Abstract
To determine the proportion of patients with COVID-19 who were readmitted to the hospital and the most common causes and the factors associated with readmission. Multicenter nationwide ...cohort study in Spain. Patients included in the study were admitted to 147 hospitals from March 1 to April 30, 2020. Readmission was defined as a new hospital admission during the 30 days after discharge. Emergency department visits after discharge were not considered readmission. During the study period 8392 patients were admitted to hospitals participating in the SEMI-COVID-19 network. 298 patients (4.2%) out of 7137 patients were readmitted after being discharged. 1541 (17.7%) died during the index admission and 35 died during hospital readmission (11.7%, p = 0.007). The median time from discharge to readmission was 7 days (IQR 3–15 days). The most frequent causes of hospital readmission were worsening of previous pneumonia (54%), bacterial infection (13%), venous thromboembolism (5%), and heart failure (5%). Age odds ratio (OR): 1.02; 95% confident interval (95% CI): 1.01–1.03, age-adjusted Charlson comorbidity index score (OR: 1.13; 95% CI: 1.06–1.21), chronic obstructive pulmonary disease (OR: 1.84; 95% CI: 1.26–2.69), asthma (OR: 1.52; 95% CI: 1.04–2.22), hemoglobin level at admission (OR: 0.92; 95% CI: 0.86–0.99), ground-glass opacification at admission (OR: 0.86; 95% CI:0.76–0.98) and glucocorticoid treatment (OR: 1.29; 95% CI: 1.00–1.66) were independently associated with hospital readmission. The rate of readmission after hospital discharge for COVID-19 was low. Advanced age and comorbidity were associated with increased risk of readmission.
Background:
The emergence of new SARS-CoV-2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection ...(particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units. There is an urgent need for easily implementable and clinically effective Early Warning Scores (EWSs) that can predict the risk of complications within the next 24–48 hr. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection.
Methods:
We developed and validated COVID-19 Early Warning Score (COEWS), an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients.
Results:
The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to-calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hr.
The discrimination in the external validation cohort was 0.743 (95% confidence interval CI: 0.703–0.784) for the COEWS score performed with coefficients and 0.700 (95% CI: 0.654–0.745) for the COEWS performed with scores. The area under the receiver operating characteristic curve (AUROC) was similar in vaccinated and unvaccinated patients. Additionally, we observed that the AUROC of the NEWS2 was 0.677 (95% CI: 0.601–0.752) in vaccinated patients and 0.648 (95% CI: 0.608–0.689) in unvaccinated patients.
Conclusions:
The COEWS score predicts death or MV within the next 48 hr based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves.
Funding:
University of Vienna.
A decrease in blood cell counts, especially lymphocytes and eosinophils, has been described in patients with serious Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), but there is no ...knowledge of their potential role of the 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.
This work was a retrospective, multicentre cohort study of 9644 hospitalised patients with confirmed COVID-19 from the Spanish Society of Internal Medicine's SEMI-COVID-19 Registry.
This study examined patients hospitalised in 147 hospitals throughout Spain.
This work analysed 9644 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.
The 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.
An 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 the development of acute respiratory distress syndrome (8% vs. 20.1%,
= 0.000) and ICU admission (5.4% vs. 10.8%,
= 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.
Eosinophil recovery in patients with COVID-19 who required hospitalisation had an independent prognostic value for all-cause mortality and a milder course.
•Chronic IS therapies entail different risk profiles and clinical outcomes in COVID-19 patients.•Chronic corticosteroid use before admission confers higher mortality and risk of ...complications.•Chronic calcineurin inhibitor treatment does not appear to have an effect on mortality.
The aim of this study was to analyze whether subgroups of immunosuppressive (IS) medications conferred different outcomes in COVID-19.
The study involved a multicenter retrospective cohort of consecutive immunosuppressed patients (ISPs) hospitalized with COVID-19 from March to July, 2020. The primary outcome was in-hospital mortality. A propensity score-matched (PSM) model comparing ISP and non-ISP was planned, as well as specific PSM models comparing individual IS medications associated with mortality.
Out of 16 647 patients, 868 (5.2%) were on chronic IS therapy prior to admission and were considered ISPs. In the PSM model, ISPs had greater in-hospital mortality (OR 1.25, 95% CI 0.99–1.62), which was related to a worse outcome associated with chronic corticoids (OR 1.89, 95% CI 1.43–2.49). Other IS drugs had no repercussions with regard to mortality risk (including calcineurin inhibitors (CNI); OR 1.19, 95% CI 0.65–2.20). In the pre-planned specific PSM model involving patients on chronic IS treatment before admission, corticosteroids were associated with an increased risk of mortality (OR 2.34, 95% CI 1.43–3.82).
Chronic IS therapies comprise a heterogeneous group of drugs with different risk profiles for severe COVID-19 and death. Chronic systemic corticosteroid therapy is associated with increased mortality. On the contrary, CNI and other IS treatments prior to admission do not seem to convey different outcomes.
Background
Age is a risk factor for COVID severity. Most studies performed in hospitalized patients with SARS-CoV2 infection have shown an over-representation of older patients and consequently few ...have properly defined COVID-19 in younger patients who require hospital admission. The aim of the present study was to analyze the clinical characteristics and risk factors for the development of respiratory failure among young (18 to 50 years) hospitalized patients with COVID-19.
Methods
This retrospective nationwide cohort study included hospitalized patients from 18 to 50 years old with confirmed COVID-19 between March 1, 2020, and July 2, 2020. All patient data were obtained from the SEMI-COVID Registry. Respiratory failure was defined as the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO2/FiO2 ratio) ≤200 mmHg or the need for mechanical ventilation and/or high-flow nasal cannula or the presence of acute respiratory distress syndrome.
Results
During the recruitment period, 15,034 patients were included in the SEMI-COVID-19 Registry, of whom 2327 (15.4%) were younger than 50 years. Respiratory failure developed in 343 (14.7%), while mortality occurred in 2.3%. Patients with respiratory failure showed a higher incidence of major adverse cardiac events (44 (13%) vs 14 (0.8%),
p
<0.001), venous thrombosis (23 (6.7%) vs 14 (0.8%),
p
<0.001), mortality (43 (12.5%) vs 7 (0.4%),
p
<0.001), and longer hospital stay (15 (9–24) vs 6 (4–9),
p
<0.001), than the remaining patients. In multivariate analysis, variables associated with the development of respiratory failure were obesity (odds ratio (OR), 2.42; 95% confidence interval (95% CI), 1.71 to 3.43;
p
<0.0001), alcohol abuse (OR, 2.40; 95% CI, 1.26 to 4.58;
p
=0.0076), sleep apnea syndrome (SAHS) (OR, 2.22; 95% CI, 1.07 to 3.43;
p
=0.032), Charlson index ≥1 (OR, 1.77; 95% CI, 1.25 to 2.52;
p
=0.0013), fever (OR, 1.58; 95% CI, 1.13 to 2.22;
p
=0.0075), lymphocytes ≤1100 cells/μL (OR, 1.67; 95% CI, 1.18 to 2.37;
p
=0.0033), LDH >320 U/I (OR, 1.69; 95% CI, 1.18 to 2.42;
p
=0.0039), AST >35 mg/dL (OR, 1.74; 95% CI, 1.2 to 2.52;
p
=0.003), sodium <135 mmol/L (OR, 2.32; 95% CI, 1.24 to 4.33;
p
=0.0079), and C-reactive protein >8 mg/dL (OR, 2.42; 95% CI, 1.72 to 3.41;
p
<0.0001).
Conclusions
Young patients with COVID-19 requiring hospital admission showed a notable incidence of respiratory failure. Obesity, SAHS, alcohol abuse, and certain laboratory parameters were independently associated with the development of this complication. Patients who suffered respiratory failure had a higher mortality and a higher incidence of major cardiac events, venous thrombosis, and hospital stay.
Smoking can play a key role in SARS-CoV-2 infection and in the course of the disease. Previous studies have conflicting or inconclusive results on the prevalence of smoking and the severity of the ...coronavirus disease (COVID-19).
Observational, multicenter, retrospective cohort study of 14,260 patients admitted for COVID-19 in Spanish hospitals between February and September 2020. Their clinical characteristics were recorded and the patients were classified into a smoking group (active or former smokers) or a non-smoking group (never smokers). The patients were followed up to one month after discharge. Differences between groups were analyzed. A multivariate logistic regression and Kapplan Meier curves analyzed the relationship between smoking and in-hospital mortality.
The median age was 68.6 (55.8-79.1) years, with 57.7% of males. Smoking patients were older (69.9 59.6-78.0 years), more frequently male (80.3%) and with higher Charlson index (4 2-6) than non-smoking patients. Smoking patients presented a worse evolution, with a higher rate of admission to the intensive care unit (ICU) (10.4 vs 8.1%), higher in-hospital mortality (22.5 vs. 16.4%) and readmission at one month (5.8 vs. 4.0%) than in non-smoking patients. After multivariate analysis, smoking remained associated with these events.
Active or past smoking is an independent predictor of poor prognosis in patients with COVID-19. It is associated with higher ICU admissions and in-hospital mortality.
Uncontrolled inflammation following COVID-19 infection is an important characteristic of the most seriously ill patients. The present study aims to describe the clusters of inflammation in COVID-19 ...and to analyze their prognostic role. This is a retrospective observational study including 15,691 patients with a high degree of inflammation. They were included in the Spanish SEMI-COVID-19 registry from March 1, 2020 to May 1, 2021. The primary outcome was in-hospital mortality. Hierarchical cluster analysis identified 7 clusters. C1 is characterized by lymphopenia, C2 by elevated ferritin, and C3 by elevated LDH. C4 is characterized by lymphopenia plus elevated CRP and LDH and frequently also ferritin. C5 is defined by elevated CRP, and C6 by elevated ferritin and D-dimer, and frequently also elevated CRP and LDH. Finally, C7 is characterized by an elevated D-dimer. The clusters with the highest in-hospital mortality were C4, C6, and C7 (17.4% vs. 18% vs. 15.6% vs. 36.8% vs. 17.5% vs. 39.3% vs. 26.4%). Inflammation clusters were found as independent factors for in-hospital mortality. In detail and, having cluster C1 as reference, the model revealed a worse prognosis for all other clusters: C2 (OR = 1.30,
p
= 0.001), C3 (OR = 1.14,
p
= 0.178), C4 (OR = 2.28,
p
< 0.001), C5 (OR = 1.07,
p
= 0.479), C6 (OR = 2.29,
p
< 0.001), and C7 (OR = 1.28,
p
= 0.001). We identified 7 groups based on the presence of lymphopenia, elevated CRP, LDH, ferritin, and D-dimer at the time of hospital admission for COVID-19. Clusters C4 (lymphopenia + LDH + CRP), C6 (ferritin + D-dimer), and C7 (D-dimer) had the worst prognosis in terms of in-hospital mortality.
Background
Venous thrombotic events (VTE) are frequent in COVID-19, and elevated plasma D-dimer (pDd) and dyspnea are common in both entities.
Objective
To determine the admission pDd cut-off value ...associated with in-hospital VTE in patients with COVID-19.
Methods
Multicenter, retrospective study analyzing the at-admission pDd cut-off value to predict VTE and anticoagulation intensity along hospitalization due to COVID-19.
Results
Among 9386 patients, 2.2% had VTE: 1.6% pulmonary embolism (PE), 0.4% deep vein thrombosis (DVT), and 0.2% both. Those with VTE had a higher prevalence of tachypnea (42.9% vs. 31.1%; p = 0.0005), basal O2 saturation <93% (45.4% vs. 33.1%; p = 0.0003), higher at admission pDd (median IQR: 1.4 0.6–5.5 vs. 0.6 0.4–1.2 μg/ml; p < 0.0001) and platelet count (median IQR: 208 158–289 vs. 189 148–245 platelets × 10
9
/L; p = 0.0013). A pDd cut-off of 1.1 μg/ml showed specificity 72%, sensitivity 49%, positive predictive value (PPV) 4%, and negative predictive value (NPV) 99% for in-hospital VTE. A cut-off value of 4.7 μg/ml showed specificity of 95%, sensitivity of 27%, PPV of 9%, and NPV of 98%. Overall mortality was proportional to pDd value, with the lowest incidence for each pDd category depending on anticoagulation intensity: 26.3% for those with pDd >1.0 μg/ml treated with prophylactic dose (p < 0.0001), 28.8% for pDd for patients with pDd >2.0 μg/ml treated with intermediate dose (p = 0.0001), and 31.3% for those with pDd >3.0 μg/ml and full anticoagulation (p = 0.0183).
Conclusions
In hospitalized patients with COVID-19, a pDd value greater than 3.0 μg/ml can be considered to screen VTE and to consider full-dose anticoagulation.
New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable ...and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.
To describe the characteristics and prognosis of patients with COPD admitted to the hospital due to SARS-CoV-2 infection.
The SEMI-COVID registry is an ongoing retrospective cohort comprising ...consecutive COVID-19 patients hospitalized in Spain since the beginning of the pandemic in March 2020. Data on demographics, clinical characteristics, comorbidities, laboratory tests, radiology, treatment, and progress are collected. Patients with COPD were selected and compared to patients without COPD. Factors associated with a poor prognosis were analyzed.
Of the 10,420 patients included in the SEMI-COVID registry as of May 21, 2020, 746 (7.16%) had a diagnosis of COPD. Patients with COPD are older than those without COPD (77 years vs 68 years) and more frequently male. They have more comorbidities (hypertension, hyperlipidemia, diabetes mellitus, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, kidney failure) and a higher Charlson Comorbidity Index (2 vs 1, p<0.001). The mortality rate in COPD patients was 38.3% compared to 19.2% in patients without COPD (p<0.001). Male sex, a history of hypertension, heart failure, moderate-severe chronic kidney disease, presence of cerebrovascular disease with sequelae, degenerative neurological disease, dementia, functional dependence, and a higher Charlson Comorbidity Index have been associated with increased mortality due to COVID-19 in COPD patients. Survival was higher among patients with COPD who were treated with hydroxychloroquine (87.1% vs 74.9%, p<0.001) and with macrolides (57.9% vs 50%, p<0.037). Neither prone positioning nor non-invasive mechanical ventilation, high-flow nasal cannula, or invasive mechanical ventilation were associated with a better prognosis.
COPD patients admitted to the hospital with SARS-CoV-2 infection have more severe disease and a worse prognosis than non-COPD patients.
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