Red Blood Cell Distribution Width (RDW) Predicts COVID-19 Severity: A Prospective, Observational Study from the Cincinnati SARS-CoV-2 Emergency Department Cohort

Since previous evidence has demonstrated that red blood cell distribution width (RDW) may be a useful prognostic parameter in many critical illnesses and infectious diseases, we investigated the utility of RDW for monitoring patients with coronavirus disease 2019 (COVID-19). The study population consisted of 49 COVID-19 patients, including 16 (32.6%) with severe illness, 12 (24.5%) with severe acute kidney injury (AKI), and 8 (16.3%) requiring renal replacement therapy (RRT). The predictive value of blood tests, performed during emergency department evaluation, was then addressed. A progressive increase of RDW was observed with advancing COVID-19 severity. The area under the curve (AUC) of RDW was 0.73 for predicting severe illness, 0.80 for severe AKI, and 0.83 for RRT, respectively. In multivariate analysis, elevated RDW was associated with 9-fold and 16-fold increased odds of severe COVID-19 and AKI, respectively. The results of this study suggest that RDW should be part of routine laboratory assessment and monitoring of COVID-19

The khmer software package: enabling efficient nucleotide sequence analysis

The khmer package is a freely available software library for working efficiently with fixed length DNA words, or k-mers. khmer provides implementations of a probabilistic k-mer counting data structure, a compressible De Bruijn graph representation, De Bruijn graph partitioning, and digital normalization. khmer is implemented in C++ and Python, and is freely available under the BSD license at https://github.com/dib-lab/khmer/

The khmer software package: enabling efficient nucleotide sequence analysis [version 1; referees: 2 approved, 1 approved with reservations]

The khmer package is a freely available software library for working efficiently with fixed length DNA words, or k-mers. khmer provides implementations of a probabilistic k-mer counting data structure, a compressible De Bruijn graph representation, De Bruijn graph partitioning, and digital normalization. khmer is implemented in C++ and Python, and is freely available under the BSD license at https://github.com/dib-lab/khmer/

Circulating plasma levels of angiotensin II and aldosterone in patients with coronavirus disease 2019 (COVID-19): A preliminary report

Circulating plasma levels of angiotensin II and aldosterone in patients with coronavirus disease 2019 (COVID-19): A preliminary repor

False negative RT-PCR or false positive serological testing in SARS-CoV-2 diagnostics? Navigating between Scylla and Charybdis to prevent misclassification bias in COVID-19 clinical investigations

False negative RT-PCR or false positive serological testing in SARS-CoV-2 diagnostics? Navigating between Scylla and Charybdis to prevent misclassification bias in COVID-19 clinical investigation

Validation of the Corona-Score for rapid identification of SARS-CoV-2 infections in patients seeking emergency department care in the United States

Corona-Score displays lower AUC (0.74 vs. 0.91) and sensitivity (82% vs. 96%), but slightly higher specificity (96% vs. 95%) in our US cohort of patients seeking ED care compared to a Dutch cohort, which may be at least in part attributable to the different demographic characteristics of our population, the different organization of the national healthcare system and the care access in the US. While it seems unlikely that this scoring system would offer such a high diagnostic accuracy to completely replace NAATs, it may still serve as practical adjunct for adjusting pre- and post-test probabilities. Moreover, it may prove an important tool for screening of sick control groups in COVID-19 clinical studies

Gastrointestinal symptoms associated with severity of coronavirus disease 2019 (COVID-19): a pooled analysis

Gastrointestinal symptoms associated with severity of coronavirus disease 2019 (COVID-19): a pooled analysis

Combined Cytokine Scores Assessed at Emergency Department Presentation Predicts COVID-19 Critical Illness

We studied 50 COVID-19 patients (60% males; median age, 50.5 years, interquartile range, 40.5-66.0 years), 32 (64%) requiring hospitalization within 30 days of emergency department (ED visit), 14 (28%) requiring intensive care unit (ICU) admission, and 8 (16%) requiring renal replacement therapy (RRT). The results are shown in Figure 1. Both IL-6*IL-10 and IL-6*IL-8*IL-10 scores displayed similar predictive performance across the outcomes. IL-6*IL-10 displayed the most optimal performance for predicting the primary outcome (ICU admission) with an AUC of 0.89 (95%CI: 0.78 – 0.99).We found an [IL-6]×[IL-10] area under the curve (AUC) of 0.89 for predicting ICU admission, identical to that reported by Nagant et al. Given that [IL-6]×[IL-10] and [IL-6]×[IL-8]×[IL-10] displayed similar predictive performance, we suggest the use of [IL-6]×[IL-10] score, as it requires only 2 variables and is simpler to calculate, as well as more cost-effective. The combined use of IL-6 and IL-10 enables identification of patients with predominant hyperinflammatory response, as well as those who with predominant hypoinflammatory response, both conditions which significantly contribute to development of severe disease

Assessment of immune response against SARS-CoV-2 at emergency department evaluation in COVID-19 patients

The results of our study further highlight the concept that serology testing should not be used as a surrogate for direct RNA identification in diagnosing acute SARS-CoV-2 infection, especially at ED evaluation. Relying on serology by assaying either IgA or IgG would generate an unacceptable number of false negative test results, which may contribute to the risk of propagating COVID-19 within healthcare facilities

Circulating Plasminogen Concentration at Admission in Patients with Coronavirus Disease 2019 (COVID-19)

Circulating Plasminogen Concentration at Admission in Patients with Coronavirus Disease 2019 (COVID-19