Citations

Abstract Background The Global Leadership Initiative on Malnutrition (GLIM) introduced an approach for malnutrition diagnosis in 2019 that comprised screening followed by assessment of three phenotypic criteria (weight loss, low body mass index [BMI], and low muscle mass) and two etiologic criteria (reduced food intake/assimilation and inflammation/disease burden). This planned update reconsiders the GLIM framework based on published knowledge and experience over the past 5 years. Methods A working group ( n = 43 members) conducted a literature search spanning 2019–2024 using the keywords “Global Leadership Initiative on Malnutrition or GLIM.” Prior GLIM guidance activities for using the criteria on muscle mass and inflammation were reviewed. Successive rounds of revision and review were used to achieve consensus. Results More than 400 scientific reports were published in peer‐reviewed journals, forming the basis of 10 systematic reviews, some including meta‐analyses of GLIM validity that indicate strong construct and predictive validity. Limitations and future priorities are discussed. Working group findings suggest that assessment of low muscle mass should be guided by experience and available technological resources. Clinical judgment may suffice to evaluate the inflammation/disease burden etiologic criterion. No revisions of the weight loss, low BMI, or reduced food intake/assimilation criteria are suggested. After two rounds of review and revision, the working group secured 100% agreement with the conclusions reported in the 5‐year update. Conclusion Ongoing initiatives target priorities that include malnutrition risk screening procedures, GLIM adaptation to the intensive care setting, assessment in support of the reduced food intake/assimilation criterion, and determination of malnutrition in obesity.

Abstract Studies have identified genetic and epidemiologic factors associated with mammographic density (MD) phenotypes. However, MD-associated genetic variants only account for a small proportion of the total estimated heritability. Interrogating interactions between genetic and epidemiologic factors could potentially identify additional MD-associated loci, expand our understanding of the genetic basis of MD phenotypes, and clarify how epidemiologic factors modulate relationships between genetic variants and MD. We conducted six separate genome-wide, gene–environment (GxE) interaction analyses, applying 2 degrees of freedom (df) and 1df interaction tests, for each of three MD phenotypes (percent density, dense area (DA), and nondense area (NDA)). The six epidemiologic factors considered were height, ever parous, parity, ever menopausal hormone therapy, ever breastfeeding, and months of breastfeeding. We included European ancestry participants from multiple studies within the Markers of Density consortium and the Breast Cancer Association Consortium (n = 4895-16 218 depending on specific analyses). We identified 11 loci with genome-wide significant (P < 5 × 10−8) interaction tests including two novel common genetic signals interacting with parity (8p21.2) and ever breastfeeding (19p13.2) for NDA. Our results suggest that epidemiologic risk factors might influence relationships between common genetic variants and MD phenotypes at particular genomic loci.

Abstract Background Despite advances in clinical microbiology, the diagnosis of invasive fungal infections remains challenging. Giant magnetoresistance (GMR) is a novel technology that enables the detection of trace amounts of cell-free DNA (cfDNA). We evaluated a high-multiplex molecular diagnostic assay coupled with GMR-enabled lab-on-a-chip technology that can detect 18 different fungal species. Methods Analytical performance was evaluated in spiked plasma samples. After amplification, cfDNA was digested. Residual single-stranded DNA was flowed over a GMR sensor that was surface-coated with probes specific to different fungal species. After hybridization, magnetic beads bound to the probe complexes produced a GMR signal that was detected by the sensors. Clinical performance was determined using residual serum samples collected before the initiation of antifungal treatment from 20 patients with infection. Results The limit of detection of the assay ranged from 5 to 50 copies per polymerase chain reaction (PCR) reaction. Nonspecific signals were not observed in the spiked samples. Fungal cfDNA was detected in 80% of patients with invasive candidiasis (3/4 with candidemia, 5/6 with invasive candidiasis without candidemia), all 3 cases of invasive pulmonary aspergillosis, and all 3 cases of disseminated histoplasmosis. cfDNA was not detected in 2 patients with cryptococcosis (both had negative blood cultures) and 2 patients with Pneumocystis pneumonia. Conclusions We developed a novel GMR-enabled multiplex PCR assay detecting fungal pathogens that have been prioritized for public health action. Clinical sensitivity was highest in cases of presumed angioinvasion and dissemination. This technology has the potential for use in the clinical microbiology laboratory setting.

OBJECTIVES: This study aimed to establish a set of guiding principles for data sharing and harmonization in critical care, focusing on the use of real-world data (RWD) and real-world evidence (RWE) to improve patient outcomes and research efficacy. The principles were developed through a systematic literature review and a modified Delphi process, with the goal of enhancing data accessibility, standardization, and interoperability across critical care settings. DATA SOURCES: Data sources included a comprehensive search of peer-reviewed literature, specifically studies related to the use of RWD and RWE in healthcare, guidelines, best practices, and recommendations on data sharing and harmonization. A total of 8150 articles were initially identified through databases such as MEDLINE and Web of Science, with 257 studies meeting inclusion criteria. STUDY SELECTION: Inclusion criteria focused on publications discussing health-related informatics, recommendations for RWD/RWE usage, data sharing, and harmonization principles. Exclusion criteria ruled out non-human studies, case studies, conference abstracts, and articles published before 2013, as well as those not available in English. DATA EXTRACTION: From the 257 selected studies, 322 statements were extracted. After removing irrelevant definitions and off-topic content, 232 statements underwent content validation and thematic analysis. These statements were then consolidated into 24 candidate guiding principles after rigorous review and consensus-building among the expert panel. DATA SYNTHESIS: A three-phase modified Delphi process was employed, involving a conceptualization group, a review group, and a Delphi group. In phase 1, experts identified key themes and search terms for the systematic review. Phase 2 involved validating and refining the prospective guiding principles, while phase 3 employed a Delphi panel to rate principles on acceptability, importance, and feasibility. This process resulted in 24 guiding principles, with high consensus achieved in rounds 2 and 3 on their relevance and applicability. CONCLUSIONS: The systematic review and Delphi process resulted in 24 guiding principles to improve data sharing and harmonization in critical care. These principles address challenges across the data lifecycle, including generation, storage, access, and usage of RWD and RWE. This framework is designed to promote more effective and equitable data practices, with relevance for the development of artificial intelligence-based decision support tools and clinical research. The principles are intended to guide the responsible use of data science in critical care, with emphasis on ethics and equity, while acknowledging the variability of resources across settings.

INTRODUCTION: Opioids are used to treat pain in chronic pancreatitis (CP), but little is known about current use patterns. The aim of this study was to characterize the utilization of opioids and associations with clinical characteristics in adult patients with CP. METHODS: This cross-sectional analysis used baseline data from participants with definite CP enrolled in a cohort study in the United States (PROspective Evaluation of CP for EpidEmiologic and Translational StuDies). Data on demographics, pain medication use, healthcare utilization, disability, and pain patterns were systematically collected in case report forms while quality of life was assessed with patient-reported outcome instruments. Opioid use was classified according to strength (weak or strong) and frequency (scheduled or as-needed). RESULTS: A total of 681 participants (n = 364, 53% male) were included: 299 (44%) were current opioid users (22% only weak opioids and 22% at least 1 strong opioid). Increasing frequency and severity of pain was associated with increase of weak, strong, as-needed, or scheduled opioids. Neuromodulators were used by ∼40% of participants; increasing use was associated with increasing frequency and severity of pain. On multivariate analysis, independent predictors associated with strength and frequency of current opioid use were pain patterns (odds ratios [ORs] 1.84–8.32 and ORs 1.92–8.52, respectively, P < 0.001) and prior celiac plexus block (OR 3.54, 95% confidence intervals 1.82–6.87 and OR 3.42, 95% confidence intervals 1.76–6.64, respectively). Participants using opioids had higher prevalence of disability, healthcare utilization, and poorer quality of life. DISCUSSION: Opioid use in CP is common and associated with increased pain severity and constancy. These data provide foundational estimates for future trials that can elucidate the complex interactions between patient factors, pain, and interventions.