Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 May 20;18(1):24.
doi: 10.1186/s12979-021-00237-w.

The age again in the eye of the COVID-19 storm: evidence-based decision making

María C Martín #  1 , Aurora Jurado #  2 , Cristina Abad-Molina  3 , Antonio Orduña  3 , Oscar Yarce  4 , Ana M Navas  4 , Vanesa Cunill  5 , Danilo Escobar  5 , Francisco Boix  6 , Sergio Burillo-Sanz  6 , María C Vegas-Sánchez  7 , Yesenia Jiménez-de Las Pozas  7 , Josefa Melero  8 , Marta Aguilar  8 , Oana Irina Sobieschi  8 , Marcos López-Hoyos  9 , Gonzalo Ocejo-Vinyals  9 , David San Segundo  9 , Delia Almeida  10 , Silvia Medina  10 , Luis Fernández  11 , Esther Vergara  11 , Bibiana Quirant  12 , Eva Martínez-Cáceres  12 , Marc Boiges  12 , Marta Alonso  13 , Laura Esparcia-Pinedo  14 , Celia López-Sanz  14 , Javier Muñoz-Vico  15 , Serafín López-Palmero  15 , Antonio Trujillo  4 , Paula Álvarez  4 , Álvaro Prada  16 , David Monzón  16 , Jesús Ontañón  17 , Francisco M Marco  18 , Sergio Mora  18 , Ricardo Rojo  19 , Gema González-Martínez  20 , María T Martínez-Saavedra  20 , Juana Gil-Herrera  21 , Sergi Cantenys-Molina  21 , Manuel Hernández  22 , Janire Perurena-Prieto  22 , Beatriz Rodríguez-Bayona  23 , Alba Martínez  24 , Esther Ocaña  24 , Juan Molina  4
Affiliations

The age again in the eye of the COVID-19 storm: evidence-based decision making

María C Martín et al. Immun Ageing. .

Abstract

Background: One hundred fifty million contagions, more than 3 million deaths and little more than 1 year of COVID-19 have changed our lives and our health management systems forever. Ageing is known to be one of the significant determinants for COVID-19 severity. Two main reasons underlie this: immunosenescence and age correlation with main COVID-19 comorbidities such as hypertension or dyslipidaemia. This study has two aims. The first is to obtain cut-off points for laboratory parameters that can help us in clinical decision-making. The second one is to analyse the effect of pandemic lockdown on epidemiological, clinical, and laboratory parameters concerning the severity of the COVID-19. For these purposes, 257 of SARSCoV2 inpatients during pandemic confinement were included in this study. Moreover, 584 case records from a previously analysed series, were compared with the present study data.

Results: Concerning the characteristics of lockdown series, mild cases accounted for 14.4, 54.1% were moderate and 31.5%, severe. There were 32.5% of home contagions, 26.3% community transmissions, 22.5% nursing home contagions, and 8.8% corresponding to frontline worker contagions regarding epidemiological features. Age > 60 and male sex are hereby confirmed as severity determinants. Equally, higher severity was significantly associated with higher IL6, CRP, ferritin, LDH, and leukocyte counts, and a lower percentage of lymphocyte, CD4 and CD8 count. Comparing this cohort with a previous 584-cases series, mild cases were less than those analysed in the first moment of the pandemic and dyslipidaemia became more frequent than before. IL-6, CRP and LDH values above 69 pg/mL, 97 mg/L and 328 U/L respectively, as well as a CD4 T-cell count below 535 cells/μL, were the best cut-offs predicting severity since these parameters offered reliable areas under the curve.

Conclusion: Age and sex together with selected laboratory parameters on admission can help us predict COVID-19 severity and, therefore, make clinical and resource management decisions. Demographic features associated with lockdown might affect the homogeneity of the data and the robustness of the results.

Keywords: Area under the curve; COVID-19; Cut-off points; Immunity; Immunosenescence; Lockdown; Lymphocytes; Renin-angiotensin-aldosterone system inhibitors; Severe acute respiratory syndrome coronavirus 2.

PubMed Disclaimer

Conflict of interest statement

The authors stated no conflicts of interest.

Figures

Fig. 1
Fig. 1
Severity factors and comorbidities interactions. Legend. Pearson’s Chi Squared p-values. Abbreviations: Sex(m/f)a: Sex (male/female); ACEIsb: angiotensin conversor enzyme inhibitors; ARBsc: angiotensin II receptor blockers; EBd: epidemiological background
Fig. 2
Fig. 2
Severity distribution by sex of first and second series of COVID-19 inpatients. Legend. The pie charts on the top of the figure correspond to the first series and those at the bottom to the second series
Fig. 3
Fig. 3
Severity distribution by age groups of first and second series of COVID-19 inpatients. Legend. The upper part of the figure corresponds to the first series and the lower part to the second series
Fig. 4
Fig. 4
Severity distribution and dyslipidaemia of first and second series of COVID-19 inpatients. Legend. The pies on the top of the figure correspond to the first series and those at the bottom to the second series
See this image and copyright information in PMC

References

    1. Du Toit A. Outbreak of a novel coronavirus. Nat Rev Microbiol. 2020;18(3):123. doi: 10.1038/s41579-020-0332-0. - DOI - PMC - PubMed
    1. COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). https://coronavirus.jhu.edu/map.html. Accessed 3 May 2021.
    1. Guirao JJ, Cabrera CM, Jiménez N, Rincón L, Urra JM. High serum IL-6 values increase the risk of mortality and the severity of pneumonia in patients diagnosed with COVID-19. Mol Immunol. 2020;128:64–68. doi: 10.1016/j.molimm.2020.10.006. - DOI - PMC - PubMed
    1. Riveiro-Barciela M, Labrador-Horrillo M, Camps-Relats L, González-Sans D, Ventura-Cots M, Terrones-Peinador M, Nuñez-Conde A, Martínez-Gallo M, Hernández M, Antón A, González A, Pujol-Borrell R, Martínez-Valle F. Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis. PLoS One. 2020;15(12):e0244627. doi: 10.1371/journal.pone.0244627. - DOI - PMC - PubMed
    1. Fang L, Karakiulakis G, Roth M. Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection? Lancet Respir Med. 2020;8(4):e21. doi: 10.1016/S2213-2600(20)30116-8. - DOI - PMC - PubMed