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1.
PLoS One ; 18(3): e0283459, 2023.
Article in English | MEDLINE | ID: covidwho-2270007

ABSTRACT

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.


Subject(s)
COVID-19 , Pulmonary Embolism , Humans , COVID-19/complications , COVID-19/diagnosis , Pulmonary Embolism/diagnostic imaging , Patients , Fibrin Fibrinogen Degradation Products/analysis , Emergency Service, Hospital , Retrospective Studies , COVID-19 Testing
2.
PLoS One ; 17(6): e0270548, 2022.
Article in English | MEDLINE | ID: covidwho-1910685

ABSTRACT

BACKGROUND: COVID-19 is an ongoing pandemic leading to exhaustion of the hospital care system. Our health care system has to deal with a high level of sick leave of health care workers (HCWs) with COVID-19 related complaints, in whom an infection with SARS-CoV-2 has to be ruled out before they can return back to work. The aim of the present study is to investigate if the recently described CoLab-algorithm can be used to exclude COVID-19 in a screening setting of HCWs. METHODS: In the period from January 2021 till March 2021, HCWs with COVID-19-related complaints were prospectively collected and included in this study. Next to the routinely performed SARS-CoV-2 RT-PCR, using a set of naso- and oropharyngeal swab samples, two blood tubes (one EDTA- and one heparin-tube) were drawn for analysing the 10 laboratory parameters required for running the CoLab-algorithm. RESULTS: In total, 726 HCWs with a complete CoLab-laboratory panel were included in this study. In this group, 684 HCWs were tested SARS-CoV-2 RT-PCR negative and 42 cases RT-PCR positive. ROC curve analysis showed an area under the curve (AUC) of 0.853 (95% CI: 0.801-0.904). At a safe cut-off value for excluding COVID-19 of -6.525, the sensitivity was 100% with a specificity of 34% (95% CI: 21 to 49%). No SARS-CoV-2 RT-PCR cases were missed with this cut-off and COVID-19 could be safely ruled out in more than one third of HCWs. CONCLUSION: The CoLab-score is an easy and reliable algorithm that can be used for screening HCWs with COVID-19 related complaints. A major advantage of this approach is that the results of the score are available within 1 hour after collecting the samples. This results in a faster return to labour process of a large part of the COVID-19 negative HCWs (34%), next to a reduction in RT-PCR tests (reagents and labour costs) that can be saved.


Subject(s)
COVID-19 , Algorithms , COVID-19/diagnosis , Health Personnel , Hematologic Tests , Humans , SARS-CoV-2
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