XGBoost model achieved 0.963 (95% CI: 0.952–0.974), and the Neural Network scored 0.932 (95% CI: 0.915–0.948). For emergency department visits, the AUCROC values were 0.980 (95% CI: 0.971–0.987) for Elastic Net, 0.977 (95% CI: 0.967–0.986) for XGBoost, and 0.976 (95% CI: 0.968–0.982) for the neural network. A dashboard was developed to interact with an ensemble risk categorization segmenting patient risk in the cohort to aid in clinical decision-making. A clinical decision support system based on artificial intelligence using electronic medical records possibly can help segmenting the risk in populations with Noncommunicable Diseases for effective decision-making.

Trends in resource utilization for new-onset psychosis hospitalizations at children's hospitals.

Children with new-onset psychosis often require hospitalization for medical evaluation. The goal of this study was to assess variations in the management of children with new-onset psychosis and characterize trends in resource utilization. The study included index hospitalizations for children ages 7-18 admitted to children's hospitals with a primary diagnosis of psychosis from 2011 to 2022 using the Pediatric Health Information System (PHIS) database. Children with a complex chronic condition were excluded. Resource utilization categories included medication, imaging, laboratory, and other clinical resources. Variability in resource utilization was assessed using covariance tests for random intercepts with generalized linear modes after adjusting for age, sex, payor, and severity. Trends in resource utilization were examined using generalized estimating equations adjusting for the same factors and accounting for hospital clustering. Our data set included 7126 new-onset psychosis hospitalizations from 37 children's hospitals. Teenage males and non-Hispanic Whites were most likely to be hospitalized. There was a significant variation in resource utilization across hospitals in all categories (p < .001). The most frequently utilized resources were antipsychotic medications (76%), serum chemistry (77%), toxicology labs (72%), and brain magnetic resonance imaging (22%). The most notable increases in utilization were in the performance of laboratory tests, brain imaging, anesthetic use, and intravenous immunoglobulin use. Study findings suggest that there has been a stable rate of hospitalization for children with new-onset psychosis, yet a significant variation in the medical evaluation exists. Significant increases and variations in resource utilization across all categories suggest an emerging need for robust evidence and consensus-based practice guidelines.

Enhanced Prediction of Intensive Care Unit Length of Stay using a Stack Ensemble of Machine Learning Models

The Length of Stay (LoS) refers to the time between a patient's hospital admission and discharge. LoS is considered to increase as the complexity of the disease increases. A prolonged stay in the Intensive Care Unit (ICU) can consume clinical resources and be labor intensive. Models that correctly predict LoS are needed to help medical experts make better decisions. To define an ideal process system, healthcare models must consider the patient's condition, availability of beds, resources, etc. These predictions can also help insurance companies manage their budgets. Existing models deploy machines and deep learning techniques to predict LoS. However, there is a need for improvement, considering the features associated with the process. This study presents machine learning algorithms, such as SVM and a stack ensemble, with improved accuracy over existing models. Experiments were carried out on a benchmark dataset, MIMIC-III, specific to ICU patients. The SVM model achieved an accuracy of 93.88%, while the stack ensemble model showed an improved accuracy of 94.70%. The results show that combining machine learning models achieves better prediction rates, which helps healthcare professionals make better decisions.

The Gastric Cancer Registry: A multi-omic cellular and molecular resource for cancer biomarker and therapeutic discovery.

491 Background: The Gastric Cancer Registry (GCR) is a comprehensive clinical and genomic data resource focused on gastric cancer (GC) patients and individuals with a family history of GC or a germline CDH1 mutation. As part of the GCR’s ongoing enrollment, patients contribute cancer history information, archival tumor samples and other biospecimens. The cancer samples undergo extensive molecular and cellular analysis that includes genomic sequencing and spatial analysis. We have generated a robust dataset, publicly accessible online through the GCR Genome Explorer (GCR-GE). This genomic, molecular and cellular resource provides a rich data set that accelerates multidisciplinary research aimed at improving detection and treatment strategies for GC. GC disproportionately affects Latin American populations - we have included new cohorts from Latin America. The GCR enables researchers, clinicians and patients to address specific questions about the molecular and cellular basis of GC. Methods: GC samples underwent whole genome, whole exome, and bulk RNA sequencing were conducted. The genomic features identified include copy number variation, gene mutations, gene expression, microbiome composition, estimation of tumor-infiltrating immune cells, tumor neoantigens, MSI/MSS status, and HLA subtypes. In addition to genomic data, clinical data was compiled from survey responses and pathology reports and integrated into the GCR-GE. We have incorporated single cell and spatial analysis that includes diverse cell types, single cell gene expression and cellular architecture in native tumor tissues. This data was released to the GCR Genome Explorer website. Results: A total of 817 subjects have enrolled in the GCR including 598 diagnosed with GC and 219 at high-risk for GC through family history of GC and/or a CDH1 mutation. Of the 598 with GC, some met multiple criteria including at least 40 with a family history of GC, 10 with a CDH1 mutation, and 18 with both a family history of GC and a CDH1 mutation. The GCR-GE includes data from 257 gastric tumors in addition to external datasets from TCGA. In the latest data release, clinical and genomic data from 78 gastric tumors from patients in Latin America were integrated into the GCR-GE. Conclusions: The GCR is a comprehensive database of crucial clinical and genomic data necessary for driving forward GC research. Through global expansion, the GCR has generated a more representative dataset by increasing racial diversity and including underrepresented populations that are at higher risk for GC.

Clinical Burden and Healthcare Resource Use of Asthma in Children in the UK

Clinical Burden and Healthcare Resource Use of Asthma in Children in the UK

TELEHEALTH PROGRAM CREATES EXERCISE HABIT FOR NON-ADHERENT PATIENT WITH ORTHOSTATIC INTOLERANCE

INTRODUCTION Orthostatic Intolerance (OI) impacts patients’ ability to perform activities of daily living (ADLs) often leading to symptom exacerbation and significant deconditioning over time. Exercise aids symptom control by increasing cardiac output and venous return. Creating and maintaining an exercise routine often requires additional supports. CASE PRESENTATION A 15-year-old female was referred for a 6-month history of postural dizziness and presyncope. At the initial clinic visit, orthostatic testing was completed which showed a 27-bpm increase in heart rate. Baseline ECG showed sinus rhythm with normal intervals and review of systems was unremarkable. Patient history, symptoms and testing were noted to be consistent with OI. Initial interventions included general exercise resources and education materials, such as a daily aerobic walking plan (begin with 5-minute duration and increase by 5 minutes/week until 60 minutes total is achieved per day). Quarterly to bi-yearly clinic follow-ups showed no improvement in symptoms, despite reinforcing exercise recommendations and education at each visit. Follow up repeat testing, predicted aerobic capacity assessment, muscular endurance assessment, and surveys were completed. Exercise physiologist (ExP) initiated telehealth exercise sessions. OUTCOME Prior to intervention, patient physical activity consisted of walking at work. ExP conducted eleven telehealth exercise sessions over a 3-month period (30 min duration, 2 times/week). Sessions included education on form, safety, exercise components, &amp; intensity progression. Resistance exercises were completed with real-time supervision and feedback. Following completion of the telehealth program, testing showed decrease in symptom severity (-110%), increase in predicted aerobic capacity (+28%), maintenance of lower extremity muscular endurance (-6%), decrease in fatigue (-62%), and maintenance of mobility (-27%). One month post completion of telehealth intervention patient remained adherent to exercise prescription of aerobic exercise (2 times/week) &amp; strength training (2 times/week). DISCUSSION Early clinic education emphasized the importance of exercise for this patient; however, neither education nor clinic resources resulted in adherence to an exercise routine. Virtually delivering high quantity, synchronized exercise sessions, allowed the patient to experience improvement in functional capacity and reduction in symptom intensity. Virtual delivery of exercise medicine sessions can be applied to multiple clinical conditions and wider delivery to varied patient populations should be explored.

Pain revolution in the public health system: Active coping strategies for chronic pain unit.

Chronic pain represents a global health challenge demanding a transition from traditional biomedical to patient-centered biopsychosocial models. This masterclass explores active coping strategies for effective chronic pain management within healthcare systems. It describes the Unit of Active Coping Strategies for Chronic Pain (UAAD - Unidad de Afrontamiento Activo para el Dolor) in Primary Care in Valladolid, Spain, as a successful example of implementing a biopsychosocial care model for treating chronic pain. To provide tools that allow the application of active coping strategies in the treatment of patients with chronic pain and how to implement the UAAD units in other healthcare systems. This masterclass describes the UAAD's innovative approach, starting with its comprehensive and personalized methodology. This includes a referral system, a thorough assessment encompassing biological, psychological, and social factors, and a functional categorization system. These elements guide personalized treatment plans delivered through group and individual programs grounded in therapeutic exercise and pain science education. Four key pillars are highlighted: clinical care, teaching, resource management, research and dissemination. Embracing this model empowers healthcare providers to address the growing burden of chronic pain. It also enables patients to take an active role in their recovery and self-management.

Predicting accrual success for better clinical trial resource allocation

Accrual success is one key determining factor for the success of clinical trials. Global data analyses of all terminated trials reported that 55% of trials were terminated due to low accrual rates. Failure to meet accrual goals have a significant impact on costs for sponsors, academic institutions, investigators, and society at large. The ability to predict trial accrual success with high precision before the trial starts would be highly valuable, preventing the allocation of critical resources for trials unlikely to meet accrual goals. In the present study, we constructed a dataset for predicting clinical trial failure based on poor accrual using clinicaltrial.gov data containing information on 57,846 trials. Features of the dataset were informed by prior literature and constructed using data-driven natural language processing methods. We built predictive models for accrual failure using state-of-the-art supervised machine learning protocols and methods. Models resulted in good predictive performance that was stable over a 10-year time period, with predictive performance of cross-validation AUC = 0.744 (+/-0.018) and prospective validation AUC = 0.737 (+/-0.038). We also improved model calibration and examined model performance with the reject option. These modifications enable model translation into decision support tools for various real-world settings. To the best of our knowledge, this is the first study to develop models for predicting clinical trial failure due to accrual based on a large dataset with a comprehensive set of trial features.

TransAnaNet: Transformer-based anatomy change prediction network for head and neck cancer radiotherapy.

Adaptive radiotherapy (ART) can compensate for the dosimetric impact of anatomic change during radiotherapy of head-neck cancer (HNC) patients. However, implementing ART universally poses challenges in clinical workflow and resource allocation, given the variability in patient response and the constraints of available resources. Therefore, the prediction of anatomical change during radiotherapy for HNC patients is of importance to optimize patient clinical benefit and treatment resources. Current studies focus on developing binary ART eligibility classification models to identify patients who would experience significant anatomical change, but these models lack the ability to present the complex patterns and variations in anatomical changes over time. Vision Transformers (ViTs) represent a recent advancement in neural network architectures, utilizing self-attention mechanisms to process image data. Unlike traditional Convolutional Neural Networks (CNNs), ViTs can capture global contextual information more effectively, making them well-suited for image analysis and image generation tasks that involve complex patterns and structures, such as predicting anatomical changes in medical imaging. The purpose of this study is to assess the feasibility of using a ViT-based neural network to predict radiotherapy-induced anatomic change of HNC patients. We retrospectively included 121 HNC patients treated with definitive chemoradiotherapy (CRT) or radiation alone. We collected the planning computed tomography image (pCT), planned dose, cone beam computed tomography images (CBCTs) acquired at the initial treatment (CBCT01) and Fraction 21 (CBCT21), and primary tumor volume (GTVp) and involved nodal volume (GTVn) delineated on both pCT and CBCTs of each patient for model construction and evaluation. A UNet-style Swin-Transformer-based ViT network was designed to learn the spatial correspondence and contextual information from embedded image patches of CT, dose, CBCT01, GTVp, and GTVn. The deformation vector field between CBCT01 and CBCT21 was estimated by the model as the prediction of anatomic change, and deformed CBCT01 was used as the prediction of CBCT21. We also generated binary masks of GTVp, GTVn, and patient body for volumetric change evaluation. We used data from 101 patients for training and validation, and the remaining 20 patients for testing. Image and volumetric similarity metrics including mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), Dice coefficient, and average surface distance were used to measure the similarity between the target image and predicted CBCT. Anatomy change prediction performance of the proposed model was compared to a CNN-based prediction model and a traditional ViT-based prediction model. The predicted image from the proposed method yielded the best similarity to the real image (CBCT21) over pCT, CBCT01, and predicted CBCTs from other comparison models. The average MSE, PSNR, and SSIM between the normalized predicted CBCT and CBCT21 are 0.009, 20.266, and 0.933, while the average Dice coefficient between body mask, GTVp mask, and GTVn mask is 0.972, 0.792, and 0.821, respectively. The proposed method showed promising performance for predicting radiotherapy-induced anatomic change, which has the potential to assist in the decision-making of HNC ART.

Study of the impact of ClinGen Revisions on ACMG/AMP variant semi-automatic classification for Rare Diseases diagnosis

With the rapid development of massive sequencing technologies, the analysis of genetic variants for clinical diagnosis has exponentially escalated, particularly in the context of Rare Diseases (RDs). Diagnosing them involves identifying the genetic variants responsible for the underlying pathology development.In 2015, the American College of Medical Genetics (ACMG) established a set of recommendations to assess the evidence associated with each variant, aiming to achieve a standardized five tier classification. Over the past 5 years, ClinGen, the NIH-funded Clinical Genome Resource, has reviewed these criteria in order to make variant classification a more reproducible and rigorous process.This paper examines the impact of ClinGen-Rev modifications on variant classification, comparing them with the ACMG-2015 original recommendations. After analyzing sets of genetic variants, extracted from VCFs samples, using both criteria, we observed a change in 8.0 % of the clinical verdicts for these variants. ClinGen-Rev modifications correctly categorized 89.2 % of the curated variants, representing a significant improvement compared to the 65.6 % achieved by ACMG-2015. We also analyzed the modifications impact in a real like clinical setting, showing a significant overall reduction of VUS variants and thus potential reduction in analysis time. Finally, we discuss the underlying reasons for the most relevant changes in terms of specific labels and present their implications on the prioritization and selection process of variants, identifying some recommendations of key significant importance.

Robot-assisted PFNA surgery improves clinical outcomes in the treatment of unstable femoral intertrochanteric fractures in elderly patients compared with traditional PFNA surgery

To assess the clinical outcomes of robot-assisted proximal femoral nail antirotation (PFNA) surgery in elderly patients with unstable femoral intertrochanteric fractures (UFIFs). 151 patients who underwent UFIF and PFNA surgery between January 2020 and May 2024 were analyzed retrospectively. Of these, 78 patients were treated with traditional PFNA surgery (control group), and 73 patients were treated with robot-assisted PFNA surgery (observation group). The observed indicators included visual analog pain scale (VAS) score, surgery time, PFNA operation time, tip-apex distance (TAD), intraoperative blood loss (IBL), perioperative hidden blood loss (PHBL), postoperative allogeneic blood transfusion rate (PABTR), hospital stay, time to first independent walking with crutches postoperatively, time to independent walking without crutches, and Harris hip function score (HHFS). There were no statistically significant differences in general condition between the two groups before surgery. Surgery time, PFNA operation time, IBL, PHBL, PABTR, TAD, hospital stay, time to first independent walking with crutches postoperatively, time to independent walking without crutches, VAS score, and HHFS were significantly improved in the observation group compared with the control group. Thus, compared with traditional PFNA surgery for UFIF in elderly patients, robot-assisted PFNA yields better outcomes in terms of the quality of PFNA surgery, surgical trauma, conserving clinical blood resources, and patient recovery.

P-1555. Impact of Empiric Treatment Failure on Clinical Outcomes and Healthcare Resource Utilization Among US Females with Uncomplicated Urinary Tract Infections

Abstract Background Management of uncomplicated urinary tract infections (uUTIs) could be impacted by antibiotic (ABX) treatment failure (TF), which may lead to suboptimal outcomes. This study aimed to investigate the impact of TF to empiric oral ABX on clinical outcomes and healthcare resource utilization (HCRU) in female outpatients with uUTI in the United States. Methods This study used retrospective data from Optum’s de-identified Electronic Health Record (EHR) dataset (Jan 2017 to Sep 2022). Eligible female outpatients (ambulatory/emergency department [ED] setting) had a UTI diagnosis with no evidence of complicated UTI and ≥ 1 empiric prescription (Rx) for an oral ABX of interest within ±5 days (date of 1st Rx = index date), were ≥ 12 years of age on the index date and had 12 months of EHR activity pre- and post-index. TF was defined as a second oral ABX Rx, administration of intravenous ABX, or an ED visit or inpatient stay with a new primary diagnosis of UTI within 28 days post-index. Risk of clinical outcomes in the 12 months post-index, and rates of all-cause and UTI-related HCRU per 100 person-months during the index uUTI episode and in the 12-months post-index, were compared between patients with vs without TF using multivariable regression models. Results Of 376,004 patients, 62,873 (16.7%) experienced TF. In the 12 months post-index, patients with TF had a higher risk of recurrent UTI (adjusted risk ratio [95% confidence interval (CI)] = 1.23 [1.21–1.25]), acute pyelonephritis (1.77 [1.70–1.84]), and bacteremia/sepsis (2.51 [2.24–2.82]) vs those without TF (all p&amp;lt; 0.001; Table 1). Patients with TF also had higher rates of all-cause and UTI-related hospitalizations (adjusted rate ratio [95% CI] = 6.00 [5.59–6.39] and 12.09 [10.50–14.14], respectively) and ED visits (1.48 [1.45–1.50] and 1.28 [1.25–1.30], respectively) during the index uUTI episode (all p&amp;lt; 0.001; Table 2). Similar trends were observed in the 12-months post-index (Table 3). Conclusion Patients with empiric ABX TF had significantly greater risk of suboptimal clinical outcomes and higher HCRU than patients without TF, underscoring the importance of effective empiric ABX Rx to minimize the short- and long-term burden of TF in uUTI. Funding: GSK study 219500 Disclosures Debra L. Fromer, MD, GSK: Advisor/Consultant|Johnson &amp; Johnson/Janssen Pharmaceuticals: Advisor/Consultant Meghan Luck, PharmD, BCPS, GSK: Employee|GSK: Stocks/Bonds (Private Company) Malena Mahendran, MS, Analysis Group, Inc.: Employee of Analysis Group, Inc., a consulting company that received funding from GSK to conduct this study Rose Chang, ScD, Analysis Group, Inc.: Employee of Analysis Group, Inc., a consulting company that received funding from GSK to conduct this study Megan Pinaire, MPH, Analysis Group, Inc.: Employee of Analysis Group, Inc., a consulting company that received funding from GSK to conduct this study Dar Alon, MS, Analysis Group, Inc.: Employee of Analysis Group, Inc., a consulting company that received funding from GSK to conduct this study Mei Sheng Duh, MPH, ScD, Analysis Group, Inc.: Employee of Analysis Group, Inc., a consulting company that received funding from GSK to conduct this study Madison T. Preib, MPH, GSK: Employee|GSK: Stocks/Bonds (Public Company) Jeffrey J. Ellis, PharmD, MS, GSK: Employee|GSK: Stocks/Bonds (Public Company)

P-1534. The Impact of Broad-spectrum Antimicrobials in Patients with Community-onset Pneumonia at Low Risk of Drug-resistant Pathogens: A Historical Cohort Study

Abstract Background The administration of broad-spectrum antimicrobials has prevailed in the clinical management of community-onset pneumonia (COP). Growing evidence suggests that the use of unnecessary broad-spectrum antimicrobials for patients with COP leads to adverse events and poor outcomes. This study aims to understand the impact of broad-spectrum antipseudomonal β-lactam use on clinical outcomes and resource utilization in patients hospitalized for COP with a low risk for drug-resistant pathogens (DRPs). Methods This historical cohort study analyzed the hospital claims database in Japan from January to December 2018. The study includes patients aged 20 or older hospitalized with COP having intravenous antimicrobial therapy administration and excludes patients with a high risk for DRPs. Patients were selected into a broad-spectrum (antipseudomonal β-lactam therapy) and a narrow-spectrum (non-antipseudomonal β-lactam therapy) group based on the initial antimicrobial regimen. This study evaluates 30-day mortality as a primary outcome by inverse probability of treatment weighting (IPTW)and healthcare resource utilization as one of the secondary outcomes. Results A total of 15,617 patients met the inclusion criteria. This target population was divided into two groups: 2,627 patients in the broad-spectrum and 12,990 in the narrow-spectrum group. In the first group, the most commonly used antimicrobials were Piperacillin / Tazobactam (68%), and the 30-day mortality was 10.6 in contrast with 5.3% in the second group. Thus, the broad-spectrum group was associated with an increased 30-day mortality rate in contrast with the narrow-spectrum group after IPTW weighting (adjusted odds ratio 1.77, 95% confidence interval [1.52 to 2.06], &amp;lt; 0.001). This trend was observed regardless of pneumonia severity. The average inpatient cost was $6,139 USD for the broad-spectrum group against $5,184 USD for the narrow-spectrum group, with respective length of stay (LOS) figures of 23.1 and 19.8 days. Conclusion This study shows that the initial use of antipseudomonal β-lactams for COP with a low risk for DRPs is associated with higher poor outcomes, including death and higher healthcare resource utilization. Judicious selection of initial antimicrobials is warranted in the management of COP. Disclosures Junichi Hirayama, n/a, bioMerieux Japan: employee of bioMerieux, salary Rie Ueno, MD., BioMérieux Japan Ltd.: Employee of BioMérieux Japan Ltd. Hiroshi Mukae, M.D., Ph.D., AstraZeneca: Lecture fees|Gilead Sciences: Lecture fees|GSK: Lecture fees|MSD: Advisor/Consultant|MSD: Lecture fees|Pfizer: Lecture fees|Shionogi &amp; Co., Ltd.: Advisor/Consultant|Shionogi &amp; Co., Ltd.: Grant/Research Support|Shionogi &amp; Co., Ltd.: Lecture fees

P-505. Integrating PrEP Services into a Gynecology Clinic in Alabama: A Qualitative Analysis of Key Determinants

Abstract Background In the Southeastern United States, Black women are disproportionately diagnosed with HIV compared to White women. Client-centered approaches are needed to increase uptake of HIV prevention tools, including pre-exposure prophylaxis (PrEP). Gynecology (GYN) clinics are uniquely positioned to provide PrEP for women and Black women prefer receiving PrEP from GYN providers, but PrEP delivery in GYN settings has been limited. Thus, we evaluated barriers and facilitators to PrEP provision in GYN clinics. Methods From 08/2022-04/2023, we conducted dyadic focus groups and in-depth interviews with providers (i.e., physicians, nurses, and medical assistants) and clients accessing care at a university-affiliated GYN clinic in Alabama where clients face high STI burden. Interview guides grounded in the Information-Motivation-Behavioral Skills Model explored barriers to PrEP service delivery. Inductive qualitative analysis identified emergent themes from transcripts. Results 10 providers and 21 clients (100% Black, 100% female, median age 32 (range 19-44), 90.5% insured (67% with Medicaid)) were interviewed. Emergent themes included: (1) Sexual health – including STI/HIV prevention – as part of routine women’s healthcare. Providers preferred a risk-based approach to sexual healthcare while clients preferred a sex-positive approach. (2) Incorporation of PrEP services into women’s healthcare. Both clients and providers supported that PrEP is important for women’s health. Providers felt PrEP discussions should be limited to specific visits whereas clients desired regular discussions of PrEP. (3) Multi-level barriers for PrEP service delivery in GYN clinics. Barriers included limited client and provider PrEP knowledge, inadequate clinic resources, financial costs, and STI/HIV stigma. Conclusion Incorporating PrEP services into GYN clinics could increase PrEP uptake amongst Black women. Effective multi-level interventions should focus on increasing client and provider PrEP knowledge, integrating sexual health history assessment into routine care, mitigating STI/HIV stigma, and addressing structural barriers. Disclosures Latesha Elopre, MD, Merck: Grant/Research Support

P-1194. Comparison of COVID-19 inpatient burden in hospitalized children age &amp;lt; 5 years, by SARS-CoV-2 variant

Abstract Background COVID-19 disease severity has fluctuated with emergence of variants. To investigate disease severity in children, we analyzed clinical resource utilization among children hospitalized for COVID-19 during different variant periods in the United States. Methods We constructed a cohort of children age &amp;lt; 5 years hospitalized for COVID-19 (ICD-10-CM code U07.1 “COVID-19”) using PINC AI Healthcare Data, a hospital-based real world data source from the United States. Hospitalizations in pre-Delta (April - June 2021), Delta (July - December 2021) or Omicron (January 2022 – July 2023) variant periods were included. We examined pre-existing conditions, hospitalization length of stay (LOS), supplemental oxygen (O2), admission to and number of days in the intensive care unit (ICU) and receipt of invasive mechanical ventilation (IMV). P-values for 3-way comparisons were calculated using Chi-square tests for proportions and Kruskal-Wallis for medians. Results Of 10,316 children hospitalized for COVID-19, the majority occurred in the Omicron period (n=7,767, 75%) followed by Delta (2,241; 22%) and pre-Delta (308; 3%) periods (Table 1). The age, sex, race and ethnicity distribution of cases was similar between Delta and Omicron eras. Rheumatologic and other inflammatory conditions were more prevalent during pre-Delta; otherwise, the prevalence of pre-existing health conditions was generally similar across each variant wave (Table 2). Among the following outcomes, there were no significant differences across the variants: supplemental O2 (13% pre-Delta, 16% Delta, 18% Omicron, p = 0.06), LOS (median 2 days), proportion requiring ICU care (20-21%, p = 0.77), days in ICU care (median 1 pre-Delta, 2 Delta and Omicron) and proportion requiring IMV (6-8%, p = 0.35) (Table 3). Deaths were more common in the Delta (0.9%) than pre-Delta (0.7%) or Omicron (0.4%) eras. Conclusion Similar disease severity was observed among children age &amp;lt; 5 years hospitalized for COVID-19 during pre-Delta, Delta and Omicron periods. Although in-hospital death was more common with Delta, Omicron led to increased hospitalizations, highlighting the importance of continued preventative measures to prevent SARS-CoV-2 infection in children, including routine immunization. Disclosures Kathleen M. Andersen, PhD MSc, Pfizer Inc.: Employee|Pfizer Inc.: Stocks/Bonds (Public Company) Thomas M. Porter, MPH, Pfizer Inc.: Employee|Pfizer Inc.: Stocks/Bonds (Public Company) Maya Reimbaeva, MS, Pfizer Inc.: Employee|Pfizer Inc.: Stocks/Bonds (Public Company) Maria McColgan, MD, Pfizer Inc.: Employee|Pfizer Inc.: Stocks/Bonds (Public Company) Santiago M.C. Lopez, MD, Pfizer Inc.: Employee|Pfizer Inc.: Stocks/Bonds (Public Company)

582. Feeling the Vibes: An Investigation Into Resident Physician Antibiotic Prescribing Practices

Abstract Background Efforts to improve inpatient antibiotic prescribing are limited by a lack of insight into the heuristics and thought processes that influence the complicated decisions around antimicrobial use. We aimed to explore antimicrobial therapeutic decision making among resident physicians at a large academic health system. Figure 1. Thematic map of resident trainee antibiotic decision making process Methods We performed a qualitative study with a constructivist paradigm employing semi-structured, in-person focus groups of Internal Medicine interns and residents at the UCLA Ronald Reagan and Santa Monica Medical Centers from December 2023 through January 2024. Sessions were recorded, auto-transcribed, and edited for accuracy. Two researchers independently performed a thematic analysis of three distinct focus group transcripts. Results 25 residents participated in a total of three focus groups. Residents consistently identified a clear general approach to prescribing antibiotics, including triaging critical illness and identifying the following: the presence of infection; the source of infection; the antibiotic that covers the likely pathogens; and co-morbid conditions and risk factors. Two broad themes regarding initial acquisition of an approach to antibiotics emerged: institutional culture or “oral tradition,” and internal and external clinical resources. A major challenge in therapeutic decision-making centered around navigating uncertainty (Figure 1). Comments on the inconsistency of clinical reasoning and “vibes-based” prescribing were common. Residents identified certain safety net strategies to mitigate the anxieties associated with this uncertainty (Figure 1). Resident confidence improved over time via habit formation and trial and error, particularly when working independently as the Night Admitting Resident. ID physicians and pharmacists provided a model approach for therapeutic reasoning and supported residents to take risks they were otherwise reluctant to take (such as de-escalation). Conclusion Despite articulating a systematic approach to antimicrobial prescribing, resident therapeutic decision-making remains subject to many vulnerabilities, including emotional prescribing. A reliance on the ID consult as a safety net may account for increases in consult volume and requires further investigation. Disclosures All Authors: No reported disclosures

Choice of resuscitation fluids in critically ill adults: key messages from the European Society of Intensive Care Medicine 2024 clinical practice guideline.

The 2024 European Society of Intensive Care Medicine clinical practice guideline provide clinicians with evidence-based recommendations on intravenous fluid in critically ill adults across a range of common conditions. These guidelines aim to improve the practices of fluid therapy by adopting a global perspective that considers both clinical efficacy and resource utilization in diverse healthcare settings. The guidelines address three key questions: (1) albumin versus crystalloids, (2) balanced crystalloids versus isotonic saline, and (3) small-volume hypertonic solutions versus isotonic crystalloids. Recommendations were developed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach, ensuring a rigorous and transparent evaluation of the evidence across critical outcomes. Albumin was generally not favored over crystalloids due to its lack of demonstrated mortality benefit, higher cost, and limited availability in resource-limited settings. Balanced crystalloids were suggested over isotonic saline in most scenarios due to their potential to reduce mortality and mitigate the harmful effects of hyperchloremia, although the certainty of evidence was low. Small-volume hypertonic solutions were not shown to provide significant advantages over isotonic crystalloids, leading to a preference for the latter based on very low certainty evidence. This review provides an overview of the guideline development process and a detailed summary of their recommendations, highlighting key considerations for clinical practice. The guidelines also identify critical evidence gaps in fluid therapy research, underscoring the need for future studies to refine and optimize fluid management strategies in critically ill patients.

Blood lactate as a predictor of clinical outcomes and health-care resource needs in combat abdominal injuries

Background. Combat abdominal injuries (CAI) are cha­racterized by high mortality and occupy a leading position among the issues of battlefield surgery. It is very important to evaluate early and accurately the severity of CAI, which will allow starting correct treatment in time, preventing possible complications and improving clinical outcomes. The low sensitivity of commonly used cardiovascular parameters to detect the initial manifestations of tissue hypoperfusion induced by blood loss dictates the need to use more effective markers that reflect metabolic changes in tissues. The aim of this study is to determine the predictive efficacy of serum lactate regarding the risk of death and health-care resources needs in patients with CAI. Materials and methods. We examined 86 injured servicemen with CAI who were randomized into two groups — survivors and nonsurvivors. The predictive efficacy of capillary blood lactate and its clearance regarding the risk of death and health-care resource needs in CAI were studied. Six consecutive (within 48 h) measurements of lactate and lactate clearance corresponding to each measurement (except the first) were evaluated for each patient. Logistic regression models were constructed and analyzed to assess the influence of lactate level and lactate clearance on the risk of death and need for vasopressor support during medical evacuation to the Role 3 (after performing an emergency surgery). A correlation analysis was conducted on the association between lactate and the need for RBC transfusion, as well as assessment of the CAI severity using the Admission Trauma Score (AdTS). Results. In the group of nonsurvivors, lactate was higher (p = 0.001) at all stages of the study. We determined that an increase in the lactate level at the time of admission of a victim with CAI to the forward surgical team (LacpreOP) increased the risk of death by 1.5 times for every 1 mmol/l of lactate elevation (odds ratio (OR) 1.5, 95% confidence interval (CI) 1.22–1.85), and the chances of the need for vasopressor support during medical evacuation to the Role 3 — by 1.7 times for every 1 mmol/l of lactate elevation (OR 1.7, 95% CI 1.05–1.86). A strong positive correlation (ρ = 0.8) was found between LacpreOP and the need for RBC transfusion during the first 24 h after injury, as well as a moderate positive correlation (ρ = 0.5) between LacpreOP and CAI severity assessment on the AdTS. Conclusions. The results of the study indicate the high predictive efficacy of lactate regarding the risk of death (OR 1.5, 95% CI 1.22–1.85) and the need for vasopressor support during medical evacuation of victims with CAI to the Role 3 (OR 1.7, 95% CI 1.05–1.86). There was a positive correlation between lactate and the need for RBC transfusion du­ring first 24 h after injury (ρ = 0.8), as well as between lactate level and CAI severity according to the AdTS (ρ = 0.5).

A Single Institution's Surgical Care Model for Pediatric Burns With ≤10% Body Surface Area Involvement.

Small burn injuries are extremely prevalent in the pediatric population and continue to pose a challenge for clinicians. Despite their high incidence, a standardized algorithm for treating small burns does not currently exist, and care is often guided by clinical judgement and resource availability. The aim of this study was to explore the utility of a two-stage grafting technique, involving allograft and autograft, for treating small burns (≤10% total body surface area) in pediatric patients. A retrospective review of patients aged 0-18 years who had a small burn and underwent a two-stage grafting procedure between September 1, 2018 and September 1, 2022 was conducted. One hundred and seventy-five patients with 220 wounds met the inclusion criteria for this study. The mean time from presentation to allograft surgery was 11.4 days (SD 5.2) followed by autograft surgery approximately one week later. Most patients were discharged within 24 hours following allograft surgery (87.4%) and autograft surgery (81.1%). Mean autograft take was 97.7% (SD 11.8) with only four patients experiencing significant graft loss requiring subsequent re-grafting. These positive outcomes demonstrate that the two-stage technique can be successfully utilized for treating smaller pediatric burns. Moreover, these findings help to address the significant knowledge gap regarding the optimal approach to treating small burn wounds. Further research is warranted to learn more about aesthetic outcomes following two-stage grafting and determine how it compares to other techniques for treating small burns.