The Impact of Tocilizumab on Radiological Changes Assessed by Quantitative Chest CT in Severe COVID-19 Patients

Anghel, Ana-Maria-Jennifer; Niculae, Cristian-Mihail; Manea, Eliza-Daniela; Lazăr, Mihai; Popescu, Mara; Damalan, Anca-Cristina; Bel, A.; Nedelcu, Iulia-Maria; Patrascu, R.; Hristea, Adriana

doi:10.3390/jcm11051247

Cited by 10 publications

(12 citation statements)

References 24 publications

(48 reference statements)

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“…CT characteristics of interstitial pulmonary fibrosis include patchy, predominantly peripheral, subpleural, and symmetrical basal honeycombing, reticular abnormalities, and “ground glass” opacities [ 35 , 36 ]. Honeycombing, distorted lung architecture, and traction bronchiectasis represent reliable radiological indicators to start empiric antifibrotic drugs, with the highest concurrence of the expert groups for honeycombing [ 19 ]. In the present study, we recommend an additional marker, “percent of interstitial pulmonary lesions,” to identify the patients who will develop fibrosis; for 26.94% of interstitial lung involvement at hospital admission, we anticipate the occurrence of pulmonary fibrosis with Se = 0.83 and Sp = 0.73.…”

Section: Discussionmentioning

confidence: 99%

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Interstitial Lung Fibrosis Following COVID-19 Pneumonia

et al. 2022

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Background and Objectives: Pulmonary fibrosis represents a stage of normal physiologic response to inflammatory aggression, mostly self-limiting and reversible; however, numerous patients treated for SARS-CoV-2 pneumonia present after release from hospital residual lung fibrosis. In this article, we aim to present an optimization method for evaluating pulmonary fibrosis by quantitative analysis, to identify the risk factors/predictors for pulmonary fibrosis in patients with SARS-CoV-2 infection, and to characterize the impact of pulmonary fibrosis on the symptomatology of patients after release from the hospital. Materials and Methods: We performed a prospective observational study on 100 patients with severe forms of pneumonia, with a control group of 61 non-COVID normal patients. Results: We found persistent interstitial changes consistent with fibrotic changes in 69% of patients. The risk of fibrosis was proportional to the values of erythrocyte sedimentation rate (ESR), C reactive protein (CRP), and lactate dehydrogenase (LDH), and to the duration of hospitalization. The imaging parameters correlated with increased risk for interstitial fibrosis were the number of affected pulmonary lobes and the percent of interstitial pulmonary fibrosis. Conclusions: The main risk factors for pulmonary fibrosis post-COVID-19 identified in our study are increased ESR, CRP, LDH, duration of hospitalization and the severity of pneumonia.

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Section: Discussionmentioning

confidence: 99%

“…For evaluation of CT images, we used following density range scale: higher than 0UH—alveolar lesions (consolidations), between 0 and −200HU—mixed lesions (alveolar and interstitial), −201 to −800 HU—interstitial lesions, −801 to −1000 HU—normal lung parenchyma [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

Interstitial Lung Fibrosis Following COVID-19 Pneumonia

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“…The administration of steroids is considered beneficial in the management of COVID-19 [ 38 ], because it reduces mortality by a third in patients with mechanical ventilation and by a fifth in those who require oxygen therapy [ 39 ]. Violi et al showed that steroid treatment could reduce myocardial injury during the acute phase of community-acquired pneumonia and, eventually, cardiovascular events and mortality during a short- and long-term follow-up for patients [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Pulmonary Involvement in SARS-CoV-2 Infection Estimates Myocardial Injury Risk

et al. 2022

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Background and Objectives: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection represents a pathology with primary pulmonary involvement and multisystemic impact, including cardiovascular injuries. The present study aimed to assess the value of clinical, biochemical, and imaging factors in COVID-19 patients in determining the severity of myocardial involvement, and to create a model that can be used toevaluate myocardial injury risk based on clinical, biochemical and imaging factors. Materials and Methods: We performed an observational cohort study on 150 consecutive patients, evaluating their age, sex, hospitalization period, peripheral oxygen saturation (SpO2) in ambient air, systolic and diastolic blood pressure, heart rate, respiratory rate, biochemical markers of cardiac dysfunction (TnI, and NT-proBNP), inflammatory markers (C reactive protein (CRP), fibrinogen, serum ferritin, interleukin-6 (IL-6), tumor necrosis factor alpha (TNFα)), D-dimers, lactate dehydrogenase (LDH), myoglobin and radio-imaging parameters. All patients underwent computerized tomography chest scan in the first two days following admission. Results: We observed elevated heart and respiratory rates, higher systolic blood pressure, and a lower diastolic blood pressure in the patients with cardiac injury; significant differences between groups were registered in TnI, NT-proBNP, LDH, CRP, and D-dimers. For the radiological parameters, we found proportional correlations with the myocardial injury for the severity of lung disease, number of pulmonary segments with alveolar consolidation, number of pulmonary lobes with pneumonia, crazy paving pattern, type of lung involvement, the extent of fibroatelectatic lesions and the mediastinal adenopathies. Conclusions: Myocardial injury occurred in 12% of patients in the study group. Ground glass opacities, interstitial interlobular septal thickening (crazy paving pattern), fibroatelectasic lesions and alveolar consolidations on CT scan were correlated with myocardial injury. Routine lung sectional imaging along with non-specific biomarkers (LDH, D-dimers, and CRP) can be further valuable in the characterization of the disease burden, thus impacting patient care.

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“…After the loading of the CT images into SyngoPulmo 3D, segmentation of lung parenchyma is performed automatically (with possibility of manual adjustment) ( Figure S1 ), excluding the main bronchi and main pulmonary vessels from the pulmonary densitometric analysis ( Figure S2 ); the software performs further the calculation of the lung volume for specific density ranges defined by the user ( Figure S3 ). We calculated the percent of lung involvement based on density ranges, considering alveolar lesions for densities higher than 0 Hounsfield units (HU), mixt lesions (alveolar and interstitial) for densities between 0 and −200 HU, interstitial lesions for densities between −200 and −800 HU, normal parenchyma for densities between −800 and −1000 HU, and emphysema or hyperinflation for densities lower than −1000 HU [ 7 ]. We further analyzed the normal lung densities using a cluster analysis method that calculates and displays volumes of connected voxels in a specified density range.…”

Section: Methodsmentioning

confidence: 99%

Mortality Predictors in Severe SARS-CoV-2 Infection

et al. 2022

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Background and Objectives: The severe forms of SARS-CoV-2 pneumonia are associated with acute hypoxic respiratory failure and high mortality rates, raising significant challenges for the medical community. The objective of this paper is to present the importance of early quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters. The current evolution models used in the evaluation of severe cases of COVID-19 only include qualitative or semi-quantitative evaluations of pulmonary lesions which lead to a less accurate prognosis and assessment of pulmonary involvement. Materials and Methods: We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID-19. The patients were divided into two groups: group A (76 survivors) and group B (24 non-survivors). All patients were evaluated by CT scan upon admission in to the hospital. Results: We found a low percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin associated with higher mortality; a high percentage of interstitial lesions, oxygen flow, FiO2, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine were also associated with higher mortality. The most accurate regression model included the predictors of age, lymphocytes, PaO2/FiO2 ratio, percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Based on these parameters we developed a new score (COV-Score). Conclusions: Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters. The cluster evaluation algorithm increases the non-survivor and overall prediction accuracy.COV-Score represents a viable alternative to current prediction scores, demonstrating improved sensitivity and specificity in predicting mortality at the time of admission.

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The Impact of Tocilizumab on Radiological Changes Assessed by Quantitative Chest CT in Severe COVID-19 Patients

Cited by 10 publications

References 24 publications

Interstitial Lung Fibrosis Following COVID-19 Pneumonia

Interstitial Lung Fibrosis Following COVID-19 Pneumonia

Pulmonary Involvement in SARS-CoV-2 Infection Estimates Myocardial Injury Risk

Mortality Predictors in Severe SARS-CoV-2 Infection

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