BioAcyl Corp
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Sonnweber, T., Tymoszuk, P., Sahanic, S., Boehm, A., Pizzini, A., & Luger, A., et al. (2022). Investigating phenotypes of pulmonary COVID-19 recovery - a longitudinal observational prospective multicenter trial. eLife, 11, e72500. Added by: Dr. Enrique Feoli (11/02/2022, 13:34) Last edited by: Dr. Enrique Feoli (11/02/2022, 13:35) |
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| Resource type: Journal Article DOI: 10.7554/eLife.72500 ID no. (ISBN etc.): 2050-084X BibTeX citation key: Sonnweber2022 View all bibliographic details  |
Categories: BioAcyl Corp Subcategories: COVID-19 Creators: Aichner, Boehm, Egger, Grubwieser, Hoermann, Koppelstätter, Kurz, Löffler-Ragg, Luger, Nairz, Pizzini, Puchner, Sahanic, Schwabl, Sonnweber, Tancevski, Tymoszuk, Weiss, Widmann, Wöll Collection: eLife |
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| Abstract |
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Background: The optimal procedures to prevent, identify, monitor and treat long-term pulmonary sequelae of COVID-19 are elusive. Here, we characterized the kinetics of respiratory and symptom recovery following COVID-19. Methods: We conducted a longitudinal, multi-center observational study in ambulatory and hospitalized COVID-19 patients recruited in early 2020 (n = 145). Pulmonary computed tomography (CT) and lung function (LF) readouts, symptom prevalence, clinical and laboratory parameters were collected during acute COVID-19 and at 60-, 100- and 180-days follow-up visits. Recovery kinetics and risk factors were investigated by logistic regression. Classification of clinical features and participants was accomplished by unsupervised and semi-supervised multi-parameter clustering and machine learning. Results: At the six-month follow-up, 49% of participants reported persistent symptoms. The frequency of structural lung CT abnormalities ranged from 18% in the mild outpatient cases to 76% in the ICU convalescents. Prevalence of impaired LF ranged from 14% in the mild outpatient cases to 50% in the ICU survivors. Incomplete radiological lung recovery was associated with increased anti-S1/S2 antibody titer, IL-6 and CRP levels at the early follow-up. We demonstrated that the risk of perturbed pulmonary recovery could be robustly estimated at early follow-up by clustering and machine learning classifiers employing solely non-CT and non-LF parameters. Conclusion: The severity of acute COVID-19 and protracted systemic inflammation is strongly linked to persistent structural and functional lung abnormality. Automated screening of multi-parameter health record data may assist at the prediction of incomplete pulmonary recovery and optimize COVID-19 follow-up management. Funding: The State of Tyrol (GZ 71934), Boehringer Ingelheim/Investigator initiated study (IIS 1199-0424). Trial Registration: ClinicalTrials.gov: NCT04416100
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Publisher: eLife Sciences Publications, Ltd
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