Quantitative Evaluation of COVID-19 Pneumonia Lung Extension by Specific Software and Correlation with Patient Clinical Outcome

Abstract

Lung infection named as COVID-19 is an infectious disease caused by the most recently discovered coronavirus 2 (SARS-CoV-2). CT (computed tomography) has been shown to have good sensitivity in comparison with RT-PCR, particularly in early stages. However, CT findings appear to not always be related to a certain clinical severity. The aim of this study is to evaluate a correlation between the percentage of lung parenchyma volume involved with COVID-19 infection (compared to the total lung volume) at baseline diagnosis and correlated to the patient’s clinical course (need for ventilator assistance and or death). All patients with suspected COVID-19 lung disease referred to our imaging department for Chest CT from 24 February to 6 April 2020were included in the study. Specific CT features were assessed including the amount of high attenuation areas (HAA) related to lung infection. HAA, defined as the percentage of lung parenchyma above a predefined threshold of −650 (HAA%, HAA/total lung volume), was automatically calculated using a dedicated segmentation software. Lung volumes and CT findings were correlated with patient’s clinical course. Logistic regressions were performed to assess the predictive value of clinical, inflammatory and CT parameters for the defined outcome. In the overall population we found an average infected lung volume of 31.4 ± 26.3% while in the subgroup of patients who needed ventilator assistance and who died as well as the patients who died without receiving ventilator assistance the volume of infected lung was significantly higher 41.4 ± 28.5 and 72.7 ± 36.2 (p < 0.001). In logistic regression analysis best predictors for ventilation and death were the presence of air bronchogram (p = 0.006), crazy paving (p = 0.007), peripheral distribution (p < 0.001), age (p = 0.002), fever at admission (p = 0.007), dyspnea (p = 0.002) and cardiovascular comorbidities (p < 0.001). In multivariable analysis, quantitative CT parameters and features added incremental predictive value beyond a model with only clinical parameters (area under the curve, 0.78 vs. 0.74, p = 0.02). Our study demonstrates that quantitative evaluation of lung volume involved by COVID-19 pneumonia helps to predict patient’s clinical course.

Correlation between Clinical and Laboratory Manifestation to The Severity and Outcome of COVID-19 Infection in Children with Cancer at RSUD Dr. Soetomo

“…Most COVID-19 patients experience similar symptoms, and the causes of death are related to low oxygen saturation and respiratory problems in the lungs. 13 Furthermore, from the analysis that has been carried out, in this study it was found that there was no relationship between the number of lymphocytes, the number of erythrocytes, the Hb level, the number of leukocytes, and the number of platelets on the degree of severity and outcome of pediatric cancer patients with COVID-19 infection. Research conducted in Wuhan, China showed no significant difference between an increase in the number of lymphocytes and the severity of pediatric patients.…”

“…12 Another study has shown a relationship between shortness of breath symptoms and the need for ventilators in patients. Annoni et al, 2021 stated in their research that COVID-19 patients with SpO2 <90% were associated with death outcomes. Patients with shortness of breath experience low oxygen saturation and need ventilation assistance, so it can be concluded that these two studies prove the association of symptoms of shortness of breath with the death of patients with COVID-19 infection.…”

Symptomatic post COVID patients have impaired alveolar capillary membrane function and high VE/VCO2

“…This quantitative approach enables an automated assessment of the pulmonary infection, depicting infection areas as high attenuation areas (HAAs) in respect of a defined threshold value ranging from 650 Hounsfield unit (HU) to 3071 HU. The amount of infected lung defined as the percentage of lung parenchyma above the predefined vendor-specific threshold of 650 HU (HAA%, HAA/total lung volume) was automatically calculated by the dedicated software for both lungs [ 26 , 27 ].…”

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree

“…High attenuation area (HAA) was defined as the quantified percentage of imaged lung volume with attenuation values between −600 and −250 Hounsfield units (HU), and low attenuation area (LAA) as <−950 HU ( 19 , 20 ). HAA corresponds to pneumonia-related alterations such as GGOs and consolidation ( 18 ). Airway measurements were performed using the full-width-half-maximum (FWHM) method.…”

“…These advances may be channeled into developing objective imaging biomarkers for predicting COVID-19 outcomes. Using appropriate density threshold ranges, it is possible to differentiate lung parenchyma involved in COVID-19 from normal lung parenchyma ( 18 ).…”