Which Body Composition Parameters on Computed Tomography Are More Successful in Predicting the Prognosis of COVID-19 Patients?

OBJECTIVES: The aims of the study are to compare the body composition parameters (BCPs), which have been reported to have a prognostic impact, in COVID-19 patients, and to determine the most influential BCP(s) on the prognosis. METHODS: Unenhanced chest computed tomography examinations of COVID-19 cases were assessed regarding the severity of pneumonia, pectoralis muscle area and density (PMA, PMD), visceral adipose tissue area (VAA), waist circumference, waist to paravertebral muscle circumference ratio, coronary artery calcification severity, and paravertebral muscle area at the T5 vertebral level. A second observer repeated measurements and an intraclass correlation coefficient score were used for interobserver agreement. The relationship between data and patient outcomes (intubation, death) was investigated using multivariable logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of BCPs in predicting outcomes. RESULTS: A total of 238 (121 males, median age, 48 years [interquartile range, 36-63 years]) consecutive COVID-19 patients with chest computed tomography were investigated. Twenty-four patients (10.08%) were intubated, and 15 patients (6.3%) died during at least 1 month of follow-up. Waist to paravertebral muscle circumference ratio, PMA, PMD, and T5 vertebral level were significantly associated with intubation in the multivariable analysis. Pectoralis muscle density and PMA were significantly associated with death. Pectoralis muscle density showed the highest AUC for the prediction of intubation and death (AUC of 0.814 and 0.871, respectively). There was a good to excellent agreement between observers (intraclass correlation coefficient range, 0.899-0.998). CONCLUSIONS: Pectoralis muscle density is the most influential BCP in predicting intubation and death in COVID-19 patients.

COVID-19 pneumonia: lessons learned, challenges, and preparing for the future.

Coronavirus disease 2019 (COVID-19) is a viral disease that causes life-threatening health problems during acute illness, causing a pandemic and millions of deaths. Although computed tomography (CT) was used as a diagnostic tool for COVID-19 in the early period of the pan demic due to the inaccessibility or long duration of the polymerase chain reaction tests, cur rent studies have revealed that CT scan should not be used to diagnose COVID-19. However, radiologic findings are vital in assessing pneumonia severity and investigating complications in patients with COVID-19. Long-term symptoms, also known as long COVID, in people recovering from COVID-19 affect patients' quality of life and cause global health problems. Herein, we aimed to summarize the lessons learned in COVID-19 pneumonia, the challenges in diagnosing the disease and complications, and the prospects for future studies.

Management of Patients with Connective Tissue Disease-associated Interstitial Lung Diseases During the COVID-19 Pandemic.

The novel coronavirus disease (COVID-19) is similar to connective tissue disease-associated interstitial lung diseases (CTD-ILD) in many aspects. However, patients with CTD-ILD have required particular attention during the pandemic since they are at high risk due to -immunosuppressive treatments. Thus, prompt decisions for diagnosis and treatment initiation have become more important than earlier for these patients during the pandemic. Radiological perspectives have become inevitable for the differential diagnosis of this group ¬during the pandemic, particularly to obtain rapid and accurate results that allow the physicians to start treatment immediately. However, in addition to radiological images, a definitive diagnosis also requires access to extensive information regarding patient history, including characteristics of comorbidities, and clinical and serological findings. Consequently, the differential diagnosis of COVID-19 and CTD-ILD can yield accurate treatment regimens that differ greatly between the 2 diseases, and also prevent the spread of the outbreak with COVID-19 patients treated under isolation.

An unusual cause of spontaneous pneumothorax: Post-COVID-19 pulmonary fibrosis.

Spontaneous pneumothorax (SP) is characterized by the escape of broncho-alveolar air into presence of air in the pleural space without preceding blunt or penetrating trauma. SP requires prompt diagnosis and treatment. SP is divided into two groups as primary and secondary. Primary SP is usually seen in tall and thin patients with no clinically evident underlying lung disease (especially in tall and thin subjects), whereas secondary SP cases have an underlying lung disease, such as cystic lung disease, cavitary lung lesions, severe asthma, emphysema or pneumonia. Patients with Coronavirus-2019 (COVID-19) may experience the SP during the diagnosis and treatment processes, and it is a significant cause of morbidity. However, late-onset SP after recovering from COVID-19 is unusual. Herein we present a case with post-COVID-19 pulmonary fibrosis-like changes and subsequent late onset spontaneous pneumothorax (SP). We also present the patient's radiological findings.

Visual and Quantitative Assessment of COVID-19 Pneumonia on Chest CT: The Relationship with Disease Severity and Clinical Findings.

BACKGROUND: Lungs are the primary organ involved in COVID-19, and the severity of pneumonia in COVID-19 patients is an important cause of morbidity and mortality. AIM: We aimed to evaluate the pneumonia severity through the visual and quantitative assessment on chest computed tomography (CT) in patients with coronavirus disease 2019 (COVID-19) and compare the CT findings with clinical and laboratory findings. METHODS: We retrospectively evaluated adult COVID-19 patients who underwent chest CT along with theirclinical scores, laboratory findings, and length of hospital stay. Two independent radiologists visually evaluated the pneumonia severity on chest CT (VSQS). Quantitative CT (QCT) assessment was performed using a free DICOM viewer, and the percentage of the well-aerated lung (%WAL), high-attenuation areas (%HAA) at different threshold values, and mean lung attenuation (MLA) values were calculated. The relationship between CT scores and the clinical, laboratory data, and the length of hospital stay were evaluated in this cross-sectional study. The student's t-test and chi-square test were used to analyze the differences between the variables. The Pearson correlation test analyzed the correlation between the variables. The diagnostic performance of the variables was assessed using the receiver operating characteristic (ROC) analysis. RESULTS: The VSQS and QCT scores were significantly correlated with procalcitonin, d-dimer, ferritin, and C-reactive protein levels. Both VSQ and QCT scores were significantly correlated with the disease severity (p < 0.001). Among the QCT parameters, the %HAA-600 value showed the best correlation with the VSQS (r = 730, p < 0.001). VSQS and QCT scores had high sensitivity and specificity in distinguishing disease severity and predicting prolonged hospitalization. CONCLUSION: The VSQS and QCT scores can help manage the COVID-19 and predict the duration of the hospitalization.

COVID-19-associated pancytopenia and typhlitis.

Neutropenic enterocolitis is also known as typhlitis, is characterized by severe inflammation in the bowel loops. It is often seen in immunosuppressed patients, and it has high morbidity and mortality. Although the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily affects the respiratory system and causes COVID-19 (Coronavirus Disease 2019), it may affect hematopoietic and gastrointestinal systems. Herein, we present a rare case of COVID-19-associated pancytopenia and typhlitis in a 60-year-old female who presented with abdominal pain. Contrast-enhanced abdominal computed tomography (CT) demonstrated the bowel wall thickening in the cecum and ascending colon compatible with enterocolitis. Moreover, the chest CT showed bilateral, peripheral, and multifocal ground-glass opacities, consistent with COVID-19 pneumonia. We also aimed to emphasize the laboratory, clinical, and CT findings of the patient.

Three-dimensional CT of COVID-19 Pneumonia.

Online supplemental material is available for this article.

Evaluation of disease severity with quantitative chest CT in COVID-19 patients.

PURPOSE: We aimed to assess the severity of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) using quantitative (QCT) and semiquantitative (SCT) assessments and compare with the clinical findings. METHODS: Two observers independently examined the CT images of COVID-19 patients, and the SCT severity score was calculated. The SCT score was calculated as the sum of values ranging from 0 to 4, according to the volumetric rate of involvement for each lung lobe. In quantitative assessment, total lung volume (TLV) was automatically calculated from CT density values between -200 and -950 HU. Besides, healthy lung volume (HLV) was calculated from voxels between -800 and -950 HU. The QCT score was calculated with the following formula: (TLV - HLV / TLV) ×100. All patients were clinically divided into four groups: mild, common, severe, and critical. Interobserver agreement for SCT assessment was investigated using the Cohen's Kappa statistics (κ). Pearson's correlation coefficient was used for the relationship between continuous data. The diagnostic accuracy of SCT and QCT in the differentiation of clinically limited (mild, common) and extensive (severe, critical) disease was investigated using ROC analysis. RESULTS: Seventy-six patients with a diagnosis of COVID-19 were included. There was good agreement between the two observers in the SCT evaluation of pulmonary disease severity (κ = 0.796; 95% CI, 0.751-0.841). A significant correlation was found between QCT and SCT scores (p < 0.001, r = 0.661). Both QCT and SCT scores showed a significant correlation with clinical severity score (p < 0.001, r = 0.620 and p = 0.004, r = 0.529, respectively). The ROC analysis revealed the AUC of QCT and SCT for differentiation of limited and extensive disease as 0.873 (95% CI, 0.774-0.972) and 0.816 (95% CI, 0.673-0.959), respectively. CONCLUSION: The QCT assessment is an objective method in the evaluation of COVID-19 severity and is more successful than semiquantitative CT assessment to discriminate extensive from limited disease.

The prognostic value of pneumonia severity score and pectoralis muscle Area on chest CT in adult COVID-19 patients.

PURPOSE: To assess the prognostic value of pneumonia severity score (PSS), pectoralis muscle area (PMA), and index (PMI) on chest computed tomography (CT) in adult coronavirus disease 2019 (COVID-19) patients. METHOD: The chest CT images of COVID-19 patients were evaluated for the PSS as the ratio of the volume of involved lung parenchyma to the total lung volume. The cross-sectional areas of the pectoralis muscles (PMA, cm2) were also measured automatically on axial CT images, and PMI was calculated as the following formula: PMI = PMA / patient's height square (m2). The relationship between clinical variables, PSS, PMA, sex-specific PMI values, and patient outcomes (intubation, prolonged hospital stay, and death) were investigated using multivariable logistic regression analysis. All patients were followed for more than a month. RESULTS: One-hundred thirty patients (76 males, 58.46 %) were included in the study. Fifteen patients (11.54 %) were intubated, 24 patients (18.46 %) had prolonged hospital stay, and eight patients (6.15 %) died during follow-up. Patients with comorbidity had a higher mean of PSS (6.3 + 4.5 vs 3.9 + 3.8; p = 0.001). After adjusting the confounders, PSS was an independent predictor of intubation (adjusted Odds Ratio [OR]: 1.73, 95 % CI 1.31-2.28, p < 0.001), prolonged hospital stay (OR: 1.20, 95 % CI 1.09-1.33, p < 0.001), and death (OR: 2.13, 95 % CI 1.1-4.13, p = 0.026. PMI value was a predictor of prolonged hospital stay (OR: 0.83, 95 % CI 0.72-0.96, p = 0.038) and death (OR: 0.53, 95 % CI 0.29-0.96, p = 0.036). Incrementally increasing PMA value was a predictor of prolonged hospital stay (OR: 0.93, 95 % CI 0.89-0.98, p = 0.01) and intubation (OR: 0.98, 95 % CI 0.96-1, p = 0.036). CONCLUSION: PSS, PMA, and PMI values have prognostic value in adult COVID-19 patients and can be easily assessed on chest CT images.

Chest CT features of the novel coronavirus disease (COVID-19)

A new type of coronavirus (2019-nCoV) is rapidly spreading worldwide and causes pneumonia, respiratory distress, thromboembolic events, and death. Chest computed tomography (CT) plays an essential role in the diagnosis of viral pneumonia, monitoring disease progression, determination of disease severity, and evaluating therapeutic efficacy. Chest CT can show important clues of 2019-nCoV disease (also known as COVID-19) in patients with an appropriate clinic. Prompt diagnosis of COVID-19 is essential to prevent disease transmission and provides close clinical observation of patients with clinically severe disease. Therefore, radiologists and clinicians should be familiar with the CT imaging findings of COVID-19 pneumonia. Herein, we aimed to review the imaging findings of COVID-19 pneumonia and examine the critical points to be considered for imaging in cases with COVID-19 suspicion.