Dataset Information

Pre-infection HDL-cholesterol levels and mortality among elderly patients infected with SARS-CoV-2.

ABSTRACT:

Background and aims

Low HDL-cholesterol (HDLc) concentration is associated with a greater risk of infection-related mortality. We wanted to evaluate the relationship between pre-infection HDLc levels and mortality among older patients infected with SARS-Cov-2.

Methods

This is a population-based, cohort study, comprising all individuals residing in Madrid (Spain) born before 1 January 1945, and alive on 31 December 2019. Demographic, clinical, and analytical data were obtained from the primary care electronic clinical records. Confirmed SARS-CoV-2 infection was defined as a positive result in the RT-qPCR or in the antigen test. A death from COVID-19 was defined as that registered in the hospital chart, or as any death occurring in the 15 days following a confirmed SARS-CoV-2 infection. Data on infection, hospitalization, or death due to SAR-CoV-2 were collected from 1 March 2020 through 31 December 2020.

Results

Of the 593,342 individuals comprising the cohort, 36,966 had a SARS-CoV-2 infection during 2020, and at least one HDLc measurement in the previous five years. Among them, 9689 (26.2%) died from COVID-19. After adjustment for age and sex, the relative risk (95% confidence interval) of COVID-19 death across increasing quintiles of HDLc was 1.000, 0.896 (0.855-0.940), 0.816 (0.776-0.860), 0.758 (0.719-0.799), and 0.747 (0.708-0.787). The association was maintained after further adjustment for comorbidities, statin treatment and markers of malnutrition. While in females this association was linear, in males it showed a U-shaped curve.

Conclusions

In older subjects, a higher HDLc measured before SARS-CoV-2 infection was associated with a lower risk of death.

SUBMITTER: Mostaza JM

PROVIDER: S-EPMC8692242 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background and Objectives: The severe forms of SARS-CoV-2 pneumonia are associated with acute hypoxic respiratory failure and high mortality rates, raising significant challenges for the medical community. The objective of this paper is to present the importance of early quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters. The current evolution models used in the evaluation of severe cases of COVID-19 only include qualitative or semi-quantitative evaluations of pulmonary lesions which lead to a less accurate prognosis and assessment of pulmonary involvement. Materials and Methods: We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID-19. The patients were divided into two groups: group A (76 survivors) and group B (24 non-survivors). All patients were evaluated by CT scan upon admission in to the hospital. Results: We found a low percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin associated with higher mortality; a high percentage of interstitial lesions, oxygen flow, FiO2, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine were also associated with higher mortality. The most accurate regression model included the predictors of age, lymphocytes, PaO2/FiO2 ratio, percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Based on these parameters we developed a new score (COV-Score). Conclusions: Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters. The cluster evaluation algorithm increases the non-survivor and overall prediction accuracy.COV-Score represents a viable alternative to current prediction scores, demonstrating improved sensitivity and specificity in predicting mortality at the time of admission.