Systematic screening versus clinical gestalt in the diagnosis of pulmonary embolism in COVID-19 patients in the emergency department.

BACKGROUND: Diagnosing concomitant pulmonary embolism (PE) in COVID-19 patients remains challenging. As such, PE may be overlooked. We compared the diagnostic yield of systematic PE-screening based on the YEARS-algorithm to PE-screening based on clinical gestalt in emergency department (ED) patients with COVID-19. METHODS: We included all ED patients who were admitted because of COVID-19 between March 2020 and February 2021. Patients already receiving anticoagulant treatment were excluded. Up to April 7, 2020, the decision to perform CT-pulmonary angiography (CTPA) was based on physician's clinical gestalt (clinical gestalt cohort). From April 7 onwards, systematic PE-screening was performed by CTPA if D-dimer level was ≥1000 ug/L, or ≥500 ug/L in case of ≥1 YEARS-item (systematic screening cohort). RESULTS: 1095 ED patients with COVID-19 were admitted. After applying exclusion criteria, 289 were included in the clinical gestalt and 574 in the systematic screening cohort. The number of PE diagnoses was significantly higher in the systematic screening cohort compared to the clinical gestalt cohort: 8.2% vs. 1.0% (3/289 vs. 47/574; p<0.001), even after adjustment for differences in patient characteristics (adjusted OR 8.45 (95%CI 2.61-27.42, p<0.001) for PE diagnosis). In multivariate analysis, D-dimer (OR 1.09 per 1000 µg/L increase, 95%CI 1.06-1.13, p<0.001) and CRP >100 mg/L (OR 2.78, 95%CI 1.37-5.66, p = 0.005) were independently associated with PE. CONCLUSION: In ED patients with COVID-19, the number of PE diagnosis was significantly higher in the cohort that underwent systematic PE screening based on the YEARS-algorithm in comparison with the clinical gestalt cohort, with a number needed to test of 7.1 CTPAs to detect one PE.

Prevalence, pathophysiology, prediction and health-related quality of life of long COVID: study protocol of the longitudinal multiple cohort CORona Follow Up (CORFU) study.

INTRODUCTION: The variety, time patterns and long-term prognosis of persistent COVID-19 symptoms (long COVID-19) in patients who suffered from mild to severe acute COVID-19 are incompletely understood. Cohort studies will be combined to describe the prevalence of long COVID-19 symptoms, and to explore the pathophysiological mechanisms and impact on health-related quality of life. A prediction model for long COVID-19 will be developed and internally validated to guide care in future patients. METHODS AND ANALYSIS: Data from seven COVID-19 cohorts will be aggregated in the longitudinal multiple cohort CORona Follow Up (CORFU) study. CORFU includes Dutch patients who suffered from COVID-19 at home, were hospitalised without or with intensive care unit treatment, needed inpatient or outpatient rehabilitation and controls who did not suffer from COVID-19. Individual cohort study designs were aligned and follow-up has been synchronised. Cohort participants will be followed up for a maximum of 24 months after acute infection. Next to the clinical characteristics measured in individual cohorts, the CORFU questionnaire on long COVID-19 outcomes and determinants will be administered digitally at 3, 6, 12, 18 and 24 months after the infection. The primary outcome is the prevalence of long COVID-19 symptoms up to 2 years after acute infection. Secondary outcomes are health-related quality of life (eg, EQ-5D), physical functioning, and the prevalence of thromboembolic complications, respiratory complications, cardiovascular diseases and endothelial dysfunction. A prediction model and a patient platform prototype will be developed. ETHICS AND DISSEMINATION: Approval was obtained from the medical research ethics committee of Maastricht University Medical Center+ and Maastricht University (METC 2021-2990) and local committees of the participating cohorts. The project is supported by ZonMW and EuroQol Research Foundation. Results will be published in open access peer-reviewed scientific journals and presented at (inter)national conferences. TRIAL REGISTRATION NUMBER: NCT05240742.

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.

In-Depth Investigation of Conjunctival Swabs and Tear Fluid of Symptomatic COVID-19 Patients, an Observational Cohort Study.

Purpose: The putative presence of SARS-CoV-2 in ocular specimen puts healthcare workers at risk. We thoroughly examined conjunctival swabs and tear fluid in a large cohort of COVID-19 patients. Methods: A total of 243 symptomatic laboratory-confirmed COVID-19 patients were included in this observational multicenter study. Conjunctival swabs were analyzed by reverse transcription polymerase chain reaction for detection of SARS-CoV-2 RNA. Next-generation sequencing and phylogenetic analysis were performed to identify viral strains and to determine tissue tropism. Schirmer tear samples from 43 hospitalized COVID-19 patients and 25 healthy controls were analyzed by multiplex cytokine immunoassays. Results: Viral SARS-CoV-2 RNA was detected in conjunctival swabs from 17 (7.0%) of 243 COVID-19 patients. Conjunctival samples were positive for viral SARS-CoV-2 RNA as long as 12 days after disease onset. Cycle threshold (Ct) values for conjunctival swabs (mean 34.5 ± 5.1) were significantly higher than nasopharyngeal swabs (mean 16.7 ± 3.6). No correlation between Ct values of conjunctival and nasopharyngeal swabs was observed. The majority of positive conjunctival samples were detected only once and primarily during the first visit. Next-generation sequencing analysis revealed that the virus strain found in the conjunctiva was most often identical to the one found in the nasopharynx. Tear cytokine levels IL-1ß and IL-6 were elevated in COVID-19 patients compared to healthy controls. Conclusions: Conjunctival samples that were positive for SARS-CoV-2 RNA contained the same viral strain as the nasopharynx. Translational Relevance: The presence of SARS-CoV-2 viral RNA and elevated cytokines in tear fluid confirm the involvement of the ocular surface in COVID-19 disease.

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.

Can predicting COVID-19 mortality in a European cohort using only demographic and comorbidity data surpass age-based prediction: An externally validated study.

OBJECTIVE: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. METHODS: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. RESULTS: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. CONCLUSION: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features.

Clinical impact of human metapneumovirus infections before and during the COVID-19 pandemic.

BACKGROUND: The first outbreak of coronavirus disease 2019 (COVID-19) occurred in March 2020 in Europe, which is normally the peak incidence period of human metapneumovirus (HMPV) infections, implying cocirculation and potentially causing competition between them. METHODS: We investigated differences in clinical characteristics and outcomes of HMPV infections in hospitalized patients before (January 2016-28 February, 2020) and HMPV and COVID-19 during part of the COVID-19 pandemic (28 February, 2020-1 April, 2020). RESULTS: A total of 239 HMPV patients and 303 COVID-19 patients were included. Incidence of HMPV peaked in March. Despite a 324% increase in HMPV testing during the COVID-19 outbreak, incidence of HMPV remained stable. Clinical characteristics showed 25 (11%) ICU admissions and 14 (6%) deaths. History of myocardial infarction, higher age and lower BMI were independently associated with increased 30-day mortality. Clinical characteristics of HMPV-infected patients did not differ between the non-COVID-19 period and the examined COVID-19 period except for length of hospital stay (7 vs. 5 days). HMPV infection and COVID-19 shared many clinical features but HMPV was associated with female gender, elderly patients and chronic conditions (COPD and chronic heart failure). Clinical outcomes did not differ between the viruses during the COVID-19 period. CONCLUSIONS: The clinical impact of HMPV infection did not change during the COVID-19 outbreak in terms of incidence and/or disease severity; hence, HMPV and SARS-CoV-2 are probably co-circulating independently. Despite the current clinical focus on the COVID-19 pandemic, clinicians should keep in mind that HMPV-infection may mimic COVID-19 and is also associated with serious adverse outcomes.