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BACKGROUND: Anticoagulation (AC) utilization patterns and their predictors among hospitalized coronavirus disease 2019 (COVID-19) patients have not been well-described. METHODS: Using the National COVID Cohort Collaborative, we conducted a retrospective cohort study (2020-2022) to assess AC use patterns and identify factors associated with therapeutic AC employing modified Poisson regression. RESULTS: Among 162,842 hospitalized COVID-19 patients, 64% received AC and 24% received therapeutic AC. Therapeutic AC use declined from 32% in 2020 to 12% in 2022, especially after December 2021. Therapeutic AC predictors included age (relative risk (RR), 1.02 [95% confidence interval (CI), 1.02-1.02] per year), male (RR, 1.29 [1.27-1.32]), Non-Hispanic Black (RR, 1.16 [1.13-1.18]), obesity (RR, 1.48 [1.43-1.52]), increased length of stay (RR, 1.01; [1.01-1.01] per day), and invasive ventilation (RR, 1.64 [1.59-1.69]). Vaccination (RR, 0.88 [0.84-0.92]) and higher Charlson Comorbidity Index (CCI) (RR, 0.98 [0.97-0.98]) were associated with lower therapeutic AC. CONCLUSIONS: Overall, two thirds of hospitalized COVID-19 patients received any AC and a quarter received therapeutic dosing. Therapeutic AC declined after the introduction of the Omicron variant. Predictors of therapeutic AC included demographics, obesity, LOS, invasive ventilation, CCI, and vaccination, suggesting AC decisions driven by clinical factors including COVID-19 severity, bleeding risks, and comorbidities.
ABSTRACT
BACKGROUND: Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness. METHODS: Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenges encountered when constructing this dataset, beginning with an examination of data quality across data partners. We then discuss the methods and best practices used to operationalize several important study elements: exposure to treatment, baseline health comorbidities, and key outcomes of interest. RESULTS: We share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and quality. (1) The specific EHR data elements captured from a site can vary depending on source data model and practice. (2) Data missingness remains a significant issue. (3) Drug exposures can be recorded at different levels and may not contain route of administration or dosage information. (4) Reconstruction of continuous drug exposure intervals may not always be possible. (5) EHR discontinuity is a major concern for capturing history of prior treatment and comorbidities. Lastly, (6) access to EHR data alone limits the potential outcomes which can be used in studies. CONCLUSIONS: The creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including COVID-19. As with all observational research, it is important that research teams engage with appropriate domain experts to understand the data in order to define research questions that are both clinically important and feasible to address using these real world data.
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COVID-19 , Humans , Data Accuracy , COVID-19 Drug Treatment , Data CollectionABSTRACT
PURPOSE: Variation in risk of adverse clinical outcomes in patients with cancer and COVID-19 has been reported from relatively small cohorts. The NCATS' National COVID Cohort Collaborative (N3C) is a centralized data resource representing the largest multicenter cohort of COVID-19 cases and controls nationwide. We aimed to construct and characterize the cancer cohort within N3C and identify risk factors for all-cause mortality from COVID-19. METHODS: We used 4,382,085 patients from 50 US medical centers to construct a cohort of patients with cancer. We restricted analyses to adults ≥ 18 years old with a COVID-19-positive or COVID-19-negative diagnosis between January 1, 2020, and March 25, 2021. We followed N3C selection of an index encounter per patient for analyses. All analyses were performed in the N3C Data Enclave Palantir platform. RESULTS: A total of 398,579 adult patients with cancer were identified from the N3C cohort; 63,413 (15.9%) were COVID-19-positive. Most common represented cancers were skin (13.8%), breast (13.7%), prostate (10.6%), hematologic (10.5%), and GI cancers (10%). COVID-19 positivity was significantly associated with increased risk of all-cause mortality (hazard ratio, 1.20; 95% CI, 1.15 to 1.24). Among COVID-19-positive patients, age ≥ 65 years, male gender, Southern or Western US residence, an adjusted Charlson Comorbidity Index score ≥ 4, hematologic malignancy, multitumor sites, and recent cytotoxic therapy were associated with increased risk of all-cause mortality. Patients who received recent immunotherapies or targeted therapies did not have higher risk of overall mortality. CONCLUSION: Using N3C, we assembled the largest nationally representative cohort of patients with cancer and COVID-19 to date. We identified demographic and clinical factors associated with increased all-cause mortality in patients with cancer. Full characterization of the cohort will provide further insights into the effects of COVID-19 on cancer outcomes and the ability to continue specific cancer treatments.