Impact of prior SARS-COV-2 infection and vaccination on COVID-19 hospital admission and mortality amongst nursing home residents.

BACKGROUND: Nursing home residents (NHRs) have experienced disproportionately high risk of severe outcomes due to COVID-19 infection. AIM: We investigated the impact of COVID-19 vaccinations and previous SARS-CoV-2 episodes in preventing hospitalization and mortality in NHRs. METHODS: Retrospective study of a cohort of all NHRs in our area who were alive at the start of the vaccination campaign. The first three doses of SARS-CoV-2 vaccine and prior COVID-19 infections were registered. The main outcomes were hospital admission and mortality during each follow up. Random effects time-varying Cox models adjusted for age, sex, and comorbidities were fitted to estimate hazard ratios (HRs) according to vaccination status. RESULTS: COVID-19 hospitalization and death rates for unvaccinated NHRs were respectively 2.39 and 1.42 per 10,000 person-days, falling after administration of the second dose (0.37 and 0.34) and rising with the third dose (1.08 and 0.8). Rates were much lower amongst people who had previously had COVID-19. Adjusted HRs indicated a significant decrease in hospital admission amongst those with a two- and three-dose status; those who had had a previous COVID-19 infection had even lower hospital admission rates. Death rates decreased as NHRs received two and three doses, and the probability of death was much lower among those who had previously had the infection. CONCLUSIONS: The effectiveness of current vaccines against severe COVID-19 disease in NHRs remains high and SARS-CoV-2 episodes prior to vaccination entail a major reduction in hospitalization and mortality rates. The protection conferred by vaccines appears to decline in the following months. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04463706.

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.

[Influence of the social deprivation index on outcomes during the COVID-19 pandemic]. / Influencia del índice de privación social en resultados durante la pandemia de COVID-19.

OBJECTIVE: To see the relationship between the population deprivation index and the use of the health services, adverse evolution and mortality during the COVID-19 pandemic. METHOD: Retrospective cohort study of patients with SARS-CoV-2 infection from March 1, 2020 to January 9, 2022. The data collected included sociodemographic data, comorbidities and prescribed baseline treatments, other baseline data and the deprivation index, estimated by census section. Multivariable multilevel logistic regression models were performed for each outcome variable: death, poor outcome (defined as death or intensive care unit), hospital admission, and emergency room visits. RESULTS: The cohort consists of 371,237 people with SARS-CoV-2 infection. In the multivariable models, a higher risk of death or poor evolution or hospital admission or emergency room visit was observed within the quintiles with the greatest deprivation compared to the quintile with the least. For the risk of being hospitalized or going to the emergency room, there were differences between most quintiles. It has also been observed that these differences occurred in the first and third periods of the pandemic for mortality and poor outcome, and in all due for the risk of being admitted or going to the emergency room. CONCLUSIONS: The groups with the highest level of deprivation have had worse outcomes compared to the groups with lower deprivation rates. It is necessary to carry out interventions that minimize these inequalities.

Nonadverse COVID-19 evolution predictors: the CoNAE scale. / Predictores de evolución no adversa en pacientes con COVID-19: escala CoNAE (COVID-19 non-adverse evolution).

OBJECTIVES: Tools to identify patients with mild to moderate COVID-19 are as yet unavailable. Our aims were to identify factors associated with nonadverse outcomes and develop a scale to predict nonadverse evolution in patients with COVID-19 (the CoNAE scale) in hospital emergency departments. MATERIAL AND METHODS: Retrospective cohort study of patients who came to one of our area's national health service hospitals for treatment of SARS-CoV-2 infection from July 1, 2020, to July 31, 2021. From case records we collected sociodemographic information, underlying comorbidity and ongoing treatments, other relevant medical history details, and vital constants on arrival for triage. Multilevel multivariable logistic regression models were used to identify predictors. RESULTS: The model showed that patients who had nonadverse outcomes were younger, female, and vaccinated against COVID-19 (2 doses at the time of the study). They arrived with normal vital signs (heart rate, diastolic and systolic pressures, temperature, and oxygen saturation) and had none of the following concomitant diseases or factors: heart failure other heart disease, hypertension, diabetes, liver disease, dementia, history of malignant tumors, and they were not being treated with oral or other systemic corticosteroids or immunosuppressant therapy. The area under the receiver operating characteristic curve for the model was 0.840 (95% CI, 0.834-0.847). CONCLUSION: We developed the CoNAE scale to predict nonadverse outcomes. This scale may be useful in triage for evaluating patients with COVID-19. It may also help predict safe discharge or plan the level of care that patients require not only in a hospital emergency department but also in urgent primary care settings or out-of-hospital emergency care.

OBJETIVO: Faltan herramientas para identificar a los pacientes con COVID-19 moderado o leve. El objetivo de este estudio fue identificar variables asociadas a la evolución no adversa y diseñar un modelo predictivo de evolución favorable en pacientes atendidos en servicios de urgencias hospitalarios (SUH) por infección por SARS-CoV-2. METODO: Estudio de cohorte retrospectivo de pacientes con infección por SARS-CoV-2 que acudieron a alguno de los SUH de hospitales públicos de una área por una infección por COVID-19 entre el 1 de julio de 2020 y el 31 de julio de 2021. Los datos recogidos para este estudio incluyeron información sociodemográfica, comorbilidades basales y tratamientos, otros datos de antecedentes y registro de los signos vitales a la llegada (triaje) al SUH. Se utilizaron modelos de regresión logística multivariable multinivel para desarrollar los modelos predictivos. RESULTADOS: Las personas que tuvieron resultados no adversos eran más jóvenes, mujeres, habían recibido dos dosis de la vacuna COVID-19 en el momento del estudio, tenían signos vitales (frecuencia cardiaca-presión diastólica/sistólica, temperatura y saturación de oxígeno) dentro de un rango normal al llegar al triaje del SUH, y no tenían ninguna de las siguientes comorbilidades: insuficiencia cardiaca, enfermedad coronaria, hipertensión arterial, diabetes, enfermedad hepática, demencia, antecedentes de tumores malignos o prescripción de corticosteroides orales sistémicos o inmunosupresores como medicación basal. El modelo tenía un área bajo la curva (ABC) de 0,8404 (IC 95%: 0,8342-0,8466). CONCLUSIONES: Se ha desarrollado una escala de predicción de resultados no adversos que pueden ser útil como herramienta de triaje, así como para determinar el alta segura y para adaptar el nivel de atención que el paciente requiere, no sólo en el SUH, sino también a nivel de atención de emergencia primaria o extrahospitalaria.