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.
Serum levels of CLO can be measured by LC/MS/MS. When prescribing 0.05% CLO, one must bear in mind that, even after an application of 20-30 g, CLO is systemically available and potent enough to induce adrenal gland suppression.
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.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.