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Early warning scores to assess the probability of critical illness in patients with COVID-19

OBJECTIVE: Validated clinical risk scores are needed to identify patients with COVID-19 at risk of severe disease and to guide triage decision-making during the COVID-19 pandemic. The objective of the current study was to evaluate the performance of early warning scores (EWS) in the ED when identifying patients with COVID-19 who will require intensive care unit (ICU) admission for high-flow-oxygen usage or mechanical ventilation. METHODS: Patients with a proven SARS-CoV-2 infection with complete resuscitate orders treated in nine hospitals between 27 February and 30 July 2020 needing hospital admission were included. Primary outcome was the performance of EWS in identifying patients needing ICU admission within 24 hours after ED presentation. RESULTS: In total, 1501 patients were included. Median age was 71 (range 19-99) years and 60.3% were male. Of all patients, 86.9% were admitted to the general ward and 13.1% to the ICU within 24 hours after ED admission. ICU patients had lower peripheral oxygen saturation (86.7% vs 93.7, p≤0.001) and had a higher body mass index (29.2 vs 27.9 p=0.043) compared with non-ICU patients. National Early Warning Score 2 (NEWS2) ≥ 6 and q-COVID Score were superior to all other studied clinical risk scores in predicting ICU admission with a fair area under the receiver operating characteristics curve of 0.740 (95% CI 0.696 to 0.783) and 0.760 (95% CI 0.712 to 0.800), respectively. NEWS2 ≥6 and q-COVID Score ≥3 discriminated patients admitted to the ICU with a sensitivity of 78.1% and 75.9%, and specificity of 56.3% and 61.8%, respectively. CONCLUSION: In this multicentre study, the best performing models to predict ICU admittance were the NEWS2 and the Quick COVID-19 Severity Index Score, with fair diagnostic performance. However, due to the moderate performance, these models cannot be clinically used to adequately predict the need for ICU admission within 24 hours in patients with SARS-CoV-2 infection presenting at the ED

Does atrial fibrillation affect prognosis in hospitalised COVID-19 patients? A multicentre historical cohort study in the Netherlands

Objectives The aim of this multicentre COVID-PREDICT study (a nationwide observational cohort study that aims to better understand clinical course of COVID-19 and to predict which COVID-19 patients should receive which treatment and which type of care) was to determine the association between atrial fibrillation (AF) and mortality, intensive care unit (ICU) admission, complications and discharge destination in hospitalised COVID-19 patients.Setting Data from a historical cohort study in eight hospitals (both academic and non-academic) in the Netherlands between January 2020 and July 2021 were used in this study.Participants 3064 hospitalised COVID-19 patients >18 years old.Primary and secondary outcome measures The primary outcome was the incidence of new-onset AF during hospitalisation. Secondary outcomes were the association between new-onset AF (vs prevalent or non-AF) and mortality, ICU admissions, complications and discharge destination, performed by univariable and multivariable logistic regression analyses.Results Of the 3064 included patients (60.6% men, median age: 65 years, IQR 55–75 years), 72 (2.3%) patients had prevalent AF and 164 (5.4%) patients developed new-onset AF during hospitalisation. Compared with patients without AF, patients with new-onset AF had a higher incidence of death (adjusted OR (aOR) 1.71, 95% CI 1.17 to 2.59) an ICU admission (aOR 5.45, 95% CI 3.90 to 7.61). Mortality was non-significantly different between patients with prevalent AF and those with new-onset AF (aOR 0.97, 95% CI 0.53 to 1.76). However, new-onset AF was associated with a higher incidence of ICU admission and complications compared with prevalent AF (OR 6.34, 95% CI 2.95 to 13.63, OR 3.04, 95% CI 1.67 to 5.55, respectively).Conclusion New-onset AF was associated with an increased incidence of death, ICU admission, complications and a lower chance to be discharged home. These effects were far less pronounced in patients with prevalent AF. Therefore, new-onset AF seems to represent a marker of disease severity, rather than a cause of adverse outcomes

Predicting mortality of individual patients with COVID-19: A multicentre Dutch cohort

Objective Develop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital. Design Retrospective cohort study. Setting A multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020. Participants SARS-CoV-2 positive patients (age ≥18) admitted to the hospital. Main outcome measures 21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis. Results 2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81). Conclusion Both models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage

Klinisch beloop van covid-19 in Nederland: Een overzicht van 2607 ziekenhuispatiënten uit de eerste golf

OBJECTIVE: To systematically collect clinical data from patients with a proven COVID-19 infection in the Netherlands. DESIGN: Data from 2579 patients with COVID-19 admitted to 10 Dutch centers in the period February to July 2020 are described. The clinical data are based on the WHO COVID case record form (CRF) and supplemented with patient characteristics of which recently an association disease severity has been reported. METHODS: Survival analyses were performed as primary statistical analysis. These Kaplan-Meier curves for time to (early) death (3 weeks) have been determined for pre-morbid patient characteristics and clinical, radiological and laboratory data at hospital admission. RESULTS: Total in-hospital mortality after 3 weeks was 22.2% (95% CI: 20.7% - 23.9%), hospital mortality within 21 days was significantly higher for elderly patients (> 70 years; 35, 0% (95% CI: 32.4% - 37.8%) and patients who died during the 21 days and were admitted to the intensive care (36.5% (95% CI: 32.1% - 41.3%)). Apart from that, in this Dutch population we also see a risk of early death in patients with co-morbidities (such as chronic neurological, nephrological and cardiac disorders and hypertension), and in patients with more home medication and / or with increased urea and creatinine levels. CONCLUSION: Early death due to a COVID-19 infection in the Netherlands appears to be associated with demographic variables (e.g. age), comorbidity (e.g. cardiovascular disease) but also disease char-acteristics at admission