COVID EHR COHORT at the University of Wisconsin : COVID EHR COHORT at the University of Wisconsin

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 testHealthcare system encounter type: inpatient, outpatient, emergency department (ED), urgent care, or otherSES/Demographics variables: insurance status, education, housing status, sex, age, race/ethnicity, height, weight, body mass indexComorbid 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 disordersTobacco 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 useSigns 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 painCOVID-19 treatment variables: ICU admission, required supplemental oxygen, intubated for ventilator use, noninvasive positive pressure, and number of days hospitalizedCOVID-19 medications including (but not limited to): chloroquine, hydroxychloroquine, tocilizumab, remdesivir, dexamethasone, convalescent plasma, and ascorbic acidClasses 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 bronchodilatorsLab 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-TFor 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:Duke University (Duke Health)Hackensack Meridian HealthMayo ClinicMemorial Sloan Kettering Cancer CenterUniversity of Michigan (Michigan Medicine)Mount Sinai Health SystemNorthwestern UniversityNew York University (NYU Langone Health)University of California Davis (University of California Davis Comprehensive Cancer Center / UC Davis Health)University of California San FranciscoUniversity of North Carolina at Chapel Hill (UNC Health)University of ChicagoUniversity of Illinois at ChicagoUniversity of Kansas (University of Kansas Medical Center)University of MarylandUniversity of Utah (University of Utah Health)University of Wisconsin (UW Health)Vanderbilt University (Vanderbilt University Medical Center)Virginia Commonwealth University (VCU Health System/Massey Cancer Center)Washington University St LouisYale University (Yale New Haven Hospital).

Medienart:	Klinische Studie

Erscheinungsjahr:	2022
Erschienen:	2022

Enthalten in:	ClinicalTrials.gov - (2022) vom: 31. Aug. Zur Gesamtaufnahme - year:2022

Sprache:	Englisch

Links:	Volltext [kostenfrei]

Themen:	610 COVID-19 Cardiovascular Diseases Medical Condition: COVID-19, Cancer, Nicotine Dependence, Pulmonary Disease, Cardiovascular Diseases, Immunosuppression Disorders Recruitment Status: Completed Study Type: Observational Tobacco Use Disorder

Anmerkungen:	Source: Link to the current ClinicalTrials.gov record., First posted: August 10, 2020, Last downloaded: ClinicalTrials.gov processed this data on September 05, 2022, Last updated: September 07, 2022

Study ID:

NCT04506528 2020-0609 OISE-20-66590-1 A534253 SMPH/MEDICINE CTRI

Veröffentlichungen zur Studie:

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 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. 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 AO, 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. 2023 Jan 9;32(1):12-21. doi: 10.1158/1055-9965.EPI-22-0500.

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PPN (Katalog-ID):	CTG003482588