Impact of Inflammatory Response Modifiers on the Incidence of Hospital-Acquired Infections in Patients with COVID-19.

The study aim was to assess the influence of inflammatory response modifiers, including anti-interleukin-6 (IL-6) biologics and corticosteroids, on the incidence of hospital-acquired infections in patients with coronavirus disease 2019 (COVID-19). METHODS: Case-control study performed at a university hospital from February 26 to May 26, 2020. Cases were defined as patients with COVID-19 who developed hospital-acquired infections. For each case, two controls were selected among patients without infections. Cases and controls were matched obeying three criteria in a hierarchical sequence: length of hospital stay up until the first infection; comorbidity; and need for Intensive care unit (ICU) admission. Conditional logistic regression analysis was used to estimate the association of exposures with being a case. RESULTS: A total of 71 cases and 142 controls were included. Independent predictors for acquiring a hospital infection were chronic liver disease [odds ratio (OR) 16.56, 95% CI 1.87-146.5, p = 0.012], morbid obesity (OR 6.11, 95% CI 1.06-35.4, p = 0.043), current or past smoking (OR 4.15, 95% CI 1.45-11.88, p = 0.008), exposure to hydroxychloroquine (OR 0.2, 95% CI 0.041-1, p = 0.053), and invasive mechanical ventilation (OR 61.5, 95% CI 11.08-341, p ≤ 0.0001). CONCLUSIONS: Inflammatory response modifiers had no influence on acquisition of nosocomial infections in admitted patients with COVID-19. Hospital-acquired infections primarily occurred in the critically ill and invasive mechanical ventilation was the main exposure conferring risk

Clostridium difficile Infection in Special High-Risk Populations

<p>Article full text</p> <p>The full text of this article can be found here</p><p> <b><u>https://link.springer.com/article/10.1007/s40121-016-0124-z</u></b></p> <p>Provide enhanced content for this article</p> <p>If you are an author of this publication and would like to provide additional enhanced content for your article then please contact <a href="http://www.medengine.com/Redeem/”mailto:[email protected]”"><b>[email protected]</b></a>.</p> <p>The journal offers a range of additional features designed to increase visibility and readership. All features will be thoroughly peer reviewed to ensure the content is of the highest scientific standard and all features are marked as ‘peer reviewed’ to ensure readers are aware that the content has been reviewed to the same level as the articles they are being presented alongside. Moreover, all sponsorship and disclosure information is included to provide complete transparency and adherence to good publication practices. This ensures that however the content is reached the reader has a full understanding of its origin. No fees are charged for hosting additional open access content.</p> <p>Other enhanced features include, but are not limited to:</p> <ul> <li>Slide decks</li> <li>Videos and animations</li> <li>Audio abstracts</li> <li>Audio slides</li> </ul

Characteristics of Clostridium difficile infection in patients with discordant diagnostic test results

Background: Clinical features of Clostridium difficile infection (CDI) cases diagnosed by detection of polymerase chain reaction (PCR), with negative toxin enzyme immunoassay results (EIA) have not been fully elucidated. The purpose of this study was to determine the magnitude of CDI patients who had negative EIA toxin determinations but positive PCR tests, and their differences in clinical presentation. Methods: We performed a retrospective study comparing the clinical features of CDI cases detected by EIA (toxins A + B) with cases detected by PCR (toxin negative, PCR positive) over a 16-month period. Only patients with an initial Clostridium difficile infection episode that fulfilled a standardized definition were included. Results: During the study period, 107 episodes of CDI were detected. Seventy-four patients (69%) had positive glutamate dehydrogenase (GDH) antigen and EIA determinations (EIA positive patients). Thirty-three patients (31%) had GDH positive, negative toxin EIA and positive PCR determination (PCR positive patients). PCR positive patients were younger, 57 (27) years (mean [SD]), than EIA positive patients, 71 (16) years, (p < 0.001). Fewer PCR positive patients were receiving proton pump inhibitors (21 patients, 64%) than EIA positive patients (61 patients, 82%, p = 0.034). The clinical presentation was similar in both groups. In the multivariate analysis, lower age was identified as the only independent variable associated with PCR positive patients. Conclusions: One third of Clostridium difficile infection patients present negative toxin EIA and PCR positive tests. Performing PCR determination after the negative EIA test is more relevant in younger patients

Predicting critical illness on initial diagnosis of COVID-19 based on easily obtained clinical variables: development and validation of the PRIORITY model

Objectives: We aimed to develop and validate a prediction model, based on clinical history and examination findings on initial diagnosis of coronavirus disease 2019 (COVID-19), to identify patients at risk of critical outcomes. Methods: We used data from the SEMI-COVID-19 Registry, a cohort of consecutive patients hospitalized for COVID-19 from 132 centres in Spain (23rd March to 21st May 2020). For the development cohort, tertiary referral hospitals were selected, while the validation cohort included smaller hospitals. The primary outcome was a composite of in-hospital death, mechanical ventilation, or admission to intensive care unit. Clinical signs and symptoms, demographics, and medical history ascertained at presentation were screened using least absolute shrinkage and selection operator, and logistic regression was used to construct the predictive model. Results: There were 10 433 patients, 7850 in the development cohort (primary outcome 25.1%, 1967/7850) and 2583 in the validation cohort (outcome 27.0%, 698/2583). The PRIORITY model included: age, dependency, cardiovascular disease, chronic kidney disease, dyspnoea, tachypnoea, confusion, systolic blood pressure, and SpO2 ≤93% or oxygen requirement. The model showed high discrimination for critical illness in both the development (C-statistic 0.823; 95% confidence interval (CI) 0.813, 0.834) and validation (C-statistic 0.794; 95%CI 0.775, 0.813) cohorts. A freely available web-based calculator was developed based on this model (https://www.evidencio.com/models/show/2344). Conclusions: The PRIORITY model, based on easily obtained clinical information, had good discrimination and generalizability for identifying COVID-19 patients at risk of critical outcomes