bims-covirf Biomed News
on COVID19 risk factors
Issue of 2020‒09‒20
eight papers selected by
Catherine Rycroft
BresMed


  1. Clin Infect Dis. 2020 Sep 18. pii: ciaa1419. [Epub ahead of print]
    Ko JY, Danielson ML, Town M, Derado G, Greenlund KJ, Daily Kirley P, Alden NB, Yousey-Hindes K, Anderson EJ, Ryan PA, Kim S, Lynfield R, Torres SM, Barney GR, Bennett NM, Sutton M, Talbot HK, Hill M, Hall AJ, Fry AM, Garg S, Kim L, .
      BACKGROUND: Data on risk factors for COVID-19-associated hospitalization are needed to guide prevention efforts and clinical care. We sought to identify factors independently associated with COVID-19-associated hospitalizations.METHODS: U.S. community-dwelling adults (≥18 years) hospitalized with laboratory-confirmed COVID-19 during March 1-June 23, 2020 were identified from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET), a multi-state surveillance system. To calculate hospitalization rates by age, sex, and race/ethnicity strata, COVID-NET data served as the numerator and Behavioral Risk Factor Surveillance System estimates served as the population denominator for characteristics of interest. Underlying medical conditions examined included hypertension, coronary artery disease, history of stroke, diabetes, obesity [BMI ≥30 kg/m 2], severe obesity [BMI≥40 kg/m 2], chronic kidney disease, asthma, and chronic obstructive pulmonary disease. Generalized Poisson regression models were used to calculate adjusted rate ratios (aRR) for hospitalization.
    RESULTS: Among 5,416 adults, hospitalization rates were higher among those with ≥3 underlying conditions (versus without)(aRR: 5.0; 95%CI: 3.9, 6.3), severe obesity (aRR:4.4; 95%CI: 3.4, 5.7), chronic kidney disease (aRR:4.0; 95%CI: 3.0, 5.2), diabetes (aRR:3.2; 95%CI: 2.5, 4.1), obesity (aRR:2.9; 95%CI: 2.3, 3.5), hypertension (aRR:2.8; 95%CI: 2.3, 3.4), and asthma (aRR:1.4; 95%CI: 1.1, 1.7), after adjusting for age, sex, and race/ethnicity. Adjusting for the presence of an individual underlying medical condition, higher hospitalization rates were observed for adults aged ≥65, 45-64 (versus 18-44 years), males (versus females), and non-Hispanic black and other race/ethnicities (versus non-Hispanic whites).
    CONCLUSION: Our findings elucidate groups with higher hospitalization risk that may benefit from targeted preventive and therapeutic interventions.
    Keywords:  COVID-19; epidemiology; hospitalization; risk factors; surveillance
    DOI:  https://doi.org/10.1093/cid/ciaa1419
  2. Int J Clin Pract. 2020 Sep 15. e13705
    García Clemente MM, Herrero Huertas J, Fernández Fernández A, De La Escosura Muñoz C, Enríquez Rodríguez AI, Pérez Martínez L, Gómez Mañas S, Iscar Urrutia M, López González FJ, Madrid Carbajal CJ, Bedate Díaz P, Arias Guillén M, Bailón Cuadrado C, Hermida Valverde T.
      OBJECTIVE: To analyze the accuracy of commonly used risk scores (PSI and CURB-65) in predicting mortality and need for ICU admission in Covid-19.MATERIAL AND METHODS: Prospective study of patients diagnosed with covid-19 pneumonia. Patients were followed until home discharge or death. PSI, CURB-65, SMART-COP and MuLBSTA severity scores were assessed on admission. Risk scores were related to mortality and ICU admission.
    RESULTS: 249 patients, 143 males (57.4%) were included. The mean age was 65.6 + 16.1 years. Factors associates with mortality in the multivariate analysis were age > 80 years (OR: 13.9; 95%CI 3.8-51.1) (p=0.000), lymphocytes < 800 (OR:2.9; CI95% 1.1-7-9)(p=0.040), confusion (OR: 6.3; CI95% 1.6-24.7)(p=0.008) and NT-proBNP > 500 pg/mL (OR: 10.1; CI95% 1.1-63.1)(p=0.039). In predicting mortality, the PSI score: AUC 0.874 (95% CI 0.808-0.939) and the CURB-65 score: AUC 0.852 (95% CI 0.794-0.909) were the ones that obtained the best results. In the need for ICU admission, the SMART-COP score: AUC 0.749 (95% CI 0.695-0.820) and the MuLBSTA score: AUC 0.777 (95% CI 0.713-0.840) were the ones that obtained better results, with significant differences with PSI and CURB-65. The scores with the lowest value for ICU admission prediction were PSI with AUC of 0.620 (95% CI 0.549-0.690) and CURB-65 with AUC of 0.604 (95% CI 0.528-0.680).
    CONCLUSIONS: Prognosis scores routinely used for CAP (PSI and CURB-65) were good predictors for mortality in patients with covid-19 CAP but not for need of hospitalization or ICU admission. In the evaluation of covid-19 pneumonia, we need scores that allow to decide the appropriate level of care.
    Keywords:  CURB-65; Covid-19; PSI; SARS-CoV-2; risk scores
    DOI:  https://doi.org/10.1111/ijcp.13705
  3. Res Sq. 2020 Sep 09. pii: rs.3.rs-73657. [Epub ahead of print]
    Ahrenfeldt LJ, Nielsen CR, Möller S, Christensen K, Lindahl-Jacobsen R.
      Aim: International health authorities suggest that individuals aged 65 years and above and people with underlying comorbidities such as hypertension, chronic lung disease, cardiovascular disease, cancer, diabetes, and obesity are at increased risk of severe Coronavirus Disease 2019 (COVID-19); however, the prevalence of risk factors is unknown in many countries. Therefore, we aim to describe the distribution of these risk factors across Europe. Subject and Methods: Prevalence of risk factors for severe COVID-19 was identified based on interview for 73,274 Europeans aged 50+ participating in the Survey of Health, Ageing and Retirement in Europe (SHARE) in 2017. Burden of disease was estimated using population data from Eurostat. Results: A total of 75.3% of the study population (corresponding to app. 60 million European men and 71 million women) had at least one risk factor for severe COVID-19, 45.9% (app. 36 million men and 43 million women) had at least two factors and 21.2% (app. 17 million men and 20 million women) had at least three risk factors. The prevalences of underlying medical conditions ranged from 4.5% for cancer to 41.4% for hypertension, and the region-specific prevalence of having at least three risk factors ranged from 18.9% in Northern Europe to 24.6% in Eastern Europe. Conclusions: Information about the prevalences of risk factors might help authorities to identify the most vulnerable subpopulations with multiple risk factors of severe COVID-19 disease and thus to decide appropriate strategies to mitigate the pandemic.
    DOI:  https://doi.org/10.21203/rs.3.rs-73657/v1
  4. Clin Infect Dis. 2020 Sep 18. pii: ciaa1420. [Epub ahead of print]
    Miller J, Fadel RA, Tang A, Perrotta G, Herc E, Soman S, Nair S, Hanna Z, Zervos MJ, Alangaden G, Brar I, Suleyman G.
      BACKGROUND: The relationship of health disparities and comorbidities in coronavirus disease 2019 (COVID-19) related outcomes are an ongoing area of interest. This report assesses risk factors associated with mortality in patients presenting with Covid-19 infection and healthcare disparities.METHODS: A retrospective cohort study of consecutive patients presenting to emergency departments within an integrated health system who tested positive for COVID-19 between March 7 and April 30, 2020 in Metropolitan Detroit. The primary outcomes were hospitalization and 30-day mortality.
    RESULTS: A total of 3,633 patients with mean age of 58 years were included. The majority were female and black non-Hispanic. Sixty-four percent required hospitalization, 56% of whom were black. Hospitalized patients were older, more likely to reside in a low-income area, and had a higher burden of comorbidities. By 30-days, 433 (18.7%) hospitalized patients died. In adjusted analyses, the presence of comorbidities, age >60 years and more severe physiological disturbance were associated with 30-day mortality. Residence in low income areas (odds ratio, 1.02; 95% confidence interval 0.76 - 1.36), and public insurance (odds ratio, 1.24; 95% confidence interval 0.76 - 2.01) were not independently associated with higher risk of mortality. Black female patients had a lower adjusted risk of mortality (odds ratio, 0.46; 95% confidence interval, 0.27 to 0.78).
    CONCLUSIONS: In this large cohort of COVID-19 patients, those with comorbidities, advanced age, and physiological abnormalities on presentation had higher odds of death. Disparities in income or source of health insurance were not associated with outcomes. Black women had a lower risk of dying.
    Keywords:  COVID-19; Detroit; SARS-CoV-2; mortality; outcomes
    DOI:  https://doi.org/10.1093/cid/ciaa1420
  5. Curr Med Res Opin. 2020 Sep 18. 1
    Zhou J, Huang L, Chen J, Yuan X, Shen Q, Dong S, Cheng B, Guo TM.
      Background: Since December 2019, the cumulative number of coronavirus disease-2019 (COVID-19) deaths in worldwide has reached 612,876 and continues to increase as of writing. Of the deaths, more than 90% are people ages 60 and older. However, an easy-to-use clinically predictive tool for predicting the mortality risk in older individuals with COVID-19 is limited.Objective: To explore an easy-to-use clinically predictive tool that may be utilized in predicting mortality risk in older patients with COVID-19.Methods: A retrospective analysis of 118 older patients with COVID-19 admitted to the Union Dongxihu Hospital, Huazhong University of Science and Technology, Wuhan, China from January 12 to February 26, 2020. The main results of epidemiological, demographic, clinical, and laboratory tests on admission were collected and compared between dying and discharged patients.Results: No difference in major symptoms was observed between dying and discharged patients. Among the results of laboratory tests, NLR, lactate dehydrogenase, albumin, urea nitrogen, and D-dimer (NLAUD) show greater differences and have better regression coefficients (β) when using hierarchical comparisons in a multivariate logistic regression model. Predictors of mortality based on better regression coefficients (β) included NLR (OR =31.2, 95% CI 6.7-144.5, p < 0.0001), lactate dehydrogenase (OR =73.4, 95% CI 11.8-456.8, p < 0.0001), albumin (OR <0.1, 95% CI <0.1-0.2, p < 0.0001), urea nitrogen (OR =12.0, 95% CI 3.0-48.4, p = 0.0005), and D-dimer (OR =13.6, 95% CI 3.4-54.9, p = 0.0003). According to the above indicators, a predictive NLAUD score was calculated on the basis of a multivariate logistic regression model to predict mortality. This model showed a sensitivity of 0.889, specificity of 0.984, and a better predictive ability than CURB-65 (AUROC =0.955 vs. 0.703, p < 0.001). Bootstrap validation generated the similar sensitivity and specificity.Conclusions: We designed an easy-to-use clinically predictive tool for early identification and stratified treatment of severe older patients with COVID-19.
    Keywords:  COVID-19; Clinical features; and specificity; mortality; older adults; sensitivity
    DOI:  https://doi.org/10.1080/03007995.2020.1825365
  6. J Stroke Cerebrovasc Dis. 2020 Aug;pii: S1052-3057(20)30357-8. [Epub ahead of print]29(8): 104949
    Pranata R, Huang I, Lim MA, Wahjoepramono EJ, July J.
      BACKGROUND: We conducted a systematic review and meta-analysis to evaluate the latest evidence on the association between cerebrovascular, and cardiovascular diseases and poor outcome in patients with Coronavirus Disease 2019 (COVID-19) pneumonia.METHODS: A comprehensive systematic literature search was performed using PubMed, SCOPUS, EuropePMC, and Cochrane Central Database. The outcome of interest was composite poor outcome that comprised of mortality and severe COVID-19.
    RESULTS: A total of 4448 patients were obtained from 16 studies. Cerebrovascular disease was associated with an increased composite poor outcome (RR 2.04 [1.43,2.91], p<0.001; I2: 77%). Subgroup analysis revealed that cerebrovascular disease was associated with mortality (RR 2.38 [1.92,2.96], p<0.001; I2: 0%) and showed borderline significance for severe COVID-19 (RR 1.88 [1.00,3.51], p = 0.05; I2: 87%). Cardiovascular disease was associated with increased composite poor outcome (RR 2.23 [1.71,2.91], p<0.001; I2: 60%), mortality (RR 2.25 [1.53,3.29], p<0.001; I2: 33%) and severe COVID-19 (RR 2.25 [1.51,3.36], p<0.001; I2: 76%). Meta-regression demonstrate that the association was not influenced by gender, age, hypertension, diabetes, and respiratory comorbidities. Furthermore, the association between cerebrovascular disease and poor outcome was not affected by cardiovascular diseases and vice versa.
    CONCLUSION: Cerebrovascular and cardiovascular diseases were associated with an increased risk for poor outcome in patients with COVID-19.
    Keywords:  COVID-19; Cardiovascular; Cerebrovascular; Mortality; Severity
    DOI:  https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.104949
  7. Obes Rev. 2020 Sep 14.
    Chang TH, Chou CC, Chang LY.
      We conducted a systematic review of observational studies to examine the effects of body mass index (BMI) and obesity (BMI ≥ 30 kg/m2 ) on coronavirus disease 2019 (COVID-19). Medline, Embase, and the Cochrane Library were searched. Sixteen articles were finally included in the meta-analysis, and a random effects model was used. BMI was found to be higher in patients with severe disease than in those with mild or moderate disease (MD 1.6, 95% CI, 0.8-2.4; p = .0002) in China; however, the heterogeneity was high (I2 = 75%). Elevated BMI was associated with invasive mechanical ventilation (IMV) use (MD 4.1, 95% CI, 2.1-6.1; p < .0001) in Western countries, and this result was consistent across studies (I2 = 0%). Additionally, there were increased odds ratios of IMV use (OR 2.0, 95% CI, 1.4-2.9; p < .0001) and hospitalization (OR 1.4, 95% CI, 1.3-1.60; p < .00001) in patients with obesity. There was no substantial heterogeneity (I2 = 0%). In conclusion, obesity or high BMI increased the risk of hospitalization, severe disease and invasive mechanical ventilation in COVID-19. Physicians must be alert to these early indicators to identify critical patients.
    Keywords:  BMI; COVID-19; obesity; severe disease
    DOI:  https://doi.org/10.1111/obr.13089
  8. Healthcare (Basel). 2020 Sep 13. pii: E338. [Epub ahead of print]8(3):
    Acharya D, Lee K, Lee DS, Lee YS, Moon SS.
      Studies have confirmed COVID-19 patients with diabetes are at higher risk of mortality than their non-diabetic counterparts. However, data-driven evidence of factors associated with increased mortality risk among hospitalized COVID-19 patients with diabetes is scarce in South Korea. This study was conducted to determine the mortality rate and identify risk factors of mortality among hospitalized COVID-19 patients with type 2 diabetes in Gyeongsangbuk-do province, South Korea. In this hospital-based, cross-sectional study, we enrolled a total of 324 patients with confirmed COVID-19, hospitalized at two of the tertiary level healthcare facilitates of Gyeongsangbuk-do, South Korea from 18 February to 30 June 2020. Demographic and clinical data and laboratory profiles were analyzed and multivariate logistic regression analysis was used to identify risk factors of mortality among diabetic patients with COVID-19. Of the 324 patients, 55 (16.97%) had diabetes mellitus. The mean age of all study subjects was 55 years, and the mean age of those with diabetes was greater than that of those without (69.8 years vs. 51.9 years). Remarkably, the mortality rate was much higher among those with diabetes (20.0% vs. 4.8%). Multivariate logistic regression analysis revealed that an older age (≥70 years) and a high serum lactate dehydrogenase (LDH) levels significantly predicted mortality among hospitalized COVID-19 patients with diabetes. Our study cautions more attention to be paid to patients with diabetes mellitus hospitalized for COVID-19, especially those aged ≥ 70 years and those with a high serum LDH level, to reduce the risk of mortality.
    Keywords:  Korea; coronavirus; diabetes; mortality; pandemics
    DOI:  https://doi.org/10.3390/healthcare8030338