Prediction of the Development of Pulmonary Fibrosis Using Serial Thin-Section CT and Clinical Features in Patients Discharged after Treatment for COVID-19 Pneumonia
Abstract:Objective: To identify predictors of pulmonary fibrosis development by combining follow-up thin-section CT findings and clinical features in patients discharged after treatment for COVID-19. Materials and Methods: This retrospective study involved 32 confirmed COVID-19 patients who were divided into two groups according to the evidence of fibrosis on their latest follow-up CT imaging. Clinical data and CT imaging features of all the patients in different stages were collected and analyzed for comparison. Resul… Show more
Background
The tomographic findings in COVID-19, its classification, a brief overview of the application of artificial intelligence, and the stages during the course of the disease in patients with moderate COVID-19
Main body
Chest CT allows us to follow the course of COVID-19 in an objective way; each phase has characteristic imaging findings and, consequently, takes the corresponding measures.
A search was made in the PubMed database with the keywords extracted from the DeCs and the combinations of these. Only articles published between December 2019 and June 2020 were included. The search was limited to the English language.
Conclusions
CT serves to monitor the course of the disease since it assesses the severity of lung involvement. The most frequent finding is bilateral ground glass opacities with a subpleural distribution. The progression occurs in two phases: one slow and one fast. At discharge, the patient may have ground glass opacities or areas that will later become fibrosis, leaving sequelae for life.
Background
The tomographic findings in COVID-19, its classification, a brief overview of the application of artificial intelligence, and the stages during the course of the disease in patients with moderate COVID-19
Main body
Chest CT allows us to follow the course of COVID-19 in an objective way; each phase has characteristic imaging findings and, consequently, takes the corresponding measures.
A search was made in the PubMed database with the keywords extracted from the DeCs and the combinations of these. Only articles published between December 2019 and June 2020 were included. The search was limited to the English language.
Conclusions
CT serves to monitor the course of the disease since it assesses the severity of lung involvement. The most frequent finding is bilateral ground glass opacities with a subpleural distribution. The progression occurs in two phases: one slow and one fast. At discharge, the patient may have ground glass opacities or areas that will later become fibrosis, leaving sequelae for life.
“…Some of these will develop an acute respiratory distress syndrome (ARDS) with high mortality rates 2 . CT ndings have been described in recent publications [3][4][5][6][7] . Ground-glass opacities (GGO) with or without consolidation with a peripheral, posterior and lower distribution are commonly seen 3 .…”
Section: Introductionmentioning
confidence: 96%
“…Areas of GGO become coalescent, and other patterns, including consolidations appear and peak at 6-13 days 8 . Signs of brosis has been described at the end-stage of the disease or as a sequalae 9,5 .…”
OBJECTIVESCT findings of COVID-19 infected patients has been well described, but it it’s roll in depicting signs of fibrosis in critically ill patients remains unclear. To our knowledge, there are no radiopathological correlations of the pulmonary pathology. Exudative and proliferative diffuse alveolar damage (DAD) are the most commonly reported injury. Few studies describe fibrosis, the last phase of DAD. Our study correlates post-mortem chest US and CT findings of COVID-19 infected patients with the histopathology from biopsies taken of the lung. It focuses on the role of CT to depict fibrosis. METHODSThis is a prospective observational study of six consecutive deceased patients infected with COVID-19. Post-mortem chest CTs and US were performed within 24 hours of death. CT and US were used to obtain biopsies of different radiological patterns. Pre-mortem CT examinations were also retrospectively evaluated. RESULTSOn CT, all patients presented with extensive areas of consolidation and ground-glass opacities affecting most segments of the lung. Pleural effusion was present in all cases. Four of the patients showed signs of fibrosis. On US, subpleural consolidation, pleural thickening, and B-pattern were present.All patients showed different stages of DAD, mostly proliferative DAD. Four patients presented with fibrotic DAD, all of which had been admitted for over three weeks and correlated with the CT findings of fibrosis. CONCLUSIONIn our study, signs of fibrosis on CT show a histopathological correlation. CT may be useful to identify the group of COVID-infected patients that develop fibrosis as a marker of poor prognosis, in the late stage of the disease.
“…Chest computed tomography (CT) plays an important role in the diagnosis and treatment of patients with COVID-19 that helps in the diagnosis by depicting lung abnormalities and in the evaluation of the progress of disease and response to treatment [6][7][8]. Several studies have described the imaging and clinical features of patients during hospitalization or discharged after treatment with COVID-19 using serial thin-section CT [9][10][11][12][13][14]. At present, it is not known if COVID-19 patients who survived pneumonia would be at risk of chronic sequelae.…”
Background: Thousands of the Coronavirus Disease 2019 (COVID-19) patients have been discharged from hospitals, long-term follow-up studies are required to evaluate the prevalence of post-COVID-19 fibrosis.Methods: This study involves 462 laboratory confirmed patients with COVID-19 who were admitted to Shenzhen Third People’s Hospital from January 11, 2020 to April 26, 2020. A total of 457 patients underwent thin-section chest CT scans during the hospitalization or after discharge to identify the pulmonary lesion. A total of 289 patients were followed up from 90 days to 150 days after the onset of the disease.Results: Parenchymal bands, irregular interfaces, meshwork and traction bronchiectasis were the most common CT features in all COVID-19 patients. 86.87%, 74.40%, 79.56%, 68.12% and 62.03% patients developed with pulmonary fibrosis and 4.53%, 19.61%, 18.02%, 38.30% and 48.98% patients reversed pulmonary fibrosis during the 0-30, 31-60, 61-90, 91-120 and >120 days after onset, respectively. It was observed that Age, BMI, Fever, and Highest PCT were predictive factors for sustaining fibrosis even after 90 days from onset. A predictive model of the persistence with pulmonary fibrosis was developed based-on the Logistic Regression method with an accuracy, PPV, NPV, Sensitivity and Specificity of the model of 76%, 71%, 79%, 67%, and 82%, respectively. Only a fraction of COVID-19 patients suffered with abnormal lung function after 90 days from onset, and the ratio of abnormal lung function did not differ on a statistically significant level between the fibrotic and non-fibrotic groups.Conclusions: Long-term pulmonary fibrosis was more likely to develop in patients with older age, high BMI, severe/critical condition, fever, long time to turn the viral RNA negative, pre-existing disease and delay to admission. Fibrosis developed in COVID-19 patients could be reversed in about a half of the patients after 120 days from onset. The pulmonary function of most of COVID-19 patients with pulmonary fibrosis could turn to normal condition after three months from onset. An effective prediction model with an average Area Under the Curve (AUC) of 0.84 was established to predict the persistence of pulmonary fibrosis in COVID-19 patients for early diagnosis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
