bims-covirf Biomed News
on COVID19 risk factors
Issue of 2020‒08‒16
nine papers selected by
Catherine Rycroft
BresMed
  1. Epidemiology of COVID-19: A Systematic Review and Meta-analysis of Clinical Characteristics, Risk factors and Outcomes.
  2. Risk factors of severe cases with COVID-19: a meta-analysis.
  3. Risk factors for severe illness in hospitalized Covid-19 patients at a regional hospital.
  4. Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19.
  5. COVID-19 outcomes, risk factors and associations by race: a comprehensive analysis using electronic health records data in Michigan Medicine.
  6. COVID-19 Clinical Characteristics, and Sex-Specific Risk of Mortality: Systematic Review and Meta-Analysis.
  7. Comorbidities and Mortality in Patients With COVID-19 Aged 60 Years and Older in a University Hospital in Spain.
  8. SARS-CoV-2 viral load predicts COVID-19 mortality.
  9. Overweight, obesity, and risk of hospitalization for COVID-19: A community-based cohort study of adults in the United Kingdom.
  1. J Med Virol. 2020 Aug 13.
    Epidemiology of COVID-19: A Systematic Review and Meta-analysis of Clinical Characteristics, Risk factors and Outcomes.
    Jie Li, Daniel Q Huang, Biyao Zou, Hongli Yang, Wan Zi Hui, Fajuan Rui, Natasha Tang Sook Yee, Chuanli Liu, Sanjna Nilesh Nerurkar, Justin Chua Ying Kai, Margaret Li Peng Teng, Xiaohe Li, Hua Zeng, John A Borghi, Linda Henry, Ramsey Cheung, Mindie H Nguyen.
      BACKGROUND: COVID-19 has become a pandemic, but its reported characteristics and outcomes vary greatly amongst studies.OBJECTIVES: We determined pooled estimates for clinical characteristics and outcomes in COVID-19 patients including subgroups by disease severity (based on WHO Interim Guidance Report or IDSA/ATS criteria) and by country/region.
    METHODS: We searched Pubmed, Embase, Scopus, Cochrane, Chinese Medical Journal, and preprint databases from January 1, 2020 to April 6, 2020. Studies of laboratory confirmed COVID-19 patients with relevant data were included. Two reviewers independently performed study selection and data extraction.
    RESULTS: From 6,007 articles, 212 studies from 11 countries/regions involving 281,461 individuals were analyzed. Overall, mean age was 46.7 years, 51.8% were male, 22.9% had severe disease, and mortality was 5.6%. Underlying immunosuppression, diabetes, and malignancy were most strongly associated with severe COVID-19 (coefficient=53.9, 23.4, 23.4, respectively, all p<0.0007), while older age, male gender, diabetes, and hypertension were also associated with higher mortality (coefficient=0.05 per year, 5.1, 8.2, 6.99, respectively, p=0.006 to 0.0002). Gastrointestinal (nausea, vomiting, abdominal pain) and respiratory symptoms (shortness of breath, chest pain) were associated with severe COVID-19, while pneumonia and end organ failure were associated with mortality.
    CONCLUSION: COVID-19 is associated with a severe disease course in about 23% and mortality in about 6% of infected persons. Individuals with comorbidities and clinical features associated with severity should be monitored closely, and preventive efforts should especially target those with diabetes, malignancy and immunosuppression. This article is protected by copyright. All rights reserved.
    Keywords:  COVID-19; clinical characteristics; mortality; risk factors; severe
    DOI:  https://doi.org/10.1002/jmv.26424
  2. Epidemiol Infect. 2020 Aug 12. 1-23
    Risk factors of severe cases with COVID-19: a meta-analysis.
    Mingchun Ou, Jieyun Zhu, Pan Ji, Hongyuan Li, Zhimei Zhong, Bocheng Li, Jielong Pang, Jianfeng Zhang, Xiaowen Zheng.
      
    DOI:  https://doi.org/10.1017/S095026882000179X
  3. PLoS One. 2020 ;15(8): e0237558
    Risk factors for severe illness in hospitalized Covid-19 patients at a regional hospital.
    Justin J Turcotte, Barry R Meisenberg, James H MacDonald, Nandakumar Menon, Marcia B Fowler, Michaline West, Jane Rhule, Sadaf S Qureshi, Eileen B MacDonald.
      BACKGROUND: The Covid-19 pandemic threatens to overwhelm scarce clinical resources. Risk factors for severe illness must be identified to make efficient resource allocations.OBJECTIVE: To evaluate risk factors for severe illness.
    DESIGN: Retrospective, observational case series.
    SETTING: Single-institution.
    PARTICIPANTS: First 117 consecutive patients hospitalized for Covid-19 from March 1 to April 12, 2020.
    EXPOSURE: None.
    MAIN OUTCOMES AND MEASURES: Intensive care unit admission or death.
    RESULTS: In-hospital mortality was 24.8% and average total length of stay was 11.82 days (95% CI: 10.01 to 13.63 days). 30.8% of patients required intensive care unit admission and 29.1% required mechanical ventilation. Multivariate regression identified the amount of supplemental oxygen required at admission (OR: 1.208, 95% CI: 1.011-1.443, p = .037), sputum production (OR: 6.734, 95% CI: 1.630-27.812, p = .008), insulin dependent diabetes mellitus (OR: 11.873, 95% CI: 2.218-63.555, p = .004) and chronic kidney disease (OR: 4.793, 95% CI: 1.528-15.037, p = .007) as significant risk factors for intensive care unit admission or death. Of the 48 patients who were admitted to the intensive care unit or died, this occurred within 3 days of arrival in 42%, within 6 days in 71%, and within 9 days in 88% of patients.
    CONCLUSIONS: At our regional medical center, patients with Covid-19 had an average length of stay just under 12 days, required ICU care in 31% of cases, and had a 25% mortality rate. Patients with increased sputum production and higher supplemental oxygen requirements at admission, and insulin dependent diabetes or chronic kidney disease may be at increased risk for severe illness. A model for predicting intensive care unit admission or death with excellent discrimination was created that may aid in treatment decisions and resource allocation. Early identification of patients at increased risk for severe illness may lead to improved outcomes in patients hospitalized with Covid-19.
    DOI:  https://doi.org/10.1371/journal.pone.0237558
  4. Radiology. 2020 Aug 13. 202723
    Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19.
    S Schalekamp, M Huisman, R A van Dijk, M F Boomsma, P J Freire Jorge, W S de Boer, G J M Herder, M Bonarius, O A Groot, E Jong, A Schreuder, C M Schaefer-Prokop.
      Background The prognosis of hospitalized patients with severe coronavirus disease 2019 (COVID-19) is difficult to predict, while the capacity of intensive care units (ICUs) is a limiting factor during the peak of the pandemic and generally dependent on a country's clinical resources. Purpose To determine the value of chest radiographic findings together with patient history and laboratory markers at admission to predict critical illness in hospitalized patients with COVID-19. Material and Methods In this retrospective study including patients from 7th March 2020 to 24th April 2020, a consecutive cohort of hospitalized patients with RT-PCR-confirmed COVID-19 from two large Dutch community hospitals was identified. After univariable analysis, a risk model to predict critical illness (i.e. death and/or ICU admission with invasive ventilation) was developed, using multivariable logistic regression including clinical, CXR and laboratory findings. Distribution and severity of lung involvement was visually assessed using an 8-point scale (chest radiography score). Internal validation was performed using bootstrapping. Performance is presented as an area under the receiver operating characteristic curve (AUC). Decision curve analysis was performed, and a risk calculator was derived. Results The cohort included 356 hospitalized patients (69 ±12 years, 237 male) of whom 168 (47%) developed critical illness. The final risk model's variables included gender, chronic obstructive lung disease, symptom duration, neutrophil count, C-reactive protein level, lactate dehydrogenase level, distribution of lung disease and chest radiography score at hospital presentation. The AUC of the model was 0.77 (95% CI: 0.72-0.81, P < .001). A risk calculator was derived for individual risk assessment; Dutch COVID-19 risk model (see Appendix E2). At an example threshold of 0.70, 71 of 356 patients would be predicted to develop critical illness of which 59 (83%) would be true-positives. Conclusion A risk model based on chest radiographic and laboratory findings obtained at admission was predictive of critical illness in hospitalized patients with coronavirus disease 2019. This risk calculator might be useful for triage of patients to the limited number of ICU beds/facilities.
    DOI:  https://doi.org/10.1148/radiol.2020202723
  5. medRxiv. 2020 Jun 18. pii: 2020.06.16.20133140. [Epub ahead of print]
    COVID-19 outcomes, risk factors and associations by race: a comprehensive analysis using electronic health records data in Michigan Medicine.
    Tian Gu, Jasmine A Mack, Maxwell Salvatore, Swaraaj Prabhu Sankar, Thomas S Valley, Karandeep Singh, Brahmajee K Nallamothu, Sachin Kheterpal, Lynda Lisabeth, Lars G Fritsche, Bhramar G Mukherjee.
      IMPORTANCE: Blacks/African-Americans are overrepresented in the number of COVID-19 infections, hospitalizations and deaths. Reasons for this disparity have not been well-characterized but may be due to underlying comorbidities or sociodemographic factors.OBJECTIVE: To systematically determine patient characteristics associated with racial/ethnic disparities in COVID-19 outcomes.
    DESIGN: A retrospective cohort study with comparative control groups.
    SETTING: Patients tested for COVID-19 at University of Michigan Medicine from March 10, 2020 to April 22, 2020.
    PARTICIPANTS: 5,698 tested patients and two sets of comparison groups who were not tested for COVID-19: randomly selected unmatched controls (n = 7,211) and frequency-matched controls by race, age, and sex (n = 13,351). Main Outcomes and Measures: We identified factors associated with testing and testing positive for COVID-19, being hospitalized, requiring intensive care unit (ICU) admission, and mortality (in/out-patient during the time frame). Factors included race/ethnicity, age, smoking, alcohol consumption, healthcare utilization, and residential-level socioeconomic characteristics (SES; i.e., education, unemployment, population density, and poverty rate). Medical comorbidities were defined from the International Classification of Diseases (ICD) codes, and were aggregated into a comorbidity score.
    RESULTS: Of 5,698 patients, (median age, 47 years; 38% male; mean BMI, 30.1), the majority were non-Hispanic Whites (NHW, 59.2%) and non-Hispanic Black/African-Americans (NHAA, 17.2%). Among 1,119 diagnosed, there were 41.2% NHW and 37.4% NHAA; 44.8% hospitalized, 20.6% admitted to ICU, and 3.8% died. Adjusting for age, sex, and SES, NHAA were 1.66 times more likely to be hospitalized (95% CI, 1.09-2.52; P=.02), 1.52 times more likely to enter ICU (95% CI, 0.92-2.52; P=.10). In addition to older age, male sex and obesity, high population density neighborhood (OR, 1.27 associated with one SD change [95% CI, 1.20-1.76]; P=.02) was associated with hospitalization. Pre-existing kidney disease led to 2.55 times higher risk of hospitalization (95% CI, 1.62-4.02; P<.001) in the overall population and 11.9 times higher mortality risk in NHAA (95% CI, 2.2-64.7, P=.004).
    CONCLUSIONS AND RELEVANCE: Pre-existing type II diabetes/kidney diseases and living in high population density areas were associated with high risk for COVID-19 susceptibility and poor prognosis. Association of risk factors with COVID-19 outcomes differed by race. NHAA patients were disproportionately affected by obesity and kidney disease.
    DOI:  https://doi.org/10.1101/2020.06.16.20133140
  6. Front Med (Lausanne). 2020 ;7 459
    COVID-19 Clinical Characteristics, and Sex-Specific Risk of Mortality: Systematic Review and Meta-Analysis.
    Mohammad Javad Nasiri, Sara Haddadi, Azin Tahvildari, Yeganeh Farsi, Mahta Arbabi, Saba Hasanzadeh, Parnian Jamshidi, Mukunthan Murthi, Mehdi Mirsaeidi.
      Background: The rapidly evolving coronavirus disease 2019 (COVID-19), was declared a pandemic by the World Health Organization on March 11, 2020. It was first detected in the Wuhan city of China and has spread globally resulting in a substantial health and economic crisis in many countries. Observational studies have partially identified different aspects of this disease. There have been no published systematic reviews that combine clinical, laboratory, epidemiologic, and mortality findings. Also, the effect of gender on the outcomes of COVID-19 has not been well-defined. Methods: We reviewed the scientific literature published from January 1, 2019 to May 29, 2020. Statistical analyses were performed with STATA (version 14, IC; Stata Corporation, College Station, TX, USA). The pooled frequency with 95% confidence intervals (CI) was assessed using random effect model. P < 0.05 was considered a statistically significant publication bias. Results: Out of 1,223 studies, 34 satisfied the inclusion criteria. A total of 5,057 patients with a mean age of 49 years were evaluated. Fever (83.0%, CI 77.5-87.6) and cough (65.2%, CI 58.6-71.2) were the most common symptoms. The most prevalent comorbidities were hypertension (18.5%, CI 12.7-24.4) and Cardiovascular disease (14.9%, CI 6.0-23.8). Among the laboratory abnormalities, elevated C-Reactive Protein (CRP) (72.0%, CI 54.3-84.6) and lymphopenia (50.1%, CI 38.0-62.4) were the most common. Bilateral ground-glass opacities (66.0%, CI 51.1-78.0) was the most common CT scan presentation. The pooled mortality rate was 6.6%, with males having significantly higher mortality compared to females (OR 3.4; 95% CI 1.2-9.1, P = 0.01). Conclusion: COVID-19 has caused a significant number of hospitalization and mortality worldwide. Mortality associated with COVID-19 was higher in our study compared to the previous reports from China. The mortality was significantly higher among the hospitalized male group. Further studies are required to evaluate the effect of different variables resulting in sex disparity in COVID-19 mortality.
    Keywords:  COVID-19; coronavirus; male; mortality; pandemic
    DOI:  https://doi.org/10.3389/fmed.2020.00459
  7. Arch Bronconeumol. 2020 Jul 16. pii: S0300-2896(20)30215-5. [Epub ahead of print]
    Comorbidities and Mortality in Patients With COVID-19 Aged 60 Years and Older in a University Hospital in Spain.
    Margarita Posso, Mercè Comas, Marta Román, Laia Domingo, Javier Louro, Cristina González, María Sala, Albert Anglès, Isabel Cirera, Francesc Cots, Víctor-Manuel Frías, Joaquim Gea, Robert Güerri-Fernández, Joan Ramon Masclans, Xavier Noguès, Olga Vázquez, Judith Villar-García, Juan Pablo Horcajada, Julio Pascual, Xavier Castells.
      
    DOI:  https://doi.org/10.1016/j.arbres.2020.06.012
  8. Lancet Respir Med. 2020 Aug 06. pii: S2213-2600(20)30354-4. [Epub ahead of print]
    SARS-CoV-2 viral load predicts COVID-19 mortality.
    Elisabet Pujadas, Fayzan Chaudhry, Russell McBride, Felix Richter, Shan Zhao, Ania Wajnberg, Girish Nadkarni, Benjamin S Glicksberg, Jane Houldsworth, Carlos Cordon-Cardo.
      
    DOI:  https://doi.org/10.1016/S2213-2600(20)30354-4
  9. Proc Natl Acad Sci U S A. 2020 Aug 11. pii: 202011086. [Epub ahead of print]
    Overweight, obesity, and risk of hospitalization for COVID-19: A community-based cohort study of adults in the United Kingdom.
    Mark Hamer, Catharine R Gale, Mika Kivimäki, G David Batty.
      The role of obesity and overweight in occurrence of COVID-19 is unknown. We conducted a large-scale general population study using data from a community-dwelling sample in England (n = 334,329; 56.4 ±8.1 y; 54.5% women) with prospective linkage to national registry on hospitalization for COVID-19. Body mass index (BMI, from measured height and weight) was used as an indicator of overall obesity, and waist-hip ratio for central obesity. Main outcome was cases of COVID-19 serious enough to warrant a hospital admission from 16 March 2020 to 26 April 2020. Around 0.2% (n = 640) of the sample were hospitalized for COVID-19. There was an upward linear trend in the likelihood of COVID-19 hospitalization with increasing BMI, that was evident in the overweight (odds ratio, 1.39; 95% CI 1.13 to 1.71; crude incidence 19.1 per 10,000) and obese stage I (1.70;1.34 to 2.16; 23.3 per 10,000) and stage II (3.38; 2.60 to 4.40; 42.7 per 10,000) compared to normal weight (12.5 per 10,000). This gradient was little affected after adjustment for a wide range of covariates; however, controlling for biomarkers, particularly high-density lipoprotein cholesterol and glycated hemoglobin, led to a greater degree of attenuation. A similar pattern of association emerged for waist-hip ratio. In summary, overall and central obesity are risk factors for COVID-19 hospital admission. Elevated risk was apparent even at modest weight gain. The mechanisms may involve impaired glucose and lipid metabolism.
    Keywords:  COVID-19; epidemiology; infection; obesity
    DOI:  https://doi.org/10.1073/pnas.2011086117
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