ABSTRACT
Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to predict patient/cluster assignment over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.
Subject(s)
COVID-19 , Acute DiseaseABSTRACT
This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing COVID-19 hospitalization rates. From an eligible population of 632,822 COVID-19 patients seen at 33 clinical sites across the United States between December 23, 2021 and December 31, 2022, patients were matched across observed treatment groups, yielding an analytical sample of 410,642 patients patients. We estimate a 65% reduced odds of hospitalization among Paxlovid-treated patients within a 28-day follow-up period, and this effect did not vary by patient vaccination status. Notably, we observe disparities in Paxlovid treatment, with lower rates among Black and Hispanic or Latino patients, and within socially vulnerable communities. Ours is the largest study of Paxlovid's real-world effectiveness to date, and our primary findings are consistent with previous randomized control trials and real-world studies.
Subject(s)
COVID-19ABSTRACT
BackgroundAcute kidney injury (AKI) is associated with mortality in patients hospitalized with COVID-19, however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied. MethodsElectronic health record data were obtained from 53 health systems in the United States (US) in the National COVID Cohort Collaborative (N3C). We selected hospitalized adults diagnosed with COVID-19 between March 6th, 2020, and January 6th, 2022. AKI was determined with serum creatinine (SCr) and diagnosis codes. Time were divided into 16-weeks (P1-6) periods and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality. ResultsOut of a total cohort of 306,061, 126,478 (41.0 %) patients had AKI. Among these, 17.9% lacked a diagnosis code but had AKI based on the change in SCr. Similar to patients coded for AKI, these patients had higher mortality compared to those without AKI. The incidence of AKI was highest in P1 (49.3%), reduced in P2 (40.6%), and relatively stable thereafter. Compared to the Midwest, the Northeast, South, and West had higher adjusted AKI incidence in P1, subsequently, the South and West regions continued to have the highest relative incidence. In multivariable models, AKI defined by either SCr or diagnostic code, and the severity of AKI was associated with mortality. ConclusionsUncoded cases of COVID-19-associated AKI are common and associated with mortality. The incidence and distribution of COVID-19-associated AKI have changed since the first wave of the pandemic in the US.
Subject(s)
COVID-19 , Kidney DiseasesABSTRACT
Objective To define pregnancy episodes and estimate gestational aging within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS) and applied it to EHR data in the N3C from 1 January 2018 to 7 April 2022. HIPPS combines: 1) an extension of a previously published pregnancy episode algorithm, 2) a novel algorithm to detect gestational aging-specific signatures of a progressing pregnancy for further episode support, and 3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated three types of pregnancy cohorts based on the level of precision for gestational aging and pregnancy outcomes for comparison of COVID-19 and other characteristics. Results We identified 628,165 pregnant persons with 816,471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, spontaneous abortions), and 23.3% had unknown outcomes. We were able to estimate start dates within one week of precision for 431,173 (52.8%) episodes. 66,019 (8.1%) episodes had incident COVID-19 during pregnancy. Across varying COVID-19 cohorts, patient characteristics were generally similar though pregnancy outcomes differed. Discussion HIPPS provides support for pregnancy-related variables based on EHR data for researchers to define pregnancy cohorts. Our approach performed well based on clinician validation. Conclusion We have developed a novel and robust approach for inferring pregnancy episodes and gestational aging that addresses data inconsistency and missingness in EHR data.