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      Relevance of comorbidities for main outcomes during different periods of the COVID‐19 pandemic

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          Abstract

          Background

          Throughout the evolution of the COVID‐19 pandemic, the severity of the disease has varied. The aim of this study was to determine how patients' comorbidities affected and were related to, different outcomes during this time.

          Methods

          Retrospective cohort study of all patients testing positive for SARS‐CoV‐2 infection between March 1, 2020, and January 9, 2022. We extracted sociodemographic, basal comorbidities, prescribed treatments, COVID‐19 vaccination data, and outcomes such as death and admission to hospital and intensive care unit (ICU) during the different periods of the pandemic. We used logistic regression to quantify the effect of each covariate in each outcome variable and a random forest algorithm to select the most relevant comorbidities.

          Results

          Predictors of death included having dementia, heart failure, kidney disease, or cancer, while arterial hypertension, diabetes, ischemic heart, cerebrovascular, peripheral vascular diseases, and leukemia were also relevant. Heart failure, dementia, kidney disease, diabetes, and cancer were predictors of adverse evolution (death or ICU admission) with arterial hypertension, ischemic heart, cerebrovascular, peripheral vascular diseases, and leukemia also relevant. Arterial hypertension, heart failure, diabetes, kidney, ischemic heart diseases, and cancer were predictors of hospitalization, while dyslipidemia and respiratory, cerebrovascular, and peripheral vascular diseases were also relevant.

          Conclusions

          Preexisting comorbidities such as dementia, cardiovascular and renal diseases, and cancers were those most related to adverse outcomes. Of particular note were the discrepancies between predictors of adverse outcomes and predictors of hospitalization and the fact that patients with dementia had a lower probability of being admitted in the first wave.

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          Most cited references34

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          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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            Random Forests

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              The meaning and use of the area under a receiver operating characteristic (ROC) curve.

              A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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                Author and article information

                Contributors
                irantzu.barrio@ehu.eus
                Journal
                Influenza Other Respir Viruses
                Influenza Other Respir Viruses
                10.1111/(ISSN)1750-2659
                IRV
                Influenza and Other Respiratory Viruses
                John Wiley and Sons Inc. (Hoboken )
                1750-2640
                1750-2659
                16 January 2024
                January 2024
                : 18
                : 1 ( doiID: 10.1111/irv.v18.1 )
                : e13240
                Affiliations
                [ 1 ] Research Unit, Osakidetza Basque Health Service Galdakao‐Usansolo University Hospital Galdakao Spain
                [ 2 ] Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) Barakaldo Spain
                [ 3 ] Health Service Research Network on Chronic Diseases (REDISSEC) Bilbao Spain
                [ 4 ] Kronikgune Institute for Health Services Research Barakaldo Spain
                [ 5 ] Basque Center for Applied Mathematics, BCAM, Organization and Evaluation Bilbao Spain
                [ 6 ] Osakidetza Basque Health Service Sub‐Directorate for Primary Care Coordination Vitoria‐Gasteiz Spain
                [ 7 ] Biocruces Bizkaia Health Research Institute Barakaldo Spain
                [ 8 ] Basque Government Department of Health Office of Healthcare Planning Vitoria‐Gasteiz Spain
                [ 9 ] Department of Mathematics University of the Basque Country UPV/EHU Leioa Spain
                Author notes
                [*] [* ] Correspondence

                Irantzu Barrio, Department of Mathematics, University of the Basque Country UPV/EHU, Leioa, Spain.

                Email: irantzu.barrio@ 123456ehu.eus

                Author information
                https://orcid.org/0000-0003-2170-7876
                https://orcid.org/0000-0003-0648-5769
                Article
                IRV13240
                10.1111/irv.13240
                10790186
                38229871
                93e797b5-b429-4a90-9794-a9af88e49292
                © 2024 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 02 October 2023
                : 12 July 2023
                : 03 December 2023
                Page count
                Figures: 0, Tables: 3, Pages: 9, Words: 6178
                Funding
                Funded by: Agencia Estatal de Investigación , doi 10.13039/501100011033;
                Award ID: PID2020‐115882RB‐I00/AEI/10.13039/501100011033
                Funded by: Hezkuntza, Hizkuntza Politika Eta Kultura Saila, Eusko Jaurlaritza , doi 10.13039/100015866;
                Award ID: IT1456‐22
                Funded by: Instituto de Salud Carlos III , doi 10.13039/501100004587;
                Award ID: RD16/0001/0001
                Award ID: RD21CIII/0003/0017
                Funded by: Ministerio de Ciencia e Innovación , doi 10.13039/501100004837;
                Award ID: CEX2021‐001142‐S/MICIN/AEI/10.13039/501100011
                Funded by: Galdakao‐Barrualde Health Organization
                Funded by: Kronikgune Institute for Health Service Research
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                January 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.6 mode:remove_FC converted:16.01.2024

                Infectious disease & Microbiology
                comorbidity,covid‐19,healthcare,outcome assessment,patient acuity

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