In light of the evolving COVID-19 pandemic, the Association of American Medical Colleges (AAMC) and Liaison Committee on Medical Education (LCME) released a joint statement in March 2020 recommending an immediate suspension of medical student participation in direct patient contact. As graduating medical students who will soon begin residency, the authors fully support this recommendation. Though paid health care workers, like residents, nurses, and environmental services staff, are essential to the management of COVID-19 patients, medical students are not. Students’ continued involvement in direct patient care will contribute to SARS-CoV-2 exposures and transmissions and will waste already limited personal protective equipment. By decreasing nonessential personnel in health care settings, including medical students, medical schools will contribute to national and global efforts to “flatten the curve.” The authors also assert that medical schools are responsible for ensuring medical student safety. Without the protections provided to paid health care workers, students are uniquely disadvantaged within the medical hierarchy; these inequalities must be addressed before medical students are safely reintegrated into clinical roles. Although graduating medical students and institutional leadership may worry that suspending clinical rotations might prevent students from completing graduation requirements, the authors argue the ethical obligation to “flatten the curve” supersedes usual teaching responsibilities. Therefore, the authors request further guidance from the LCME and AAMC regarding curricular exemptions/alternatives and adjusted graduation timelines. The pool of graduating medical students affected by this pause in direct patient contact represents a powerful reserve, which may soon need to be used as the COVID-19 pandemic continues to challenge the U.S. health care infrastructure.

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Clostridioides difficile infection (CDI) can result in severe disease and death, with no accurate models that allow for early prediction of adverse outcomes. To address this need, we sought to develop serum-based biomarker models to predict CDI outcomes. We prospectively collected sera ≤48 h after diagnosis of CDI in two cohorts. Biomarkers were measured with a custom multiplex bead array assay. Patients were classified using IDSA severity criteria and the development of disease-related complications (DRCs), which were defined as ICU admission, colectomy, and/or death attributed to CDI. Unadjusted and adjusted models were built using logistic and elastic net modeling. The best model for severity included procalcitonin (PCT) and hepatocyte growth factor (HGF) with an area (AUC) under the receiver operating characteristic (ROC) curve of 0.74 (95% confidence interval, 0.67 to 0.81). The best model for 30-day mortality included interleukin-8 (IL-8), PCT, CXCL-5, IP-10, and IL-2Rα with an AUC of 0.89 (0.84 to 0.95). The best model for DRCs included IL-8, procalcitonin, HGF, and IL-2Rα with an AUC of 0.84 (0.73 to 0.94). To validate our models, we employed experimental infection of mice with C. difficile. Antibiotic-treated mice were challenged with C. difficile and a similar panel of serum biomarkers was measured. Applying each model to the mouse cohort of severe and nonsevere CDI revealed AUCs of 0.59 (0.44 to 0.74), 0.96 (0.90 to 1.0), and 0.89 (0.81 to 0.97). In both human and murine CDI, models based on serum biomarkers predicted adverse CDI outcomes. Our results support the use of serum-based biomarker panels to inform Clostridioides difficile infection treatment. IMPORTANCE Each year in the United States, Clostridioides difficile causes nearly 500,000 gastrointestinal infections that range from mild diarrhea to severe colitis and death. The ability to identify patients at increased risk for severe disease or mortality at the time of diagnosis of C. difficile infection (CDI) would allow clinicians to effectively allocate disease modifying therapies. In this study, we developed models consisting of only a small number of serum biomarkers that are capable of predicting both 30-day all-cause mortality and adverse outcomes of patients at time of CDI diagnosis. We were able to validate these models through experimental mouse infection. This provides evidence that the biomarkers reflect the underlying pathophysiology and that our mouse model of CDI reflects the pathogenesis of human infection. Predictive models can not only assist clinicians in identifying patients at risk for severe CDI but also be utilized for targeted enrollment in clinical trials aimed at reduction of adverse outcomes from severe CDI.

The present study raises the need to investigate factors that could be implicated in the poor neurocognitive performance among the younger, less educated HIV+ individuals in Zambia. (PsycINFO Database Record

Background Many models have been developed to predict severe outcomes from Clostridioides difficile infection. These models are usually developed at a single institution and largely are not externally validated. This aim of this study was to validate previously published risk scores in a multicenter cohort of patients with CDI. Methods Retrospective study on four separate inpatient cohorts with CDI from three distinct sites: The Universities of Michigan (2010-2012 and 2016), Chicago (2012), and Wisconsin (2012). The primary composite outcome was admission to an intensive care unit, colectomy, and/or death attributed to CDI within 30 days of positive testing. Both within each cohort and combined across all cohorts, published CDI severity scores were assessed and compared to each other and the IDSA guideline definitions of severe and fulminant CDI. Results A total of 3,646 patients were included for analysis. Including the two IDSA guideline definitions, fourteen scores were assessed. Performance of scores varied within each cohort and in the combined set (mean area under the receiver operator characteristic curve(AUC 0.61, range 0.53-0.66). Only half of the scores had performance at or better than IDSA severe and fulminant definitions (AUCs 0.64 and 0.63, respectively). Most of the scoring systems had more false than true positives in the combined set (mean: 81.5%, range:0-91.5%). Conclusions No published CDI severity score showed stable, good predictive ability for adverse outcomes across multiple cohorts/institutions or in a combined multicenter cohort.

Background In patients with Clostridioides difficile infection (CDI), the relationship between clinical, microbial, and temporal/epidemiological trends relate and disease severity and adverse outcomes is incompletely understood. Here, in a follow-up to our study conducted in 2010–2013, we evaluate stool toxin levels and C. difficile PCR ribotypes. We hypothesized that elevated stool toxins and infection with ribotype 027 associate with severe disease and adverse outcomes. Methods In a cohort of 565 subjects at the University of Michigan with CDI diagnosed by positive testing for toxins A/B by EIA or PCR for the tcdB gene, we quantified stool toxin levels via a modified cell cytotoxicity assay, isolated C. difficile by anaerobic culture, and performed PCR ribotyping. Severe CDI was defined by IDSA criteria, and primary outcomes were all-cause 30-day mortality and a composite of colectomy, ICU admission, and/or death attributable to CDI within 30 days. Analyses included bivariable tests and adjusted logistic regression. Results 199 samples were diagnosed by EIA and 447 were diagnosed by PCR. Toxin positivity associated with IDSA severity, but not primary outcomes. In 2016, compared to 2010–2013, ribotype 106 newly emerged, accounting for 10.6% of strains, ribotype 027 fell from 16.5% to 9.3%, and ribotype 014-027 remained stable at 18.9%. Ribotype 014-020 associated with IDSA severity and 30-day mortality (P=.001). Conclusion Toxin positivity by EIA and CCA associated with IDSA severity, but not with subsequent adverse outcomes. The molecular epidemiology of C. difficile has shifted, and this may have implications for the optimal diagnostic strategy for and clinical severity of CDI.

……………………………………………………………………………… AbstractThis study used data from the 2013 Zambia Demographic Health Survey (ZDHS) based on a nationally representative sample carried out by Central Statistical Office of Zambia. This paper analyzed a special module designed to collect information on the extent of the uptake of HIV testing by sexually active young people in Zambia 131Copyright © International Association of African Researchers and Reviewers, 2006-2017: www.afrrevjo.net/ijah Indexed African Journals Online (AJOL) www.ajol.info 24 years. Overall 84% of females and 57% of males reported having tested for HIV. Regression analysis further showed that age, place of residence, work status, educational level, consistency of condom use and number of sex partners were significantly related to the uptake of HIV testing for both female and male participants. Young sexually active people should be availed affordable educational opportunities which in turn will hopefully accord them to viable economic opportunities. The media exposure to the young men and women should preach consistent condom use as well as a reduction in their sexual partners.

BackgroundAnnually in the US alone, Clostridioides difficile infection (CDI) afflicts nearly 500,000 patients causing 29,000 deaths. Since early and aggressive interventions could save lives but are not optimally deployed in all patients, numerous studies have published predictive models for adverse outcomes. These models are usually developed at a single institution, and largely are not externally validated. This aim of this study was to validate the predictability for severe CDI with previously published risk scores in a multicenter cohort of patients with CDI.MethodsWe conducted a retrospective study on four separate inpatient cohorts with CDI from three distinct sites: the Universities of Michigan (2010–2012 and 2016), Chicago (2012), and Wisconsin (2012). The primary composite outcome was admission to an intensive care unit, colectomy, and/or death attributed to CDI within 30 days of positive test. Structured query and manual chart review abstracted data from the medical record at each site. Published CDI severity scores were assessed and compared with each other and the IDSA guideline definition of severe CDI. Sensitivity, specificity, area under the receiver operator characteristic curve (AuROC), precision-recall curves, and net reclassification index (NRI) were calculated to compare models.ResultsWe included 3,775 patients from the four cohorts (Table 1) and evaluated eight severity scores (Table 2). The IDSA (baseline comparator) model showed poor performance across cohorts(Table 3). Of the binary classification models, including those that were most predictive of the primary composite outcome, Jardin, performed poorly with minimal to no NRI improvement compared with IDSA. The continuous score models, Toro and ATLAS, performed better, but the AuROC varied by site by up to 17% (Table 3). The Gujja model varied the most: from most predictive in the University of Michigan 2010–2012 cohort to having no predictive value in the 2016 cohort (Table 3).ConclusionNo published CDI severity score showed stable, acceptable predictive ability across multiple cohorts/institutions. To maximize performance and clinical utility, future efforts should focus on a multicenter-derived and validated scoring system, and/or incorporate novel biomarkers. Disclosures All authors: No reported disclosures.