Predicting critical illness on initial diagnosis of COVID-19: Development and validation of the PRIORITY model for outpatient applicability.

ObjectivesCurrently available COVID-19 prognostic models have focused on laboratory and radiological data obtained following admission. However, these tests are not available on initial assessment or in resource-limited settings. We aim to develop and validate a prediction model, based on clinical history and examination findings on initial diagnosis of COVID-19, to identify patients at risk of critical outcomes. MethodsWe used data from the SEMI-COVID-19 Registry, a nationwide multicenter cohort of consecutive patients hospitalized for COVID-19 from 132 centers in Spain. Clinical signs and symptoms, demographic variables, and medical history ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive model. We externally validated the final model in a separate cohort of patients admitted at less-complex hospitals (< 300 beds).We undertook decision curve analysis to assess the clinical usefulness of the predictive model. The primary outcome was a composite of in-hospital death, mechanical ventilation or admission to intensive care unit. ResultsThere were 10,433 patients, 7,850 (primary outcome 25.1%) in the development cohort and 2,583 (primary outcome 27.0%) in the validation cohort. Variables in the final model included: age, cardiovascular disease, chronic kidney disease, dyspnea, tachypnea, confusion, systolic blood pressure, and SpO2[≤]93% or oxygen requirement.The C-statistic in the development cohort was 0.823 (95% CI,0.813-0.834). On external validation, the C-statistic was 0.792 (95% CI,0.772-0.812). The model showed a positive net benefit for threshold probabilities between 3% and 79%. ConclusionsAmong patients presenting with COVID-19, the model based on easily-obtained clinical information had good discrimination and generalizability for identifying patients at risk of critical outcomes without the need of additional testing. The online calculator provided would facilitate triage of patients during the pandemic. This study could provide a useful tool for decision-making in health systems under pandemic pressure and resource-limited settings.

Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry.

AimTo determine whether healthcare workers (HCW) hospitalized in Spain due to COVID-19 have a worse prognosis than non-healthcare workers (NHCW). MethodsObservational cohort study based on the SEMI-COVID-19 Registry, a nationwide registry that collects sociodemographic, clinical, laboratory, and treatment data on patients hospitalised with COVID-19 in Spain. Patients aged 20-65 years were selected. A multivariate logistic regression model was performed to identify factors associated with mortality. ResultsAs of 22 May 2020, 4393 patients were included, of whom 419 (9.5%) were HCW. Median (interquartile range) age of HCW was 52 (15) years and 62.4% were women. Prevalence of comorbidities and severe radiological findings upon admission were less frequent in HCW. There were no difference in need of respiratory support and admission to intensive care unit, but occurrence of sepsis and in-hospital mortality was lower in HCW (1.7% vs. 3.9%; p=0.024 and 0.7% vs. 4.8%; p<0.001 respectively). Age, male sex and comorbidity, were independently associated with higher in-hospital mortality and healthcare working with lower mortality (OR 0.219, 95%CI 0.069-0.693, p=0.01). 30-days survival was higher in HCW (0.968 vs. 0.851 p<0.001). ConclusionsHospitalized COVID-19 HCW had fewer comorbidities and a better prognosis than NHCW. Our results suggest that professional exposure to COVID-19 in HCW does not carry more clinical severity nor mortality.

Predicting clinical outcome with phenotypic clusters in COVID-19 pneumonia: 2 an analysis of 12,066 hospitalized patients from the Spanish registry SEMI-3 COVID-19.

(1) Background: This study aims to identify different clinical phenotypes in COVID-19 88 pneumonia using cluster analysis and to assess the prognostic impact among identified clusters in 89 such patients. (2) Methods: Cluster analysis including 11 phenotypic variables was performed in a 90 large cohort of 12,066 COVID-19 patients, collected and followed-up from March 1, to July 31, 2020, 91 from the nationwide Spanish SEMI-COVID-19 Registry. (3) Results: Of the total of 12,066 patients 92 included in the study, most were males (7,052, 58.5%) and Caucasian (10,635, 89.5%), with a mean 93 age at diagnosis of 67 years (SD 16). The main pre-admission comorbidities were arterial 94 hypertension (6,030, 50%), hyperlipidemia (4,741, 39.4%) and diabetes mellitus (2,309, 19.2%). The 95 average number of days from COVID-19 symptom onset to hospital admission was 6.7 days (SD 7). 96 The triad of fever, cough, and dyspnea was present almost uniformly in all 4 clinical phenotypes 97 identified by clustering. Cluster C1 (8,737 patients, 72.4%) was the largest, and comprised patients 98 with the triad alone. Cluster C2 (1,196 patients, 9.9%) also presented with ageusia and anosmia; 99 cluster C3 (880 patients, 7.3%) also had arthromyalgia, headache, and sore throat; and cluster C4 100 (1,253 patients, 10.4%) also manifested with diarrhea, vomiting, and abdominal pain. Compared to 101 each other, cluster C1 presented the highest in-hospital mortality (24.1% vs. 4.3% vs. 14.7% vs. 102 18.6%; p<0.001). The multivariate study identified phenotypic clusters as an independent factor for 103 in-hospital death. (4) Conclusion: The present study identified 4 phenotypic clusters in patients with 104 COVID-19 pneumonia, which predicted the in-hospital prognosis of clinical outcomes.

The Prognostic Value of Eosinophil Recovery in COVID-19: A Multicentre, Retrospective Cohort Study on Patients Hospitalised in Spanish Hospitals.

ObjectivesA decrease in blood cell counts, especially lymphocytes and eosinophils, has been described in patients with severe SARS-CoV-2 (COVID-19), but there is no knowledge of the potential role of their recovery in these patients prognosis. This article aims to analyse the effect of blood cell depletion and blood cell recovery on mortality due to COVID-19. DesignThis work is a multicentre, retrospective, cohort study of 9,644 hospitalised patients with confirmed COVID-19 from the Spanish Society of Internal Medicines SEMI-COVID-19 Registry. SettingThis study examined patients hospitalised in 147 hospitals throughout Spain. ParticipantsThis work analysed 9,644 patients (57.12% male) out of a cohort of 12,826 patients [≥]18 years of age hospitalised with COVID-19 in Spain included in the SEMI-COVID-19 Registry as of 29 May 2020. Main outcome measuresThe main outcome measure of this work is the effect of blood cell depletion and blood cell recovery on mortality due to COVID-19. Univariate analysis was performed to determine possible predictors of death and then multivariate analysis was carried out to control for potential confounders. ResultsAn increase in the eosinophil count on the seventh day of hospitalisation was associated with a better prognosis, including lower mortality rates (5.2% vs 22.6% in non-recoverers, OR 0.234 [95% CI, 0.154 to 0.354]) and lower complication rates, especially regarding to development of acute respiratory distress syndrome (8% vs 20.1%, p=0.000) and ICU admission (5.4% vs 10.8%, p=0.000). Lymphocyte recovery was found to have no effect on prognosis. Treatment with inhaled or systemic glucocorticoids was not found to be a confounding factor. ConclusionEosinophil recovery in patients with COVID-19 is a reliable marker of a good prognosis that is independent of prior treatment. This finding could be used to guide discharge decisions.