Glycemic Control and Clinical Outcomes in U.S. Patients With COVID-19: Data From the National COVID Cohort Collaborative (N3C) Database

OBJECTIVE: The purpose of the study is to evaluate the relationship between HbA1c and severity of coronavirus disease 2019 (COVID-19) outcomes in patients with type 2 diabetes (T2D) with acute COVID-19 infection. RESEARCH DESIGN AND METHODS: We conducted a retrospective study using observational data from the National COVID Cohort Collaborative (N3C), a longitudinal, multicenter U.S. cohort of patients with COVID-19 infection. Patients were \u3e /=18 years old with T2D and confirmed COVID-19 infection by laboratory testing or diagnosis code. The primary outcome was 30-day mortality following the date of COVID-19 diagnosis. Secondary outcomes included need for invasive ventilation or extracorporeal membrane oxygenation (ECMO), hospitalization within 7 days before or 30 days after COVID-19 diagnosis, and length of stay (LOS) for patients who were hospitalized. RESULTS: The study included 39,616 patients (50.9% female, 55.4% White, 26.4% Black or African American, and 16.1% Hispanic or Latino, with mean +/- SD age 62.1 +/- 13.9 years and mean +/- SD HbA1c 7.6% +/- 2.0). There was an increasing risk of hospitalization with incrementally higher HbA1c levels, but risk of death plateaued at HbA1c \u3e 8%, and risk of invasive ventilation or ECMO plateaued \u3e9%. There was no significant difference in LOS across HbA1c levels. CONCLUSIONS: In a large, multicenter cohort of patients in the U.S. with T2D and COVID-19 infection, risk of hospitalization increased with incrementally higher HbA1c levels. Risk of death and invasive ventilation also increased but plateaued at different levels of glycemic control

Fibroblast Growth Factor 23 Associates with Death in Critically Ill Patients

BACKGROUND AND OBJECTIVES: Dysregulated mineral metabolism is a common and potentially maladaptive feature of critical illness, especially in patients with AKI, but its association with death has not been comprehensively investigated. We sought to determine whether elevated plasma levels of the osteocyte-derived, vitamin D-regulating hormone, fibroblast growth factor 23 (FGF23), are prospectively associated with death in critically ill patients with AKI requiring RRT, and in a general cohort of critically ill patients with and without AKI. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We measured plasma FGF23 and other mineral metabolite levels in two cohorts of critically ill patients (n=1527). We included 817 patients with AKI requiring RRT who enrolled in the ARF Trial Network (ATN) study, and 710 patients with and without AKI who enrolled in the Validating Acute Lung Injury biomarkers for Diagnosis (VALID) study. We hypothesized that higher FGF23 levels at enrollment are independently associated with higher 60-day mortality. RESULTS: In the ATN study, patients in the highest compared with lowest quartiles of C-terminal (cFGF23) and intact FGF23 (iFGF23) had 3.84 (95% confidence interval, 2.31 to 6.41) and 2.08 (95% confidence interval, 1.03 to 4.21) fold higher odds of death, respectively, after adjustment for demographics, comorbidities, and severity of illness. In contrast, plasma/serum levels of parathyroid hormone, vitamin D metabolites, calcium, and phosphate were not associated with 60-day mortality. In the VALID study, patients in the highest compared with lowest quartiles of cFGF23 and iFGF23 had 3.52 (95% confidence interval, 1.96 to 6.33) and 1.93 (95% confidence interval, 1.12 to 3.33) fold higher adjusted odds of death. CONCLUSIONS: Higher FGF23 levels are independently associated with greater mortality in critically ill patients

The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction

The majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy. In a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics; we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen <1%) as well as 1,133,848 adult patients that served as lab-negative controls. Among 32,472 hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March/April 2020 to 8.6% in September/October 2020 (p = 0.002 monthly trend). In a multivariable logistic regression model, age, male sex, liver disease, dementia, African-American and Asian race, and obesity were independently associated with higher clinical severity. To demonstrate the utility of the N3C cohort for analytics, we used machine learning (ML) to predict clinical severity and risk factors over time. Using 64 inputs available on the first hospital day, we predicted a severe clinical course (death, discharge to hospice, invasive ventilation, or extracorporeal membrane oxygenation) using random forest and XGBoost models (AUROC 0.86 and 0.87 respectively) that were stable over time. The most powerful predictors in these models are patient age and widely available vital sign and laboratory values. The established expected trajectories for many vital signs and laboratory values among patients with different clinical severities validates observations from smaller studies, and provides comprehensive insight into COVID-19 characterization in U.S. patients. This is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease