A Cross-Sectional Study Conducted in Tehran, Iran, Analyzed the Typical Patterns of Primary Lung CT Scans in Critically Ill COVID-19 Patients Using a Semi-Quantitative Scoring System

Background: Given the ongoing COVID-19 global pandemic and the potential for respiratory failure in several COVID-19 patients, early diagnosis and timely treatment are of paramount importance. Objectives: In this context, we aimed to investigate the role of initial chest computed tomography (CT) in predicting the severity of COVID-19. Methods: The study, conducted at Imam Hossein Hospital between March 6, 2020, and May 6, 2020, was cross-sectional in nature. All patients diagnosed with severe to critical COVID-19 underwent high-resolution chest CT (HRCT), and the findings of the chest CT were meticulously analyzed using a semi-quantitative scoring system. Results: Out of 47 patients with severe to critical COVID-19, 72.4% were male, with a mean age of 62 ± 14 years. The most common chest CT findings were ground-glass opacity (55%) and consolidation (30%). Bilateral involvement was observed in all patients, with the most frequently affected areas being the peribronchovascular distribution (76%) and peripheral distribution (74%). Most patients had pulmonary involvement across all five lobes, with scores ranging from a minimum of 3.25 to a maximum of 21.25 (CI: 12 - 15; mean: 13.25). Conclusions: This study suggests a relationship between the initial chest CT scan findings and the severity of respiratory disease in COVID-19 patients. However, further studies are needed to confirm these findings.

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Journal
Journal of Microbiota
Published
2024-09-01
DOI
https://doi.org/10.5812/jmb-143267
Primary Topic
COVID-19 diagnosis using AI
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article
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article

A Cross-Sectional Study Conducted in Tehran, Iran, Analyzed the Typical Patterns of Primary Lung CT Scans in Critically Ill COVID-19 Patients Using a Semi-Quantitative Scoring System

Shahram Sayadi, Masoomeh Raoufi, Mohammad Torabi, Sara Salarian
Journal of Microbiota
COVID-19 diagnosis using AI
article

A Cross-Sectional Study Conducted in Tehran, Iran, Analyzed the Typical Patterns of Primary Lung CT Scans in Critically Ill COVID-19 Patients Using a Semi-Quantitative Scoring System

Shahram Sayadi, Masoomeh Raoufi, Mohammad Torabi, Sara Salarian
article en

Abstract

Background: Given the ongoing COVID-19 global pandemic and the potential for respiratory failure in several COVID-19 patients, early diagnosis and timely treatment are of paramount importance. Objectives: In this context, we aimed to investigate the role of initial chest computed tomography (CT) in predicting the severity of COVID-19. Methods: The study, conducted at Imam Hossein Hospital between March 6, 2020, and May 6, 2020, was cross-sectional in nature. All patients diagnosed with severe to critical COVID-19 underwent high-resolution chest CT (HRCT), and the findings of the chest CT were meticulously analyzed using a semi-quantitative scoring system. Results: Out of 47 patients with severe to critical COVID-19, 72.4% were male, with a mean age of 62 ± 14 years. The most common chest CT findings were ground-glass opacity (55%) and consolidation (30%). Bilateral involvement was observed in all patients, with the most frequently affected areas being the peribronchovascular distribution (76%) and peripheral distribution (74%). Most patients had pulmonary involvement across all five lobes, with scores ranging from a minimum of 3.25 to a maximum of 21.25 (CI: 12 - 15; mean: 13.25). Conclusions: This study suggests a relationship between the initial chest CT scan findings and the severity of respiratory disease in COVID-19 patients. However, further studies are needed to confirm these findings.

Journal of MicrobiotaVol. 1(2)
Imam Hossein Hospital (IR), Shahid Beheshti University of Medical Sciences (IR)
Good health and well-being
Openalex Percentile: Top 76%
COVID-19 diagnosis using AI
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A Cross-Sectional Study Conducted in Tehran, Iran, Analyzed the Typical Patterns of Primary Lung CT Scans in Critically Ill COVID-19 Patients Using a Semi-Quantitative Scoring System — Shahram Sayadi, Masoomeh Raoufi, et al. · Journal of Microbiota (2024) | TGRS Research Map | TGRS