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To characterize the relationship between chlorhexidine gluconate (CHG) skin concentration and skin microbial colonization. Serial cross-sectional study. Adult patients in medical intensive care units (ICUs) from 7 hospitals; from 1 hospital, additional patients colonized with carbapenemase-producing Enterobacterales (CPE) from both ICU and non-ICU settings. All hospitals performed routine CHG bathing in the ICU. Skin swab samples were collected from adjacent areas of the neck, axilla, and inguinal region for microbial culture and CHG skin concentration measurement using a semiquantitative colorimetric assay. We used linear mixed effects multilevel models to analyze the relationship between CHG concentration and microbial detection. We explored threshold effects using additional models. We collected samples from 736 of 759 (97%) eligible ICU patients and 68 patients colonized with CPE. On skin, gram-positive bacteria were cultured most frequently (93% of patients), followed by species (26%) and gram-negative bacteria (20%). The adjusted odds of microbial recovery for every twofold increase in CHG skin concentration were 0.84 (95% CI, 0.80-0.87; < .001) for gram-positive bacteria, 0.93 (95% CI, 0.89-0.98; = .008) for species, 0.96 (95% CI, 0.91-1.02; = .17) for gram-negative bacteria, and 0.94 (95% CI, 0.84-1.06; = .33) for CPE. A threshold CHG skin concentration for reduced microbial detection was not observed. On a cross-sectional basis, higher CHG skin concentrations were associated with less detection of gram-positive bacteria and species on the skin, but not gram-negative bacteria, including CPE. For infection prevention, targeting higher CHG skin concentrations may improve control of certain pathogens.

To assess whether measurement and feedback of chlorhexidine gluconate (CHG) skin concentrations can improve CHG bathing practice across multiple intensive care units (ICUs). A before-and-after quality improvement study measuring patient CHG skin concentrations during 6 point-prevalence surveys (3 surveys each during baseline and intervention periods). The study was conducted across 7 geographically diverse ICUs with routine CHG bathing. Adult patients in the medical ICU. CHG skin concentrations were measured at the neck, axilla, and inguinal region using a semiquantitative colorimetric assay. Aggregate unit-level CHG skin concentration measurements from the baseline period and each intervention period survey were reported back to ICU leadership, which then used routine education and quality improvement activities to improve CHG bathing practice. We used multilevel linear models to assess the impact of intervention on CHG skin concentrations. We enrolled 681 (93%) of 736 eligible patients; 92% received a CHG bath prior to survey. At baseline, CHG skin concentrations were lowest on the neck, compared to axillary or inguinal regions ( < .001). CHG was not detected on 33% of necks, 19% of axillae, and 18% of inguinal regions ( < .001 for differences in body sites). During the intervention period, ICUs that used CHG-impregnated cloths had a 3-fold increase in patient CHG skin concentrations as compared to baseline ( < .001). Routine CHG bathing performance in the ICU varied across multiple hospitals. Measurement and feedback of CHG skin concentrations can be an important tool to improve CHG bathing practice.

As of October 21, 2022, a total of 27,884 monkeypox cases (confirmed and probable) have been reported in the United States. Gay, bisexual, and other men who have sex with men have constituted a majority of cases, and persons with HIV infection and those from racial and ethnic minority groups have been disproportionately affected (1,2). During previous monkeypox outbreaks, severe manifestations of disease and poor outcomes have been reported among persons with HIV infection, particularly those with AIDS (3-5). This report summarizes findings from CDC clinical consultations provided for 57 patients aged ≥18 years who were hospitalized with severe manifestations of monkeypox during August 10-October 10, 2022, and highlights three clinically representative cases. Overall, 47 (82%) patients had HIV infection, four (9%) of whom were receiving antiretroviral therapy (ART) before monkeypox diagnosis. Most patients were male (95%) and 68% were non-Hispanic Black (Black). Overall, 17 (30%) patients received intensive care unit (ICU)-level care, and 12 (21%) have died. As of this report, monkeypox was a cause of death or contributing factor in five of these deaths; six deaths remain under investigation to determine whether monkeypox was a causal or contributing factor; and in one death, monkeypox was not a cause or contributing factor.** Health care providers and public health professionals should be aware that severe morbidity and mortality associated with monkeypox have been observed during the current outbreak in the United States (6,7), particularly among highly immunocompromised persons. Providers should test all sexually active patients with suspected monkeypox for HIV at the time of monkeypox testing unless a patient is already known to have HIV infection. Providers should consider early commencement and extended duration of monkeypox-directed therapy in highly immunocompromised patients with suspected or laboratory-diagnosed monkeypox. Engaging all persons with HIV in sustained care remains a critical public health priority.

Background CHG bathing is used to suppress patients’ microbial skin colonization, in order to prevent infections and transmission of multidrug-resistant organisms. Prior work has suggested that microbial growth is inhibited when CHG skin concentrations exceed threshold levels. Methods We conducted 6 single-day surveys from January 2018 to February 2019 in 7 academic hospital MICUs with established CHG patient bathing. Adult patients were eligible to have skin swabbed from adjacent 25 cm2 areas on the neck, axilla, and inguinal region for culture and CHG concentration determination. CHG skin concentrations were measured by a semi-quantitative colorimetric assay. Selective media were used to isolate targeted microorganisms (Table 1). Species were confirmed by matrix-assisted laser desorption ionization time-of-flight mass spectrometry; antibiotic susceptibility was determined by MicroScan (Beckman Coulter). We modeled the relationship between CHG skin concentrations (log2-transformed) and microorganism recovery (yes/no as primary outcome) using multilevel models controlling for clustering of body sites within patients and within ICUs, assessing slope and threshold effects. Results We enrolled 736/759 (97%) patients and sampled 2176 skin sites. Gram-positive bacteria were detected most frequently (Table 1). The adjusted odds of identifying gram-positive organisms decreased linearly as CHG skin levels increased (Figure 1a), without evidence of a threshold effect. We also found significant negative linear slopes without evidence of threshold effects for other pathogens tested (Table 2; Figure 1), with the exception of gram-negative bacteria and vancomycin-resistant enterococci. When modeling quantitative culture results (colony-forming units) for gram-positive organisms as a continuous outcome variable, a similar relationship was found. Conclusion Higher concentrations of CHG were associated with less frequent recovery of gram-positive bacteria and Candida species on the skin of MICU patients who were bathed routinely with CHG. For microbial inhibition, we did not identify a threshold concentration of CHG on the skin; rather, increasing CHG skin concentrations led to additional gains in inhibition. For infection prevention, aiming for high CHG skin levels may be beneficial. Disclosures All authors: No reported disclosures.

Background Higher CHG skin levels may be needed to adequately control infection and transmission of pathogens in the ICU. We assessed whether measurement and feedback of patient CHG skin concentrations could improve CHG bathing quality and identified factors associated with higher CHG skin concentrations. Methods We conducted 6 one-day surveys from January 2018 to February 2019 in 7 academic hospital MICUs with established daily CHG bathing. Adults admitted >1 day were assessed for CHG skin levels with a semi-quantitative colorimetric assay using swabbed 25 cm2 areas of anterior neck, axilla, and inguinal skin. Prior to survey 4, results from the first 3 surveys (baseline) were reported to ICU leadership and front-line staff to retrain and reeducate on bathing technique. Feedback of results from prior surveys also occurred before surveys 5 and 6. For statistical analysis, mixed-effects models accounted for clustering of CHG measurements within patients and ICUs. We categorized CHG product type as “cloth” for no-rinse 2% CHG-impregnated cloth and “liquid” for 4% CHG liquid or foam. Results In total, 681 of 704 (97%) patients were enrolled. Three ICUs used CHG cloth, 3 ICUs used CHG liquid, and 1 ICU switched from liquid to cloth after the second survey. Median CHG skin concentrations were higher in both the baseline and feedback period for institutions using CHG cloth, as compared with liquid (table). Across all time points, axillary and inguinal regions had higher skin CHG concentrations than the neck (median 39.1, 78.1, 19.5 µg/mL, respectively, P < 0.001). After controlling for age, mechanical ventilation, presence of a central venous catheter, body site, and hours since last CHG bath, institutions that used CHG cloth had a 3-fold increase in adjusted CHG skin concentrations in the feedback period compared with the baseline period (P = 0.001, Figure). There was no significant change in CHG skin concentrations from baseline to feedback period for institutions that used liquid CHG. Conclusion CHG skin concentrations on MICU patients receiving daily CHG bathing varied by body site and CHG product type. The use of CHG cloth was associated with higher CHG skin levels, compared with CHG liquid. For ICUs using CHG cloth, feedback of CHG skin concentration results to ICU staff improved CHG bathing quality. Disclosures All Authors: No reported Disclosures.

The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.

Andrew Koh and colleagues report that gut anaerobes in adult mice prevent Candida albicans colonization by inducing an antimicrobial peptide. Candida albicans colonization is required for invasive disease 1 , 2 , 3 . Unlike humans, adult mice with mature intact gut microbiota are resistant to C. albicans gastrointestinal (GI) colonization 2 , 4 , but the factors that promote C. albicans colonization resistance are unknown. Here we demonstrate that commensal anaerobic bacteria—specifically clostridial Firmicutes (clusters IV and XIVa) and Bacteroidetes—are critical for maintaining C. albicans colonization resistance in mice. Using Bacteroides thetaiotamicron as a model organism, we find that hypoxia-inducible factor-1α (HIF-1α), a transcription factor important for activating innate immune effectors, and the antimicrobial peptide LL-37 (CRAMP in mice) are key determinants of C. albicans colonization resistance. Although antibiotic treatment enables C. albicans colonization, pharmacologic activation of colonic Hif1a induces CRAMP expression and results in a significant reduction of C. albicans GI colonization and a 50% decrease in mortality from invasive disease. In the setting of antibiotics, Hif1a and Camp (which encodes CRAMP) are required for B. thetaiotamicron –induced protection against C. albicans colonization of the gut. Thus, modulating C. albicans GI colonization by activation of gut mucosal immune effectors may represent a novel therapeutic approach for preventing invasive fungal disease in humans.

Background Knowledge graphs are well-suited for modeling complex, unstructured, and multi-source data and facilitating their analysis. During the COVID-19 pandemic, adverse event data were integrated into a knowledge graph to support vaccine safety surveillance and nimbly respond to urgent health authority questions. Here, we provide details of this post-marketing safety system using public data sources. In addition to challenges with varied data representations, adverse event reporting on the COVID-19 vaccines generated an unprecedented volume of data; an order of magnitude larger than adverse events for all previous vaccines. The Patient Safety Knowledge Graph (PSKG) is a robust data store to accommodate the volume of adverse event data and harmonize primary surveillance data sources. Methods We designed a semantic model to represent key safety concepts. We built an extract-transform-load (ETL) data pipeline to parse and import primary public data sources; align key elements such as vaccine names; integrated the Medical Dictionary for Regulatory Activities (MedDRA); and applied quality metrics. PSKG is deployed in a Neo4J graph database, and made available via a web interface and Application Programming Interfaces (APIs). Results We import and align adverse event data and vaccine exposure data from 250 countries on a weekly basis, producing a graph with 4,340,980 nodes and 30,544,475 edges as of July 1, 2022. PSKG is used for ad-hoc analyses and periodic reporting for several widely available COVID-19 vaccines. Analysis code using the knowledge graph is 80% shorter than an equivalent implementation written entirely in Python, and runs over 200 times faster. Conclusions Organizing safety data into a concise model of nodes, properties, and edge relationships has greatly simplified analysis code by removing complex parsing and transformation algorithms from individual analyses and instead managing these centrally. The adoption of the knowledge graph transformed how the team answers key scientific and medical questions. Whereas previously an analysis would involve aggregating and transforming primary datasets from scratch to answer a specific question, the team can now iterate easily and respond as quickly as requests evolve (e.g., “Produce vaccine-X safety profile for adverse event-Y by country instead of age-range”).

ObjectiveEstimating survival can aid care planning, but the use of absolute survival projections can be challenging for patients and clinicians to contextualise. We aimed to define how heart failure and its major comorbidities contribute to loss of actuarially predicted life expectancy.MethodsWe conducted an observational cohort study of 1794 adults with stable chronic heart failure and reduced left ventricular ejection fraction, recruited from cardiology outpatient departments of four UK hospitals. Data from an 11-year maximum (5-year median) follow-up period (999 deaths) were used to define how heart failure and its major comorbidities impact on survival, relative to an age–sex matched control UK population, using a relative survival framework.ResultsAfter 10 years, mortality in the reference control population was 29%. In people with heart failure, this increased by an additional 37% (95% CI 34% to 40%), equating to an additional 2.2 years of lost life or a 2.4-fold (2.2–2.5) excess loss of life. This excess was greater in men than women (2.4 years (2.2–2.7) vs 1.6 years (1.2–2.0); p<0.001). In patients without major comorbidity, men still experienced excess loss of life, while women experienced less and were non-significantly different from the reference population (1 year (0.6–1.5) vs 0.4 years (−0.3 to 1); p<0.001). Accrual of comorbidity was associated with substantial increases in excess lost life, particularly for diabetes, chronic kidney and lung disease.ConclusionsComorbidity accounts for the majority of lost life expectancy in people with heart failure. Women, but not men, without comorbidity experience survival close to reference controls.

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.