Investigating phenotypes of pulmonary COVID-19 recovery - a longitudinal observational prospective multicenter trial
- Medical University of Innsbruck, Austria
- Data Analytics As a Service Tirol, Austria
- The Karl Landsteiner Institute, Austria
- University Hospital Innsbruck, Austria
- St. Vinzenz Hospital, Austria
Abstract
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
Data availability
The complete R analysis pipeline and the anonymized study data in form of stratified study variables are available as a public GitHub repository: https://github.com/PiotrTymoszuk/CovILD_6_Months. The R code for the key tools used for uni-variate modeling and model quality control (Figures 4 and 5, https://github.com/PiotrTymoszuk/lm_qc_tools) as well as cluster analysis and its quality control (Figures 6 - 7, https://github.com/PiotrTymoszuk/clustering-tools-2) is available at GitHub.
Article and author information
Author details
Judith Löffler-Ragg
Funding
Landes Tirols (GZ 71934)
- Judith Löffler-Ragg
Boehringer Ingelheim (IIS 1199-0424)
- Ivan Tancevski
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: All participants gave written informed consent. The study was approved by the institutional review board at the Medical University of Innsbruck (approval number: 1103/2020), and registered at ClinicalTrials.gov (NCT04416100).
Copyright
© 2022, Sonnweber et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Investigating phenotypes of pulmonary COVID-19 recovery - a longitudinal observational prospective multicenter trial
eLife 11:e72500.
https://doi.org/10.7554/eLife.72500
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