Age-Adjusted Endothelial Activation and Stress Index for Coronavirus Disease 2019 at Admission Is a Reliable Predictor for 28-Day Mortality in Hospitalized Patients With Coronavirus Disease 2019.

Background: Endothelial Activation and Stress Index (EASIX) predict death in patients undergoing allogeneic hematopoietic stem cell transplantation who develop endothelial complications. Because coronavirus disease 2019 (COVID-19) patients also have coagulopathy and endotheliitis, we aimed to assess whether EASIX predicts death within 28 days in hospitalized COVID-19 patients. Methods: We performed a retrospective study on COVID-19 patients from two different cohorts [derivation (n = 1,200 patients) and validation (n = 1,830 patients)]. The endpoint was death within 28 days. The main factors were EASIX [(lactate dehydrogenase * creatinine)/thrombocytes] and aEASIX-COVID (EASIX * age), which were log2-transformed for analysis. Results: Log2-EASIX and log2-aEASIX-COVID were independently associated with an increased risk of death in both cohorts (p < 0.001). Log2-aEASIX-COVID showed a good predictive performance for 28-day mortality both in the derivation cohort (area under the receiver-operating characteristic = 0.827) and in the validation cohort (area under the receiver-operating characteristic = 0.820), with better predictive performance than log2-EASIX (p < 0.001). For log2 aEASIX-COVID, patients with low/moderate risk (<6) had a 28-day mortality probability of 5.3% [95% confidence interval (95% CI) = 4-6.5%], high (6-7) of 17.2% (95% CI = 14.7-19.6%), and very high (>7) of 47.6% (95% CI = 44.2-50.9%). The cutoff of log2 aEASIX-COVID = 6 showed a positive predictive value of 31.7% and negative predictive value of 94.7%, and log2 aEASIX-COVID = 7 showed a positive predictive value of 47.6% and negative predictive value of 89.8%. Conclusion: Both EASIX and aEASIX-COVID were associated with death within 28 days in hospitalized COVID-19 patients. However, aEASIX-COVID had significantly better predictive performance than EASIX, particularly for discarding death. Thus, aEASIX-COVID could be a reliable predictor of death that could help to manage COVID-19 patients.

Characteristics, complications and outcomes among 1549 patients hospitalised with COVID-19 in a secondary hospital in Madrid, Spain: a retrospective case series study.

OBJECTIVES: To describe demographic, clinical, radiological and laboratory characteristics, as well as outcomes, of patients admitted for COVID-19 in a secondary hospital. DESIGN AND SETTING: Retrospective case series of sequentially hospitalised patients with confirmed SARS-CoV-2, at Infanta Leonor University Hospital (ILUH) in Madrid, Spain. PARTICIPANTS: All patients attended at ILUH testing positive to reverse transcriptase-PCR on nasopharyngeal swabs and diagnosed with COVID-19 between 1 March 2020 and 28 May 2020. RESULTS: A total of 1549 COVID-19 cases were included (median age 69 years (IQR 55.0-81.0), 57.5% men). 78.2% had at least one underlying comorbidity, the most frequent was hypertension (55.8%). Most frequent symptoms at presentation were fever (75.3%), cough (65.7%) and dyspnoea (58.1%). 81 (5.8%) patients were admitted to the intensive care unit (ICU) (median age 62 years (IQR 51-71); 74.1% men; median length of stay 9 days (IQR 5-19)) 82.7% of them needed invasive ventilation support. 1393 patients had an outcome at the end of the study period (case fatality ratio: 21.2% (296/1393)). The independent factors associated with fatality (OR; 95% CI): age (1.07; 1.06 to 1.09), male sex (2.86; 1.85 to 4.50), neurological disease (1.93; 1.19 to 3.13), chronic kidney disease (2.83; 1.40 to 5.71) and neoplasia (4.29; 2.40 to 7.67). The percentage of hospital beds occupied with COVID-19 almost doubled (702/361), with the number of patients in ICU quadrupling its capacity (32/8). Median length of stay was 9 days (IQR 6-14). CONCLUSIONS: This study provides clinical characteristics, complications and outcomes of patients with COVID-19 admitted to a European secondary hospital. Fatal outcomes were similar to those reported by hospitals with a higher level of complexity.

The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19.

This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. METHODS: We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient's death, thus making the results easy to interpret. RESULTS: Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score. CONCLUSIONS: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization.

[High prevalence of asymptomatic COVID-19 in haemodialysis: learning day by day in the first month of the COVID-19 pandemic]. / Alta prevalencia de COVID-19 asintomático en hemodiálisis. Aprendiendo día a día el primer mes de pandemia de COVID-19.

Dialysis patients are a risk group for SARS-CoV-2 infection and possibly further complications, but we have little information. The aim of this paper is to describe the experience of the first month of the SARS-CoV-2 pandemic in a hospital haemodialysis (HD) unit serving the district of Madrid with the second highest incidence of COVID-19 (almost 1,000 patients in 100,000h). In the form of a diary, we present the actions undertaken, the incidence of COVID-19 in patients and health staff, some clinical characteristics and the results of screening all the patients in the unit. We started with 90 patients on HD: 37 (41.1%) had COVID-19, of whom 17 (45.9%) were diagnosed through symptoms detected in triage or during the session, and 15 (40.5%) through subsequent screening of those who, until that time, had not undergone SARS-CoV-2 PCR testing. Fever was the most frequent symptom, 50% had lymphopenia and 18.4% <95% O2 saturation. Sixteen (43.2%) patients required hospital admission and 6 (16.2%) died. We found a cluster of infection per shift and also among those using public transport. In terms of staff, of the 44 people involved, 15 (34%) had compatible symptoms, 4 (9%) were confirmed as SARS-CoV-2 PCR cases by occupational health, 9 (20%) required some period of sick leave, temporary disability to work (ILT), and 5 were considered likely cases. CONCLUSIONS: We detected a high prevalence of COVID-19 with a high percentage detected by screening; hence the need for proactive diagnosis to stop the pandemic. Most cases are managed as outpatients, however severe symptoms are also appearing and mortality to date is 16.2%. In terms of staff, 20% have required sick leave in relation to COVID-19.

The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19

This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. Methods: We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient"s death, thus making the results easy to interpret. Results. Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score. Conclusions: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization.