Critical Care Requirements Under Uncontrolled Transmission of SARS-CoV-2

Objectives. To estimate the critical care bed capacity that would be required to admit all critical COVID-19 cases in a setting of unchecked SARS-CoV-2 transmission, both with and without elderly-specific protection measures. Methods. Using electronic health records of all 2432 COVID-19 patients hospitalized in a large hospital in Madrid, Spain, between February 28 and April 23, 2020, we estimated the number of critical care beds needed to admit all critical care patients. To mimic a hypothetical intervention that halves SARS-CoV-2 infections among the elderly, we randomly excluded 50% of patients aged 65 years and older. Results. Critical care requirements peaked at 49 beds per 100 000 on April 1-2 weeks after the start of a national lockdown. After randomly excluding 50% of elderly patients, the estimated peak was 39 beds per 100 000. Conclusions. Under unchecked SARS-CoV-2 transmission, peak critical care requirements in Madrid were at least fivefold higher than prepandemic capacity. Under a hypothetical intervention that halves infections among the elderly, critical care peak requirements would have exceeded the prepandemic capacity of most high-income countries. Public Health Implications. Pandemic control strategies that rely exclusively on protecting the elderly are likely to overwhelm health care systems.S

Development and validation of a prediction model for 30-day mortality in hospitalised patients with COVID-19: the COVID-19 SEIMC score

Objective To develop and validate a prediction model of mortality in patients with COVID-19 attending hospital emergency rooms. Design Multivariable prognostic prediction model. Setting 127 Spanish hospitals. Participants Derivation (DC) and external validation (VC) cohorts were obtained from multicentre and single centre databases, including 4035 and 2126 patients with confirmed COVID-19, respectively. Interventions Prognostic variables were identified using multivariable logistic regression. Main outcome measures 30-day mortality. Results Patients? characteristics in the DC and VC were median age 70 and 61 years, male sex 61.0% and 47.9%, median time from onset of symptoms to admission 5 and 8 days, and 30-day mortality 26.6% and 15.5%, respectively. Age, low age-adjusted saturation of oxygen, neutrophil-to-lymphocyte ratio, estimated glomerular filtration rate by the Chronic Kidney Disease Epidemiology Collaboration (CKD EPI) equation, dyspnoea and sex were the strongest predictors of mortality. Calibration and discrimination were satisfactory with an area under the receiver operating characteristic curve with a 95% CI for prediction of 30-day mortality of 0.822 (0.806?0.837) in the DC and 0.845 (0.819?0.870) in the VC. A simplified score system ranging from 0 to 30 to predict 30-day mortality was also developed. The risk was considered to be low with 0?2 points (0%?2.1%), moderate with 3?5 (4.7%?6.3%), high with 6?8 (10.6%?19.5%) and very high with 9?30 (27.7%?100%). Conclusions A simple prediction score, based on readily available clinical and laboratory data, provides a useful tool to predict 30-day mortality probability with a high degree of accuracy among hospitalised patients with COVID-19.Funding. This work was supported by Fundación SEIMC/GeSIDA. The funders had no role in study design, data collection, data interpretation or writing of the manuscript. JB, JRB, IJ, JC, JP and JRA received funding for research from Plan Nacional de I+D+i 2013-2016 and Instituto de Salud Carlos III, Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Ciencia, Innovación y Universidades, cofinanced by the European Development Regional Fund “A way to achieve Europe”, Operative program Intelligent Growth 2014-2020. Spanish AIDS Research Network (RIS) (RD16/0025/0017 (JB), RD16/0025/0018 (JRA), RD16CIII/0002/0006 (IJ)). Spanish Network for Research in Infectious Diseases (REIPI) (RD16/0016/0001 (JRB), RD16/0016/0005 (JC) and RD16/0016/0009 (JP))

A case-control of patients with COVID-19 to explore the association of previous hospitalisation use of medication on the mortality of COVID-19 disease: a propensity score matching analysis

Data from several cohorts of coronavirus disease 2019 (COVID-19) suggest that the most common comorbidities for severe COVID-19 disease are the elderly, high blood pressure, and diabetes; however, it is not currently known whether the previous use of certain drugs help or hinder recovery. This study aims to explore the association of previous hospitalisation use of medication on the mortality of COVID-19 disease. A retrospective case-control from two hospitals in Madrid, Spain, included all patients aged 18 years or above hospitalised with a diagnosis of COVID-19. A Propensity Score matching (PSM) analysis was performed. Confounding variables were considered to be age, sex, and the number of comorbidities. Finally, 3712 patients were included. Of these, 687 (18.5%) patients died (cases). The 22,446 medicine trademarks used previous to admission were classified according to the ATC, obtaining 689 final drugs; all of them were included in PSM analysis. Eleven drugs displayed a reduction in mortality: azithromycin, bemiparine, budesonide-formoterol fumarate, cefuroxime, colchicine, enoxaparin, ipratropium bromide, loratadine, mepyramine theophylline acetate, oral rehydration salts, and salbutamol sulphate. Eight final drugs displayed an increase in mortality: acetylsalicylic acid, digoxin, folic acid, mirtazapine, linagliptin, enalapril, atorvastatin, and allopurinol. Medication associated with survival (anticoagulants, antihistamines, azithromycin, bronchodilators, cefuroxime, colchicine, and inhaled corticosteroids) may be candidates for future clinical trials. Drugs associated with mortality show an interaction with the underlying condition

Identification and validation of clinical phenotypes with prognostic Iimplications in hospitalized COVID-19 patients. A multicentre cohort-based study

Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. Methods In this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)?phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])?and reproduced in the internal validation cohort (n=1368)? phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2·5% (95% CI 1·4?4·3) for patients with phenotype A, 30·5% (28·5?32·6) for patients with phenotype B, and 60·7% (53·7?67·2) for patients with phenotype C (log-rank test p <0·0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5·3% [95% CI 3·4?8·1] for phenotype A, 31·3% [28·5?34·2] for phenotype B, and 59·5% [48·8?69·3] for phenotype C; external validation cohort: 3·7% [2·0?6·4] for phenotype A, 23·7% [21·8?25·7] for phenotype B, and 51·4% [41·9?60·7] for phenotype C).Interpretation Patients admitted to hospital with COVID-19 can be classified into three phenotypes that correlate with mortality. We developed and validated a simplified tool for the probabilistic assignment of patients into phenotypes. These results might help to better classify patients for clinical management, but the pathophysiological mechanisms of the phenotypes must be investigated.Funding: Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation, and Fundación SEIMC/GeSIDAAcknowledgments: The study was funded by Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation (COV20/01031), and Fundación SEIMC/GeSIDA. Additionally, JR-B, BG-G, JB, IJ, JC, JP, and JRA received funding for research from Plan Nacional de I+D+i 2013–2016 and Instituto de Salud Carlos III, Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Ciencia, Innovación y Universidades (cofinanced by European Development Regional Fund “A way to achieve Europe”), and operative programme Intelligent Growth 2014–2020 through the following networks: Spanish Network for Research in Infectious Diseases (RD16/0016/0001 [JR-B, BG-G, MDdT], RD16/0016/0005 [JC], and RD16/0016/0009 [JP]) and Spanish AIDS Research Network (RD16/0025/0017 [JB], RD16/0025/0018 [JRA], RD16/0025/00XX [IJ]). We thank Alejandro González-Herrero for programming of the web tool and app. This study was presented at the ESCMID Conference on Coronavirus Disease, Sept 23–25, 2020

A Case-Control of Patients with COVID-19 to Explore the Association of Previous Hospitalisation Use of Medication on the Mortality of COVID-19 Disease: A Propensity Score Matching Analysis

Data from several cohorts of coronavirus disease 2019 (COVID-19) suggest that the most common comorbidities for severe COVID-19 disease are the elderly, high blood pressure, and diabetes; however, it is not currently known whether the previous use of certain drugs help or hinder recovery. This study aims to explore the association of previous hospitalisation use of medication on the mortality of COVID-19 disease. A retrospective case-control from two hospitals in Madrid, Spain, included all patients aged 18 years or above hospitalised with a diagnosis of COVID-19. A Propensity Score matching (PSM) analysis was performed. Confounding variables were considered to be age, sex, and the number of comorbidities. Finally, 3712 patients were included. Of these, 687 (18.5%) patients died (cases). The 22,446 medicine trademarks used previous to admission were classified according to the ATC, obtaining 689 final drugs; all of them were included in PSM analysis. Eleven drugs displayed a reduction in mortality: azithromycin, bemiparine, budesonide-formoterol fumarate, cefuroxime, colchicine, enoxaparin, ipratropium bromide, loratadine, mepyramine theophylline acetate, oral rehydration salts, and salbutamol sulphate. Eight final drugs displayed an increase in mortality: acetylsalicylic acid, digoxin, folic acid, mirtazapine, linagliptin, enalapril, atorvastatin, and allopurinol. Medication associated with survival (anticoagulants, antihistamines, azithromycin, bronchodilators, cefuroxime, colchicine, and inhaled corticosteroids) may be candidates for future clinical trials. Drugs associated with mortality show an interaction with the underlying conditions

A Cohort of Patients with COVID-19 in a Major Teaching Hospital in Europe

BACKGROUND: Since the confirmation of the first patient infected with SARS-CoV-2 in Spain in January 2020, the epidemic has grown rapidly, with the greatest impact on the region of Madrid. This article describes the first 2226 adult patients with COVID-19, consecutively admitted to La Paz University Hospital in Madrid. METHODS: Our cohort included all patients consecutively hospitalized who had a final outcome (death or discharge) in a 1286-bed hospital of Madrid (Spain) from 25 February (first case admitted) to 19 April 2020. The data were manually entered into an electronic case report form, which was monitored prior to the analysis. RESULTS: We consecutively included 2226 adult patients admitted to the hospital who either died (460) or were discharged (1766). The patients’ median age was 61 years, and 51.8% were women. The most common comorbidity was arterial hypertension (41.3%), and the most common symptom on admission was fever (71.2%). The median time from disease onset to hospital admission was 6 days. The overall mortality was 20.7% and was higher in men (26.6% vs. 15.1%). Seventy-five patients with a final outcome were transferred to the intensive care unit (ICU) (3.4%). Most patients admitted to the ICU were men, and the median age was 64 years. Baseline laboratory values on admission were consistent with an impaired immune-inflammatory profile. CONCLUSIONS: We provide a description of the first large cohort of hospitalized patients with COVID-19 in Europe. Advanced age, male sex, the presence of comorbidities and abnormal laboratory values were more common among the patients with fatal outcomes