pre existing characteristics associated with covid 19 illness severity CORD-Papers-2021-10-25 (Version 1)

Title: Pre-Existing Characteristics Associated with Covid-19 Illness Severity
Abstract: Background. Certain individuals, when infected by SARS-CoV-2, tend to develop more severe forms of Covid-19 illness for reasons that remain unclear. Objective. To determine the demographic and pre-existing clinical characteristics associated with increased severity of Covid-19 infection. Design. Retrospective observational study. We curated data from the electronic health record, and used multivariable logistic regression to examine the association of pre-existing traits with a Covid-19 illness severity defined by level of required care: need for hospital admission, need for intensive care, and need for intubation. Setting. A large, multihospital healthcare system in Southern California. Participants. All patients with confirmed Covid-19 infection (N=442). Results. Of all patients studied, 48% required hospitalization, 17% required intensive care, and 12% required intubation. In multivariable-adjusted analyses, patients requiring a higher levels of care were more likely to be older (OR 1.5 per 10 years, P<0.001), male (OR 2.0, P=0.001), African American (OR 2.1, P=0.011), obese (OR 2.0, P=0.021), with diabetes mellitus (OR 1.8, P=0.037), and with a higher comorbidity index (OR 1.8 per SD, P<0.001). Several clinical associations were more pronounced in younger compared to older patients (Pinteraction<0.05). Of all hospitalized patients, males required higher levels of care (OR 2.5, P=0.003) irrespective of age, race, or morbidity profile. Conclusion. In our healthcare system, greater Covid-19 illness severity is seen in patients who are older, male, African American, obese, with diabetes, and with greater overall comorbidity burden. Certain comorbidities paradoxically augment risk to a greater extent in younger patients. In hospitalized patients, male sex is the main determinant of needing more intensive care. Further investigation is needed to understand the mechanisms underlying these findings.
Published: 5/5/2020
DOI: 10.1101/2020.04.29.20084533
DOI_URL: http://doi.org/10.1101/2020.04.29.20084533
Author Name: Ebinger, J E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ebinger_j_e
Author Name: Achamallah, N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/achamallah_n
Author Name: Ji, H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ji_h
Author Name: Claggett, B L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/claggett_b_l
Author Name: Sun, N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/sun_n
Author Name: Botting, P
Author link: https://covid19-data.nist.gov/pid/rest/local/author/botting_p
Author Name: Nguyen, T T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/nguyen_t_t
Author Name: Luong, E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/luong_e
Author Name: Kim, E H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kim_e_h
Author Name: Park, E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/park_e
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Rosenberry, R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rosenberry_r
Author Name: Matusov, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/matusov_y
Author Name: Zhao, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhao_s
Author Name: Pedraza, I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/pedraza_i
Author Name: Zaman, T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zaman_t
Author Name: Thompson, M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/thompson_m
Author Name: Raedschelders, K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/raedschelders_k
Author Name: Berg, A H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/berg_a_h
Author Name: Grein, J D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/grein_j_d
Author Name: Noble, P W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/noble_p_w
Author Name: Chugh, S S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chugh_s_s
Author Name: Bairey Merz, C N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bairey_merz_c_n
Author Name: Marban, E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/marban_e
Author Name: Van Eyk, J E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/van_eyk_j_e
Author Name: Solomon, S D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/solomon_s_d
Author Name: Albert, C M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/albert_c_m
Author Name: Chen, P
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chen_p
Author Name: Cheng, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/cheng_s
sha: 563ad19a07217983143625bd3ea541f0350b5555
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: https://doi.org/10.1101/2020.04.29.20084533 http://medrxiv.org/cgi/content/short/2020.04.29.20084533v1?rss=1
has_full_text: TRUE
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Extracted Text Content in Record: First 5000 Characters:Background. Certain individuals, when infected by SARS-CoV-2, tend to develop more severe forms of Covid-19 illness for reasons that remain unclear. Objective. To determine the demographic and pre-existing clinical characteristics associated with increased severity of Covid-19 infection. The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now well recognized as the cause of the coronavirus disease 2019 (Covid-19) global pandemic. (1) (2) (3) The rate of rise in Covid-19 infection and its associated outcomes in the United States is now comparable to rates observed in other severely affected countries such as China, Italy, and Spain. (4) (5) (6) (7) (8) (9) (10) The spread of Covid-19 in the United States has been especially pronounced in the states of California, New York, Michigan, Louisiana, and Washington. (11) Consistently reported across all regions is the observation that, of all individuals who become infected with SARS-CoV-2, a majority tend to have mild or no symptoms; however, an important minority will develop predominantly respiratory disease that can lead to critical illness and death. (12) (13) (14) (15) Multiple, reports suggest that certain demographic and clinical characteristics may predispose infected persons to more severe manifestations of Covid-19, such as older age, male sex, and pre-existing hypertension, pulmonary disease, or cardiovascular disease. (4, (16) (17) (18) (19) Given that these traits tend to cluster among the same persons, the relative contribution of each trait to the risk for developing more severe presentations of Covid-19 illness remains unclear. We conducted a comprehensive investigation of the pre-existing demographic and clinical correlates of Covid-19 illness severity observed among patients evaluated for Covid-19 within our multi-site healthcare system in Los Angeles, California. We deliberately focused our study on pre-existing characteristics for two main reasons: first, we recognize that patients with Covid-19 illness can present early or late in the disease course, causing many clinical features to vary at the time of initial clinical encounter; and, second, we anticipate that ongoing public health efforts can be informed and augmented by understanding which predisposing factors may render certain segments of the population at higher risk for the most morbid sequelae of SARS-CoV-2 infection. All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which this version posted May 5, 2020. . https://doi.org/10.1101/2020.04.29.20084533 doi: medRxiv preprint The Cedars-Sinai Health System is located in Los Angeles, California with a diverse catchment area of 1.8 million individuals, 33% of whom are over the age of 45 For all patients considered to have Covid-19, based on direct or documented laboratory test result and suggestive signs and/or symptoms, we obtained data from the electronic health record (EHR) on the following demographic and clinical characteristics: age at the time of 6 diagnosis, sex, race, ethnicity, body mass index (BMI), smoking status, comorbidities (as coded by ICD-10), and vital signs and laboratory diagnostics assessed within 1 week prior to presentation. We conducted iterative quality control and quality assurance analyses on all data extracted directly from the EHR and used manual chart review to verify collected data on clinical characteristics where appropriate. To capture variation in relative comorbid status, in a way that is not captured by distinct medical history variables alone, we calculated the Elixhauser Comorbidity Index (ECI) with van Walraven weighting for all patients based on all available clinical data.(20-23) The ECI uses 31 categories to quantify a patient's burden of comorbid conditions and has been shown to outperform other indices in predicting adverse outcomes (Supplemental Table 1 ).(22-28) For patients admitted to the hospital, length of stay, admission to an intensive care unit (ICU) and death were ascertained from time stamps recorded for admission, unit transfers, and discharge. Interventions such as intubation and prone positioning were identified through time stamped orders in the EHR and verified by manual chart review. Dates and times of onset for reported or observed relevant signs and/or symptoms were also determined via manual chart review. All care was provided at the discretion of the treating physicians. Our outcomes for this study included: severe illness (defined as requiring any kind of hospital admission), critical illness (defined as the need for intensive care during hospitalization), and respiratory failure (defined as the need for intubation and mechanical ventilation). The CSMC institutional review board approved all protocols for the current study. For the total sample of Covid-19 patients, we used parametri
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