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.
Reviewing Editor
- Joshua T Schiffer, Fred Hutchinson Cancer Research Center, United States
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).
Version history
- Preprint posted: June 25, 2021 (view preprint)
- Received: July 26, 2021
- Accepted: January 19, 2022
- Accepted Manuscript published: February 8, 2022 (version 1)
- Version of Record published: March 4, 2022 (version 2)
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
Further reading
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- Medicine
- Microbiology and Infectious Disease
- Epidemiology and Global Health
- Immunology and Inflammation
eLife has published articles on a wide range of infectious diseases, including COVID-19, influenza, tuberculosis, HIV/AIDS, malaria and typhoid fever.
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- Cancer Biology
- Epidemiology and Global Health
Background:
Age is the most important risk factor for cancer, but aging rates are heterogeneous across individuals. We explored a new measure of aging-Phenotypic Age (PhenoAge)-in the risk prediction of site-specific and overall cancer.
Methods:
Using Cox regression models, we examined the association of Phenotypic Age Acceleration (PhenoAgeAccel) with cancer incidence by genetic risk group among 374,463 participants from the UK Biobank. We generated PhenoAge using chronological age and nine biomarkers, PhenoAgeAccel after subtracting the effect of chronological age by regression residual, and an incidence-weighted overall cancer polygenic risk score (CPRS) based on 20 cancer site-specific polygenic risk scores (PRSs).
Results:
Compared with biologically younger participants, those older had a significantly higher risk of overall cancer, with hazard ratios (HRs) of 1.22 (95% confidence interval, 1.18–1.27) in men, and 1.26 (1.22–1.31) in women, respectively. A joint effect of genetic risk and PhenoAgeAccel was observed on overall cancer risk, with HRs of 2.29 (2.10–2.51) for men and 1.94 (1.78–2.11) for women with high genetic risk and older PhenoAge compared with those with low genetic risk and younger PhenoAge. PhenoAgeAccel was negatively associated with the number of healthy lifestyle factors (Beta = –1.01 in men, p<0.001; Beta = –0.98 in women, p<0.001).
Conclusions:
Within and across genetic risk groups, older PhenoAge was consistently related to an increased risk of incident cancer with adjustment for chronological age and the aging process could be retarded by adherence to a healthy lifestyle.
Funding:
This work was supported by the National Natural Science Foundation of China (82230110, 82125033, 82388102 to GJ; 82273714 to MZ); and the Excellent Youth Foundation of Jiangsu Province (BK20220100 to MZ).
