CEC-UW: COVID EHR COHORT at the University of Wisconsin
Study Details
Study Description
Brief Summary
This cohort study will obtain electronic health record (EHR) data (limited data set) from 21 health systems affiliated with the Cancer Center Cessation Initiative (C3I) network or health systems with large numbers of COVID-19 patients to explore whether smoking status, cancer history, and other risk factors among patients diagnosed with COVID-19 are associated with mortality and/or COVID-19 disease severity/complications. Each site will provide data from their health system EHR on a regular basis that includes all patients identified as having COVID-19 at some point in the interval from February 1, 2020, through January 31, 2022.
Condition or Disease | Intervention/Treatment | Phase |
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Detailed Description
This cohort study will obtain electronic health record (EHR) data (limited data set) from 21 health systems affiliated with the Cancer Center Cessation Initiative (C3I) network or health systems with large numbers of COVID-19 patients to explore whether smoking status, cancer history, and other risk factors among patients diagnosed with COVID-19 are associated with mortality and/or COVID-19 disease severity/complications. The Cancer Center Cessation Initiative (C3I) is a project launched by the US National Cancer Institute (NCI) to improve the rate at which NCI-designated Cancer Centers provide evidence-based smoking cessation to patients diagnosed with and treated for cancer. The C3I is coordinated at the University of Wisconsin-Center for Tobacco Research and Intervention (UW-CTRI) and the University of Wisconsin Carbone Cancer Center (UWCCC). Twenty-one health systems across the U.S. will provide EHR data to the UW-CTRI coordinating center on all COVID-19 patients identified during the period from February 1, 2020, through January 31, 2022.
Current EHR-based data elements collected will include:
Evidence of COVID-19: ICD-10-CM diagnosis of COVID-19, COVID-19 PCR lab test, and/or COVID-19 antigen lab test
Healthcare system encounter type: inpatient, outpatient, emergency department (ED), urgent care, or other
SES/Demographics variables: insurance status, education, housing status, sex, age, race/ethnicity, height, weight, body mass index
Comorbid diseases: chronic asthma, chronic COPD, chronic bronchiectasis, diabetes mellitus, cardiovascular disease, chronic renal disease, on dialysis, immunocompromised [due to SLE lupus, rheumatoid arthritis, organ transplant, HIV, Crohn's], pregnant, cancer (lymphomas, leukemias, lung/respiratory, rectal, breast, prostate, pancreas), hypertension, depression, anxiety, alcohol abuse, pro- clotting disorders, and anti-clotting disorders
Tobacco use variables: smoking status (current, former, never), passive smoke exposure for never smoker, years since quitting (for former smokers), packs smoked per day, years of smoking, pack years, smokeless tobacco user, and marijuana use
Signs and symptoms: temperature, pulse, systolic blood pressure, diastolic blood pressure, oxygen saturation, septic shock, pneumonia, chills, muscle aches/myalgia, rhinorrhea, sore throat, chronic cough, shortness of breath, nausea or vomiting, headache, abdominal pain, diarrhea, dizziness, impaired consciousness, acute cerebrovascular event, ataxia, seizure, taste impairment, smell impairment, vision impairment, nerve pain, and skeletal muscular pain
COVID-19 treatment variables: ICU admission, required supplemental oxygen, intubated for ventilator use, noninvasive positive pressure, and number of days hospitalized
COVID-19 medications including (but not limited to): chloroquine, hydroxychloroquine, tocilizumab, remdesivir, dexamethasone, convalescent plasma, and ascorbic acid
Classes of other medications used: nicotine replacement therapies (NRTs), varenicline medications; blood thinners, steroids, angiotensin converting enzyme (ACE) inhibitors, angiotensin-receptor blockers (ARBs), short-acting adrenergic bronchodilators, long-acting adrenergic bronchodilators, anticholinergic bronchodilators, bronchodilators combos, inhaled corticosteroids (includes combo medications), and inhaled corticosteroid bronchodilators
Lab tests: COVID-19 PCR test, COVID-19 Antigen test, Albumin, ALT, Bicarbonte, BUN, Calcium, C-Reactive Protein, High Sensitivity, C-Reactive Protein, Creatinine, D-DIMER, ESR, Ferritin, Hematocrit, HgbA1c., INR, LDH, Leukocytes, Platelet Count, Potassium, Procalcitonin, Sodium, Troponin-I, Troponin-T
For the initial paper(s) to be prepared based on these data, the main analytic methods will include GUIDE classification and regression tree models. However, whole sample methods will also be used as complementary analytic methods, which will vary with regard to outcome type: i.e., logistic regression for binary outcomes and Cox proportional hazard analyses for time-to-event outcomes. Initial analyses will focus on hospitalized COVID-19 patients. Later waves of analyses may use different analytic approaches and address different questions.
Participating healthcare systems:
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Duke University (Duke Health)
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Hackensack Meridian Health
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Mayo Clinic
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Memorial Sloan Kettering Cancer Center
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University of Michigan (Michigan Medicine)
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Mount Sinai Health System
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Northwestern University
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New York University (NYU Langone Health)
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University of California Davis (University of California Davis Comprehensive Cancer Center / UC Davis Health)
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University of California San Francisco
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University of North Carolina at Chapel Hill (UNC Health)
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University of Chicago
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University of Illinois at Chicago
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University of Kansas (University of Kansas Medical Center)
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University of Maryland
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University of Utah (University of Utah Health)
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University of Wisconsin (UW Health)
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Vanderbilt University (Vanderbilt University Medical Center)
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Virginia Commonwealth University (VCU Health System/Massey Cancer Center)
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Washington University St Louis
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Yale University (Yale New Haven Hospital)
Study Design
Arms and Interventions
Arm | Intervention/Treatment |
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Patients with COVID-19 Analyses of the cohort data will include (1) all patients, or (2) hospitalized patients meeting specific inclusion criteria. |
Outcome Measures
Primary Outcome Measures
- Mortality due to COVID-19 [February 1, 2020, through January 31, 2022]
In analyses of hospitalized patients, all-cause mortality in patients with COVID-19 illness vs discharge from hospital (binary outcome)
- COVID-19 Severity [February 1, 2020, through January 31, 2022]
In analyses of hospitalized patients, COVID-19 severity as measured by intubation for respiratory support (i.e., patient required intubation during hospitalization; binary outcome)
Eligibility Criteria
Criteria
Inclusion Criteria:
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COVID-19 ICD-10-CM diagnosis (U07.1 or J12.82) during a healthcare visit and/or
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COVID-19 positive PCR test and/or
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COVID-19 positive antigen test
Exclusion Criteria:
- N/A
Contacts and Locations
Locations
Site | City | State | Country | Postal Code | |
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1 | University of Wisconsin School of Medicine and Public Health Center for Tobacco Research and Intervention | Madison | Wisconsin | United States | 53711 |
Sponsors and Collaborators
- University of Wisconsin, Madison
- National Cancer Institute (NCI)
Investigators
- Principal Investigator: Betsy Rolland, PhD, MLIS, MPH, University of Wisconsin, Madison
- Principal Investigator: Michael C Fiore, MD, MPH, MBA, University of Wisconsin Center for Tobacco Research and Intervention
- Study Director: Karen L Conner, MPH, University of Wisconsin Center for Tobacco Research and Intervention
Study Documents (Full-Text)
None provided.More Information
Publications
- Loh WY, Man M, Wang S. Subgroups from regression trees with adjustment for prognostic effects and postselection inference. Stat Med. 2019 Feb 20;38(4):545-557. doi: 10.1002/sim.7677. Epub 2018 Apr 19.
- Loh, W.-Y. (2002). Regression trees with unbiased variable selection and interaction detection. Stat. Sinica, 12:361-386.
- Loh, W.-Y. (2011). Classification and regression trees. WIRES Data Min. Knowl., 1:14-23.
- Loh, W.-Y. and Zhou, P. (2020). The GUIDE approach to subgroup identification. In Ting, N., Cappelleri, J. C., Ho, S., and Chen, D.-G., editors, Design and analysis of Subgroups with Biopharmaceutical Applications, pages 147-165. Springer.
- Nolan MB, Piasecki TM, Smith SS, Baker TB, Fiore MC, Adsit RT, Bolt DM, Conner KL, Bernstein SL, Eng OD, Lazuk D, Gonzalez A, Hayes-Birchler T, Jorenby DE, D'Angelo H, Kirsch JA, Williams BS, Kent S, Kim H, Lubanski SA, Yu M, Suk Y, Cai Y, Kashyap N, Mathew J, McMahan G, Rolland B, Tindle HA, Warren GW, Abu-El-Rub N, An LC, Boyd AD, Brunzell DH, Carrillo VA, Chen LS, Davis JM, Deshmukh VG, Dilip D, Goldstein A, Ha PK, Iturrate E, Jose T, Khanna N, King A, Klass E, Lui M, Mermelstein RJ, Poon C, Tong E, Wilson KM, Theobald WE, Slutske WS. Relations of Current and Past Cancer with Severe Outcomes among 104,590 Hospitalized COVID-19 Patients: The COVID EHR Cohort at the University of Wisconsin. Cancer Epidemiol Biomarkers Prev. 2022 Aug 15. pii: EPI-22-0500. doi: 10.1158/1055-9965.EPI-22-0500. [Epub ahead of print]
- 2020-069
- OISE-20-66590-1
- A534253
- SMPH/MEDICINE
- CTRI