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Stratifying the Early Radiologic Trajectory in Dyspneic Patients with COVID-19 Pneumonia

Overview
Journal PLoS One
Date 2021 Oct 22
PMID 34679127
Citations 1
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Abstract

Objective: This study aimed to stratify the early pneumonia trajectory on chest radiographs and compare patient characteristics in dyspneic patients with coronavirus disease 2019 (COVID-19).

Materials And Methods: We retrospectively included 139 COVID-19 patients with dyspnea (87 men, 62.7±16.3 years) and serial chest radiographs from January to September 2020. Radiographic pneumonia extent was quantified as a percentage using a previously-developed deep learning algorithm. A group-based trajectory model was used to categorize the pneumonia trajectory after symptom onset during hospitalization. Clinical findings, and outcomes were compared, and Cox regression was performed for survival analysis.

Results: Radiographic pneumonia trajectories were categorized into four groups. Group 1 (n = 83, 59.7%) had negligible pneumonia, and group 2 (n = 29, 20.9%) had mild pneumonia. Group 3 (n = 13, 9.4%) and group 4 (n = 14, 10.1%) showed similar considerable pneumonia extents at baseline, but group 3 had decreasing pneumonia extent at 1-2 weeks, while group 4 had increasing pneumonia extent. Intensive care unit admission and mortality were significantly more frequent in groups 3 and 4 than in groups 1 and 2 (P < .05). Groups 3 and 4 shared similar clinical and laboratory findings, but thrombocytopenia (<150×103/μL) was exclusively observed in group 4 (P = .016). When compared to groups 1 and 2, group 4 (hazard ratio, 63.3; 95% confidence interval, 7.9-504.9) had a two-fold higher risk for mortality than group 3 (hazard ratio, 31.2; 95% confidence interval, 3.5-280.2), and this elevated risk was maintained after adjusting confounders.

Conclusion: Monitoring the early radiologic trajectory beyond baseline further prognosticated at-risk COVID-19 patients, who potentially had thrombo-inflammatory responses.

Citing Articles

Temporal Radiographic Trajectory and Clinical Outcomes in COVID-19 Pneumonia: A Longitudinal Study.

Ahn D, Seo Y, Goo T, Jeong J, Park T, Yoon S J Korean Med Sci. 2025; 40(9):e25.

PMID: 40065711 PMC: 11893352. DOI: 10.3346/jkms.2025.40.e25.


Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case.

Derevitskii I, Mramorov N, Usoltsev S, Kovalchuk S J Pers Med. 2022; 12(8).

PMID: 36013274 PMC: 9409816. DOI: 10.3390/jpm12081325.

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