COVID-19 Pneumonia, Quantitative Computed Tomography
Conditions
Keywords
Steroid, COVID-19, Quantitative computed tomography, Compromised lung volume
Brief summary
Since December 2019, the outbreak of coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern. Although corticosteroid therapy represents a milestone in the management of COVID-19, many questions remain unanswered. The optimal type of corticosteroids, timing of initiation, dose, mode of administration, duration, and dose tapering are still unclear. An approach to resolve these issues is to develop accurate tools to assess or monitor the progression of COVID-19 during the corticosteroid therapy process. Quantitative computed tomography (QCT) analysis may serve as a tool for assessing the severity of COVID-19 and for monitoring its progress. However, the effect of steroids on quantitative chest CT parameters during the treatment process remains unknown. In this retrospectively study, we aimed to assess the association between steroid administration and QCT variables in a longitudinal cohort with COVID-19
Detailed description
Since December 2019, the outbreak of coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern. Although corticosteroid therapy represents a milestone in the management of COVID-19, many questions remain unanswered. The optimal type of corticosteroids, timing of initiation, dose, mode of administration, duration, and dose tapering are still unclear. An approach to resolve these issues is to develop accurate tools to assess or monitor the progression of COVID-19 during the corticosteroid therapy process. Quantitative computed tomography (QCT) analysis may serve as a tool for assessing the severity of COVID-19 and for monitoring its progress. However, the effect of steroids on quantitative chest CT parameters during the treatment process remains unknown. In this retrospectively study, we aimed to assess the association between steroid administration and QCT variables in a longitudinal cohort with COVID-19.
Interventions
None listed
Sponsors
Guowei Tu
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 90 Years
Healthy volunteers
No
Inclusion criteria
* (1) age 18-90 years * (2) patients with severe or critical COVID-19.
Exclusion criteria
* (1) hematological or solid malignancies * (2) patients with less than two CT scans during hospital stay * (3) systemic corticosteroid or immunosuppressive therapy in the previous 6 weeks.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Changes in the percentage of compromised lung volume (Δ%CL) at different stages | 31 days | According to different Hounsfield unit (HU) intervals in the quantitative chest CT scan, we divided each lung into nonaerated lung volume (%NNL, 100 to -100 HU), poorly aerated lung volume (%PAL, -101 to -500 HU), normally aerated lung volume (%NAL, -501 to -900 HU), and hyperinflated lung volume (%HI, -901 to -1000 HU) regions. The additional compromised lung volume (%CL) was considered as the sum of %PAL and %NNL (-500 to 100 HU). To monitor COVID-19 progression during the treatment process, we chose changes in the percentage of compromised lung volume (Δ%CL) at different stages (Δ%CL = %CL at different stages-baseline %CL) as the primary outcome. The negative value of Δ%CL thus reflected clinical improvement. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Changes in the percentage of NNL at different stages | 31 days | According to different Hounsfield unit (HU) intervals in the quantitative chest CT scan, we divided each lung into nonaerated lung volume (%NNL, 100 to -100 HU), poorly aerated lung volume (%PAL, -101 to -500 HU), normally aerated lung volume (%NAL, -501 to -900 HU), and hyperinflated lung volume (%HI, -901 to -1000 HU) regions. |
| Changes in the percentage of PAL at different stages | 31 days | According to different Hounsfield unit (HU) intervals in the quantitative chest CT scan, we divided each lung into nonaerated lung volume (%NNL, 100 to -100 HU), poorly aerated lung volume (%PAL, -101 to -500 HU), normally aerated lung volume (%NAL, -501 to -900 HU), and hyperinflated lung volume (%HI, -901 to -1000 HU) regions. Under these circumstances, clinical improvement was reflected by the negative value of Δ%NNL and Δ%PAL, and the positive value of Δ%NAL. |
| Changes in the percentage of NAL at different stages | 31 days | According to different Hounsfield unit (HU) intervals in the quantitative chest CT scan, we divided each lung into nonaerated lung volume (%NNL, 100 to -100 HU), poorly aerated lung volume (%PAL, -101 to -500 HU), normally aerated lung volume (%NAL, -501 to -900 HU), and hyperinflated lung volume (%HI, -901 to -1000 HU) regions. Under these circumstances, clinical improvement was reflected by the negative value of Δ%NNL and Δ%PAL, and the positive value of Δ%NAL. |
| Changes in the percentage of HL at different stages | 31 days | According to different Hounsfield unit (HU) intervals in the quantitative chest CT scan, we divided each lung into nonaerated lung volume (%NNL, 100 to -100 HU), poorly aerated lung volume (%PAL, -101 to -500 HU), normally aerated lung volume (%NAL, -501 to -900 HU), and hyperinflated lung volume (%HI, -901 to -1000 HU) regions. Under these circumstances, clinical improvement was reflected by the negative value of Δ%NNL and Δ%PAL, and the positive value of Δ%NAL. |
Countries
China
Outcome results
None listed