IMPORTANCEThe National COVID Cohort Collaborative (N3C) is a centralized, harmonized, highgranularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.OBJECTIVES To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. DESIGN, SETTING, AND PARTICIPANTSIn a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). MAIN OUTCOMES AND MEASURESPatient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. RESULTSThe cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472(18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, (continued) Key Points Question In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity? Findings In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6%

In late 2015 and early 2016, 11 patients were identified with cultures positive for Elizabethkingia anophelis in our health system. All patients had positive blood cultures upon admission. Chart review showed that all had major comorbidities and recent health care exposure. The attributable mortality rate was 18.2%.

Poor immune status, the use of a vascular access different from an AV fistula, and intravenous drug use (IDU) may favor increased rates of vascular access infections among HIV infected patients on hemodialysis. Staphylococcus spp. and Streptococcus spp. are the main cause of these infections, but Gram-negative rods and fungi have been found as well. Using an AV fistula when possible, and eliciting a history of IVDU on every visit may prevent this type of infection. When infections are present, coverage for both Gram-positive and negative organisms is recommended. Additional studies specifically addressing the issue of vascular access infection in HIV infected patients are required.

As the COVID-19 pandemic progresses to an endemic phase, a greater number of patients with a history of COVID-19 will undergo surgery. Major adverse cardiovascular and cerebrovascular events (MACE) are the primary contributors to postoperative morbidity and mortality; however, studies assessing the relationship between a previous SARS-CoV-2 infection and postoperative MACE outcomes are limited. Here, we analyzed retrospective data from 457,804 patients within the N3C Data Enclave-the largest national, multi-institutional dataset on COVID-19 in the United States. 7.4% of patients had a history of COVID-19 prior to surgery. When controlling for comorbidities, age, race, and risk of surgery, patients with preoperative COVID-19 had an increased risk for 30-day postoperative MACE. MACE risk was influenced by an interplay between COVID-19 disease severity and time between surgery and infection; in those with mild disease, MACE risk was not increased even among those undergoing surgery within 4 weeks following infection. In those with moderate disease, risk for postoperative MACE was mitigated 8 weeks after infection, while patient with severe disease continued to have elevated postoperative MACE risk even after waiting 8 weeks. Being fully vaccinated decreased the risk for postoperative MACE in both patients with no history of COVID-19 and in those with breakthrough COVID-19 infection. Together, our results suggest that a thorough assessment of the severity, vaccination status, and timing of SARS-CoV-2 infection must be a mandatory part of perioperative stratification.