Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis

Abstract

Objective To evaluate by means of regression models the relationships between baseline clinical and laboratory data and lung involvement on baseline chest CT and to quantify the thoracic disease using an artificial intelligence tool and a visual scoring system to predict prognosis in patients with COVID-19 pneumonia. Materials and methods This study included 103 (41 women and 62 men; 68.8 years of mean age—range, 29–93 years) with suspicious COVID-19 viral infection evaluated by reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test. All patients underwent CT examinations at the time of admission in addition to clinical and laboratory findings recording. All chest CT examinations were reviewed using a structured report. Moreover, using an artificial intelligence tool we performed an automatic segmentation on CT images based on Hounsfield unit to calculate residual healthy lung parenchyma, ground-glass opacities (GGO), consolidations and emphysema volumes for both right and left lungs. Two expert radiologists, in consensus, attributed at the CT pulmonary disease involvement a severity score using a scale of 5 levels; the score was attributed for GGO and consolidation for each lung, and then, an overall radiological severity visual score was obtained summing the single score. Univariate and multivariate regression analysis was performed. Results Symptoms and comorbidities did not show differences statistically significant in terms of patient outcome. Instead, SpO2 was significantly lower in patients hospitalized in critical conditions or died while age, HS CRP, leukocyte count, neutrophils, LDH, d-dimer, troponin, creatinine and azotemia, ALT, AST and bilirubin values were significantly higher. GGO and consolidations were the main CT patterns (a variable combination of GGO and consolidations was found in 87.8% of patients). CT COVID-19 disease was prevalently bilateral (77.6%) with peripheral distribution (74.5%) and multiple lobes localizations (52.0%). Consolidation, emphysema and residual healthy lung parenchyma volumes showed statistically significant differences in the three groups of patients based on outcome (patients discharged at home, patients hospitalized in stable conditions and patient hospitalized in critical conditions or died) while GGO volume did not affect the patient's outcome. Moreover, the overall radiological severity visual score (cutoff ≥ 8) was a predictor of patient outcome. The highest value of R-squared (R2 = 0.93) was obtained by the model that combines clinical/laboratory findings at CT volumes. The highest accuracy was obtained by clinical/laboratory and CT findings model with a sensitivity, specificity and accuracy, respectively, of 88%, 78% and 81% to predict discharged/stable patients versus critical/died patients. Conclusion In conclusion, both CT visual score and computerized software-based quantification of the consolidation, emphysema and residual healthy lung parenchyma on chest CT images were independent predictors of outcome in patients with COVID-19 pneumonia.

Lymphadenopathy after BNT162b2 Covid-19 Vaccine: Preliminary Ultrasound Findings

“…Recent results have revealed the efficiency of some imaging methods, including chest radiographs and chest computed tomography scans, in the management of COVID-19 disease [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Instead, at the best of our knowledge, this is the first paper describing the appearance of nodes after BNT162b2 Covid-19 vaccine.…”

Breast Cancer Screening during COVID-19 Emergency: Patients and Department Management in a Local Experience

“…Some centers [ 28 ] suggest PCR testing before breast interventional procedures in patient with a BIRADS 5 lesion, such as to reduce the waiting time before surgery. However, this would increase the cost of each procedure, and additionally, the waiting time (from 20 min to 2 h) before each interventional procedure [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. Consequently, it would increase the time lapse between the exams with a reduced number of procedures accomplished per day.…”

The predicting roles of carcinoembryonic antigen and its underlying mechanism in the progression of coronavirus disease 2019

“…The predicting biomarkers are important for clinical decision-making; thus many efforts have been made to identify them in patients with COVID-19 pneumonia. Previously, the in ammatory biomarkers (IL-6, IL-8, IL-10 and ratio of IL-6 to IL-10), patients' characteristics (age) and chest CT images (consolidation, emphysema and residual healthy lung parenchyma) have been reported to predict the prognosis of COVID-19 patients [8,32]. In addition, the novel method of machine learning is also used to precisely evaluate the COVID-19 pneumonia [33].…”

“…Generally, patients' characteristics, nutritional status, clinical symptoms, comorbidities, in ammatory biomarkers and chest CT images are different in terms of patient outcome, however, whether these factors can serve as prognosis predictors for COVID-19 pneumonia is not clear. Regarding to chest CT images, consolidation, emphysema and residual healthy lung parenchyma are regarded as independent predictors in COVID-19 patients [8]. Additionally, high sensitivity C-reactive protein-prealbumin ratio (HsCPAR), high sensitivity C-reactive protein-albumin ratio (HsCAR) and low prognostic nutritional index (PNI) and the ratio of interleukin (IL)-6 to IL-10 were also reported to be related to the prognosis of COVID-19 patients [9,10].…”