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Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study
Tomography ; 8(3):1578-1585, 2022.
Article in English | MDPI | ID: covidwho-1894079
ABSTRACT
(1)

Background:

Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between residual respiratory symptoms and abnormal QCT results. (2)

Methods:

In this prospective monocentric trial performed during the 'first wave';of the Italian pandemic, i.e., from March to May 2020, we aimed to test the relationship between %deltaCL (variation of %CL-compromised lung volume) and variations of symptoms-dyspnea, cough and chest pain-at follow-up clinical assessment after hospitalization. (3)

Results:

282 patients (95 females, 34%) with a median age of 60 years (IQR, 51–69) were included. We reported a correlation between changing lung abnormalities measured by QCT, and residual symptoms at short-term follow up after COVID-19 pneumonia. Independently from age, a low percentage of surviving patients (1–4%) may present residual respiratory symptoms at approximately two months after discharge. QCT was able to quantify the extent of residual lung damage underlying such symptoms, as the reduction of both %PAL (poorly aerated lung) and %CL volumes was correlated to their disappearance. (4) Conclusions QCT may be used as an objective metric for the measurement of COVID-19 sequelae.
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Collection: Databases of international organizations Database: MDPI Type of study: Cohort study / Observational study / Prognostic study / Randomized controlled trials Topics: Long Covid Language: English Journal: Tomography Year: 2022 Document Type: Article

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Collection: Databases of international organizations Database: MDPI Type of study: Cohort study / Observational study / Prognostic study / Randomized controlled trials Topics: Long Covid Language: English Journal: Tomography Year: 2022 Document Type: Article