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.

[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.

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.

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.

Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19.

Despite the publication of great number of tools to aid decisions in COVID-19 patients, there is a lack of good instruments to predict clinical deterioration. COVID19-Osakidetza is a prospective cohort study recruiting COVID-19 patients. We collected information from baseline to discharge on: sociodemographic characteristics, comorbidities and associated medications, vital signs, treatment received and lab test results. Outcome was need for intensive ventilatory support (with at least standard high-flow oxygen face mask with a reservoir bag for at least 6 h and need for more intensive therapy afterwards or Optiflow high-flow nasal cannula or noninvasive or invasive mechanical ventilation) and/or admission to a critical care unit and/or death during hospitalization. We developed a Catboost model summarizing the findings using Shapley Additive Explanations. Performance of the model was assessed using area under the receiver operating characteristic and prediction recall curves (AUROC and AUPRC respectively) and calibrated using the Hosmer-Lemeshow test. Overall, 1568 patients were included in the derivation cohort and 956 in the (external) validation cohort. The percentages of patients who reached the composite endpoint were 23.3% vs 20% respectively. The strongest predictors of clinical deterioration were arterial blood oxygen pressure, followed by age, levels of several markers of inflammation (procalcitonin, LDH, CRP) and alterations in blood count and coagulation. Some medications, namely, ATC AO2 (antiacids) and N05 (neuroleptics) were also among the group of main predictors, together with C03 (diuretics). In the validation set, the CatBoost AUROC was 0.79, AUPRC 0.21 and Hosmer-Lemeshow test statistic 0.36. We present a machine learning-based prediction model with excellent performance properties to implement in EHRs. Our main goal was to predict progression to a score of 5 or higher on the WHO Clinical Progression Scale before patients required mechanical ventilation. Future steps are to externally validate the model in other settings and in a cohort from a different period and to apply the algorithm in clinical practice.Registration: ClinicalTrials.gov Identifier: NCT04463706.

Modelling the risk of hospital admission of lab confirmed SARS-CoV-2-infected patients in primary care: a population-based study.

The objectives of this study are to develop a predictive model of hospital admission for COVID-19 to help in the activation of emergency services, early referrals from primary care, and the improvement of clinical decision-making in emergency room services. The method is the retrospective cohort study of 49,750 patients with microbiological confirmation of SARS-CoV-2 infection. The sample was randomly divided into two subsamples, for the purposes of derivation and validation of the prediction rule (60% and 40%, respectively). Data collected for this study included sociodemographic data, baseline comorbidities, baseline treatments, and other background data. Multilevel analyses with generalized estimated equations were used to develop the predictive model. Male sex and the gradual effect of age were the main risk factors for hospital admission. Regarding baseline comorbidities, coagulopathies, cancer, cardiovascular diseases, diabetes with organ damage, and liver disease were among the five most notable. Flu vaccination was a risk factor for hospital admission. Drugs that increased risk were chronic systemic steroids, immunosuppressants, angiotensin-converting enzyme inhibitors, and NSAIDs. The AUC of the risk score was 0.821 and 0.828 in the derivation and validation samples, respectively. Based on the risk score, five risk groups were derived with hospital admission ranging from 2.94 to 51.87%. In conclusion, we propose a classification system for people with COVID-19 with a higher risk of hospitalization, and indirectly with it a greater severity of the disease, easy to be completed both in primary care, as well as in emergency services and in hospital emergency room to help in clinical decision-making.Registration: ClinicalTrials.gov Identifier: NCT04463706.

COPD in SARS-CoV-2 pandemic. baseline characteristics related to hospital admissions.

OBJECTIVE: To develop a predictive model for COPD patients admitted for COVID-19 to support clinical decision-making. METHOD: Retrospective cohort study of 1313 COPD patients with microbiological confirmation of SARS-CoV-2 infection. The sample was randomly divided into two subsamples, for the purposes of derivation and validation of the prediction rule (60% and 40%,respectively). Data collected for this study included sociodemographic characteristics, baseline comorbidities, baseline treatments, and other background data. Multivariable logistic regression analysis was used to develop the predictive model. RESULTS: Male sex, older age, hospital admissions in the previous year, flu vaccination in the previous season, a Charlson Index>3 and a prescription of renin-angiotensin aldosterone system inhibitors at baseline were the main risk factors for hospital admission. The AUC of the categorized risk score was 0.72 and 0.69 in the derivation and validation samples, respectively. Based on the risk score, four groups were identified with a risk of hospital admission ranging from 21% to 80%. CONCLUSIONS: We propose a classification system to identify COPD people with COVID-19 with a higher risk of hospitalization, and indirectly, more severe disease, that is easy to use in primary care, as well as hospital emergency room settings to help clinical decision-making. CLINICALTRIALS.GOV IDENTIFIER: NCT04463706.

Predictors of mortality of COVID-19 in the general population and nursing homes.

The factors that predispose an individual to a higher risk of death from COVID-19 are poorly understood. The goal of the study was to identify factors associated with risk of death among patients with COVID-19. This is a retrospective cohort study of people with laboratory-confirmed SARS-CoV-2 infection from February to May 22, 2020. Data retrieved for this study included patient sociodemographic data, baseline comorbidities, baseline treatments, other background data on care provided in hospital or primary care settings, and vital status. Main outcome was deaths until June 29, 2020. In the multivariable model based on nursing home residents, predictors of mortality were being male, older than 80 years, admitted to a hospital for COVID-19, and having cardiovascular disease, kidney disease or dementia while taking anticoagulants or lipid-lowering drugs at baseline was protective. The AUC was 0.754 for the risk score based on this model and 0.717 in the validation subsample. Predictors of death among people from the general population were being male and/or older than 60 years, having been hospitalized in the month before admission for COVID-19, being admitted to a hospital for COVID-19, having cardiovascular disease, dementia, respiratory disease, liver disease, diabetes with organ damage, or cancer while being on anticoagulants was protective. The AUC was 0.941 for this model's risk score and 0.938 in the validation subsample. Our risk scores could help physicians identify high-risk groups and establish preventive measures and better follow-up for patients at high risk of dying.ClinicalTrials.gov Identifier: NCT04463706.