Face masks and personal respirators are used to curb the transmission of SARS-CoV-2 in respiratory droplets; filters embedded in some personal protective equipment could be used as a non-invasive sample source for applications, including at-home testing, but information is needed about whether filters are suited to capture viral particles for SARS-CoV-2 detection. In this study, we generated inactivated virus-laden aerosols of 0.3–2 microns in diameter (0.9 µm mean diameter by mass) and dispersed the aerosolized viral particles onto electrostatic face mask filters. The limit of detection for inactivated coronaviruses SARS-CoV-2 and HCoV-NL63 extracted from filters was between 10 to 100 copies/filter for both viruses. Testing for SARS-CoV-2, using face mask filters and nasopharyngeal swabs collected from hospitalized COVID-19-patients, showed that filter samples offered reduced sensitivity (8.5% compared to nasopharyngeal swabs). The low concordance of SARS-CoV-2 detection between filters and nasopharyngeal swabs indicated that number of viral particles collected on the face mask filter was below the limit of detection for all patients but those with the highest viral loads. This indicated face masks are unsuitable to replace diagnostic nasopharyngeal swabs in COVID-19 diagnosis. The ability to detect nucleic acids on face mask filters may, however, find other uses worth future investigation.
PurposeIn pandemic COVID-19, a rapid clinical triage is crucial to determine which patients are in need for hospitalisation. We hypothesised that chest CT and alveolar-arterial oxygen (A-a) gradient may be useful to triage these patients, since it reflects the severity of the pneumonia-associated ventilation/perfusion abnormalities.MethodsA retrospective analysis was performed in consecutive patients (n=235) suspected for COVID-19. The diagnostic protocol included low-dose chest CT and arterial blood gas analysis. In patients with CT-based COVID-19 pneumonia, the association between “need for hospitalisation” and A-a gradient was investigated by multivariable logistic regression model; and, the A-a gradient was tested as predictor for need for hospitalisation using ROC curve analysis and logistic regression model.Results72 out of 235 patients (mean±sd age 55.5±14.6 years, 40% female) screened by chest CT showed evidence for COVID-19 pneumonia. In these patients, A-a gradient was shown to be a predictor of need for hospitalisation, with an optimal decision level (“cut-off”) of 36.4 mmHg (95% CI 0.70–0.91, p<0.001). The A-a gradient was shown to be independently associated with need for hospitalisation (OR 1.97 [95% CI 1.23–3.15], p=0.005, A-a gradient per 10 points) from CT-SS (OR 1.13 [95% CI 0.94–1.36], p=0.191), NEWS (OR 1.19 [95% CI 0.91–1.57], p=0.321) or peripheral oxygen saturation (OR 0.88 [95% CI 0.68–1.14], p=0.345).ConclusionLow dose chest CT and the alveolar-arterial oxygen gradient may serve as rapid and accurate tools to diagnose COVID-19 pneumonia and to select mildly symptomatic patients in need for hospitalisation.
Objectives A decrease of both diffusion capacity (DLCO) and Quality of Life (QoL) was reported after discharge in hospitalized COVID-19 pneumonia survivors. We studied three and 6 month outcomes in hospitalized and non-hospitalized patients. Methods COVID-19 pneumonia survivors ( n = 317) were categorized into non-hospitalized “moderate” cases ( n = 59), hospitalized “severe” cases ( n = 180) and ICU-admitted “critical” cases ( n = 39). We studied DLCO and QoL (Short Form SF-36 health survey) 3 and 6 months after discharge. Data were analyzed using (repeated measures) ANOVA, Kruskal-Wallis or Chi-square test ( p < .05). Results At 3 months DLCO was decreased in 44% of moderate-, 56% of severe- and 82% of critical cases ( p < .003). Mean DLCO in critical cases (64±14%) was lower compared to severe (76 ± 17%) and moderate (81±15%) cases ( p < .001). A total of 159/278 patients had a decreased DLCO (<80%), of whom the DLCO improved after 6 months in 45% (71/159). However the DLCO did not normalize in the majority (89%) of the cases (63 ± 10% vs 68±10%; p < .001). At 3 months, compared to critical cases, moderate cases scored lower on SF-36 domain “general health” ( p < .05); both moderate and severe cases scored lower on the domain of “health change” ( p < .05). At 6 months, there were no differences in SF-36 between the subgroups. Compared to 3 months, in all groups “physical functioning” improved; in contrast all groups scored significantly lower on “non-physical” SF-36 domains. Conclusion Three months after COVID-19 pneumonia, DLCO was still decreased in the more severely affected patients, with an incomplete recovery after 6 months. At 3 months QoL was impaired. At 6 months, while “physical functioning” improved, a decrease in “non-physical” QoL was observed but did not differ between the moderate and severely affected patients.
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