Background: One hundred million of contagions, more than 2 million deaths and less than one 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. Age, lymphocyte count and LDH had similar distributions at both moments. 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, sex and dyslipidaemia 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 could affect the homogeneity of the data and the robustness of the results.