ABSTRACT
Background: Acute kidney injury (AKI) is a common complication of patients hospitalized with coronavirus disease 2019 (COVID-19), however, the epidemiological studies are limited by single or few centers and short duration. How the incidence of COVID-19-associated AKI has changed over the last 18 months since start of the pandemic is not known. Methods: We used the N3C enclave to collect data from 42 centers from all geographical regions of the United States of patients hospitalized with COVID-19 from December 2019 to May 2021. Unique patient visit occurrence ID data across various hospitalizations for each center was harmonized to uniformly collect information on serum creatinine (SCr), acute dialysis, end-stage kidney disease (ESKD) and transplantation. From a total of 127,223 patients hospitalized with COVID-19, 3,662 patients with preexisting ESKD and 20,090 with < 2 measures of SCr were excluded. AKI and AKI stages were defined by KDIGO criteria. Baseline SCr was defined from the outpatient values before hospitalization when available or lowest inpatient value if not available. We analyzed how the incidence of in-hospital AKI changed over time (every 4-month period). Mann-Kendall Test was used to test for monotonic trends of the AKI incidence. Results: Of the 103,471 patients hospitalized with COVID-19, 31,634 (30.6%) were diagnosed with AKI (mean age 63.3 years, 43.7% female, 32.4% non-white, and 19.5% Hispanic). 14,129 (13.7%) patients were diagnosed with AKI-1, 7,996 (7.7%) had AKI-2 and 9,509 (9.2%) patients had AKI-3 (6,285 [6.1%] without dialysis and 3,224 [3.1%] with dialysis). The incidence of 'all AKI' decreased from 38.8% in Dec 2019-March 2020 to 26.2% in March-May 2021 (p-value for trend = 0.086) and the incidence of AKI-3 declined from 15.5% to 6.5% (p = 0.086). Conclusions: This is the largest and most nationally representative cohort of patients hospitalized with COVID-19 with the highest number of cases of AKI and of AKI-3 reported thus far. The incidence of COVID-19-associated AKI has shown a nonstatistically significant decline during the past 18 months of the pandemic.
ABSTRACT
Driven by the use cases of PubChemRDF and SCAIView, we have developed a first community-based clinical trial ontology (CTO) by following the OBO Foundry principles. CTO uses the Basic Formal Ontology (BFO) as the top level ontology and reuses many terms from existing ontologies. CTO has also defined many clinical trial-specific terms. The general CTO design pattern is based on the PICO framework together with two applications. First, the PubChemRDF use case demonstrates how a drug Gleevec is linked to multiple clinical trials investigating Gleevec’s related chemical compounds. Second, the SCAIView text mining engine shows how the use of CTO terms in its search algorithm can identify publications referring to COVID-19-related clinical trials. Future opportunities and challenges are discussed. Copyright © 2020 for this paper by its authors.