Relevance of comorbidities for main outcomes during different periods of the COVID-19 pandemic.

Background: Throughout the evolution of the COVID-19 pandemic, the severity of the disease has varied. The aim of this study was to determine how patients' comorbidities affected and were related to, different outcomes during this time. Methods: Retrospective cohort study of all patients testing positive for SARS-CoV-2 infection between March 1, 2020, and January 9, 2022. We extracted sociodemographic, basal comorbidities, prescribed treatments, COVID-19 vaccination data, and outcomes such as death and admission to hospital and intensive care unit (ICU) during the different periods of the pandemic. We used logistic regression to quantify the effect of each covariate in each outcome variable and a random forest algorithm to select the most relevant comorbidities. Results: Predictors of death included having dementia, heart failure, kidney disease, or cancer, while arterial hypertension, diabetes, ischemic heart, cerebrovascular, peripheral vascular diseases, and leukemia were also relevant. Heart failure, dementia, kidney disease, diabetes, and cancer were predictors of adverse evolution (death or ICU admission) with arterial hypertension, ischemic heart, cerebrovascular, peripheral vascular diseases, and leukemia also relevant. Arterial hypertension, heart failure, diabetes, kidney, ischemic heart diseases, and cancer were predictors of hospitalization, while dyslipidemia and respiratory, cerebrovascular, and peripheral vascular diseases were also relevant. Conclusions: Preexisting comorbidities such as dementia, cardiovascular and renal diseases, and cancers were those most related to adverse outcomes. Of particular note were the discrepancies between predictors of adverse outcomes and predictors of hospitalization and the fact that patients with dementia had a lower probability of being admitted in the first wave.

Clinical prediction rules for adverse evolution in patients with COVID-19 by the Omicron variant.

OBJECTIVE: We identify factors related to SARS-CoV-2 infection linked to hospitalization, ICU admission, and mortality and develop clinical prediction rules. METHODS: Retrospective cohort study of 380,081 patients with SARS-CoV-2 infection from March 1, 2020 to January 9, 2022, including a subsample of 46,402 patients who attended Emergency Departments (EDs) having data on vital signs. For derivation and external validation of the prediction rule, two different periods were considered: before and after emergence of the Omicron variant, respectively. Data collected included sociodemographic data, COVID-19 vaccination status, baseline comorbidities and treatments, other background data and vital signs at triage at EDs. The predictive models for the EDs and the whole samples were developed using multivariate logistic regression models using Lasso penalization. RESULTS: In the multivariable models, common predictive factors of death among EDs patients were greater age; being male; having no vaccination, dementia; heart failure; liver and kidney disease; hemiplegia or paraplegia; coagulopathy; interstitial pulmonary disease; malignant tumors; use chronic systemic use of steroids, higher temperature, low O2 saturation and altered blood pressure-heart rate. The predictors of an adverse evolution were the same, with the exception of liver disease and the inclusion of cystic fibrosis. Similar predictors were found to be related to hospital admission, including liver disease, arterial hypertension, and basal prescription of immunosuppressants. Similarly, models for the whole sample, without vital signs, are presented. CONCLUSIONS: We propose risk scales, based on basic information, easily-calculable, high-predictive that also function with the current Omicron variant and may help manage such patients in primary, emergency, and hospital care.