Frequency of hepatic steatosis and its association with the pneumonia severity score on chest computed tomography in adult COVID-19 patients

Can Opportunistic Use of Computed Tomography Help Reveal the Association Between Hepatic Steatosis and Disease Severity in Hospitalized COVID-19 Patients?

To measure hepatic steatosis (HS) in hospitalized COVID-19 patients using unenhanced chest computed tomography (CT) imaging and to evaluate the relationship between disease severity and prognosis in adult patients.This retrospective study included 152 consecutive hospitalized COVID-19 patients with a positive reverse transcriptase polymerase chain reaction (RT-PCR) test. The COVID-19 Reporting and Data System (CO-RADS) and the chest CT score were evaluated. HS measurements were performed based on CT images using a single region of interest placed on the right liver lobe (segments V-VII). HS was defined as a liver attenuation value <40 Hounsfield units. Data were collected and compared with the patients' prognostic parameters.Of the 152 inpatients, 137 patients (90.1%) had a CT score ≥3 and 109 patients (71.7%) had a CO-RADS score ≥4, 43 (28.2%) had HS. All patients with HS (100%) and 94/109 (86.2%) patients without HS had a CT score ≥3. There was a statistically significant difference between the two groups in terms of chest CT score (p=0.006). There was no statistically significant difference between the two groups in terms of CO-RADS score (p=0.291). The median CRP levels were significantly increased in patients with HS compared to patients without HS (p=0.023). There was no significant difference in ICU hospitalization and mortality due to the presence of HS (p>0.05).The current study revealed significantly higher chest CT scores in COVID-19 patients with HS measured on CT compared to those without HS. Opportunistic use of CT images for the detection of HS can be considered as an adjunctive tool in the risk analysis of COVID-19 patients hospitalized due to COVID-19 pneumonia.The severity of COVID-19 disease is increased in hospitalized patients with hepatosteatosis compared to patients with a normal liver. Density measurements for the evaluation of HS using opportunistic CT applications can be considered as an adjunctive tool in the prognostic evaluation of hospitalized patients with COVID-19 pneumonia. · Parlak AE, Erdem Toslak İ, Turkoglu Selcuk N. Can Opportunistic Use of Computed Tomography Help Reveal the Association Between Hepatic Steatosis and Disease Severity in Hospitalized COVID-19 Patients?. Fortschr Röntgenstr 2024; DOI 10.1055/a-2369-8377.

Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT

Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remained unexplored. The accuracy of previous methodologies has been limited by the inclusion of non-parenchymal liver regions. To overcome this limitation, we present a novel deep-learning (DL) based method tailored for the automatic selection of parenchymal portions in CT images. This innovative method automatically delineates circular regions for effectively detecting hepatic steatosis. We use 1,014 multinational CT images to develop a DL model for segmenting liver and selecting the parenchymal regions. The results demonstrate outstanding performance in both tasks. By excluding non-parenchymal portions, our DL-based method surpasses previous limitations, achieving radiologist-level accuracy in liver attenuation measurements and hepatic steatosis detection. To ensure the reproducibility, we have openly shared 1014 annotated CT images and the DL system codes. Our novel research contributes to the refinement the automated detection methodologies of hepatic steatosis on CT images, enhancing the accuracy and efficiency of healthcare screening processes.

Is Steatotic Liver Disease Related to Poor Outcome in COVID-19-Hospitalized Patients?

Background: Steatotic liver disease (SLD) has been linked to more exacerbated inflammatory responses in various scenarios. The relationship between SLD and COVID-19 prognosis remains unclear. Our aim was to investigate the impact of SLD on the outcome of COVID-19. Methods: Patients hospitalized with confirmed COVID-19 and who underwent laboratory tests and chest CT scans were included. SLD was assessed by measuring the attenuation coefficient on CT scans. The relationship between SLD, the severity of COVID-19 clinical presentation and in-hospital mortality were assessed. Results: A total of 610 patients were included (mean age 62 ± 16 years, 64% male). The prevalence of SLD was 30%, and the overall in-hospital mortality rate was 19%. Patients with SLD were younger (58 ± 13 vs. 64 ± 16 years, p < 0.001) and had a higher BMI (32 ± 5 vs. 28 ± 4 kg/m2, p = 0.014). Admission AST values were higher in patients with SLD (82 ± 339 vs. 50 ± 37, p = 0.02), while D-dimer (1112 ± 2147 vs. 1959 ± 8509, p = 0.07), C-reactive protein (12 ± 9 vs. 11 ± 8, p = 0.27), ALT (67 ± 163 vs. 47 ± 90, p = 0.11), ALP (83 ± 52 vs. 102 ± 125, p = 0.27), and GGT (123 ± 125 vs. 104 ± 146, p = 0.61) did not significantly differ compared to patients without SLD. No difference was observed regarding lung parenchyma involvement >50% (20% vs. 17%, p = 0.25), hospital length of stay (14 ± 19 vs. 16 ± 23 days, p = 0.20), hemodialysis support (14% vs. 16%, p = 0.57), use of mechanical ventilation (20% vs. 20%, p = 0.96), and in-hospital mortality (17% vs. 20%, p = 0.40) when comparing patients with and without SLD. Conclusions: SLD showed no significant association with morbidity and mortality in patients with COVID-19.

A Novel Combined Model to Predict the Prognosis of COVID-19: Radiologicalmetabolic Scoring

AIM

To investigate the performance of a novel radiological-metabolic scoring (RM-S) system to predict mortality and intensive care unit (ICU) requirements among COVID-19 patients and to compare performance with the chest computed-tomography severity-scoring (C-CT-SS). The RMS was created from scoring systems such as visual coronary-artery-calcification scoring (V-CAC-S), hepatic-steatosis scoring (HS-S) and pancreatic-steatosis scoring (PS-S).

METHODS

Between May 2021 and January 2022, 397 patients with COVID-19 were included in this retrospective cohort study. All demographic, clinical and laboratory data and chest CT images of patients were retrospectively reviewed. RM-S, V-CAC-S, HS-S, PS-S and C-CT-SS scores were calculated, and their performance in predicting mortality and ICU requirement were evaluated by univariate and multivariable analyses.

RESULTS

A total of 32 (8.1%) patients died, and 77 (19.4%) patients required ICU admission. Mortality and ICU admission were both associated with older age (p < 0.001). Sex distribution was similar in the deceased vs. survivor and ICU vs. non-ICU comparisons (p = 0.974 and p = 0.626, respectively). Multiple logistic regression revealed that mortality was independently associated with having a C-CT-SS score of ≥ 14 (p < 0.001) and severe RM-S category (p = 0.010), while ICU requirement was independently associated with having a C-CT-SS score of ≥ 14 (p < 0.001) and severe V-CAC-S category (p = 0.010).

CONCLUSION

RM-S, C-CT-SS, and V-CAC-S are useful tools that can be used to predict patients with poor prognoses for COVID-19. Long-term prospective follow-up of patients with high RM-S scores can be useful for predicting long COVID.

Histology and cytokine levels in hepatic injury accompanying a case of non-severe COVID-19

The pathogenesis of liver dysfunction that complicates coronavirus disease 2019 (COVID-19) remains unclear, especially in mild to moderate severity cases. In this case, a novel coronavirus infection was detected by polymerase chain reaction (PCR) in a 76-year-old woman hospitalized after presenting with fever. No other abnormal physical findings were observed, and oxygen administration was not required. Chest computed tomography (CT) showed a ground-glass-like and an infiltrative shadow in the right lung, and moderate COVID-19 was diagnosed. Initially, the fever resolved, and PCR turned negative; however, the fever reappeared on hospitalization day 14, and CT showed pneumonia exacerbation accompanied by new onset of fatty liver. Biochemical testing revealed marked liver dysfunction, accompanied by elevated serum interleukin (IL)-6, IL-10, and tumor necrosis factor-α levels. Physical findings and all laboratory parameters improved after conservative treatment, and she was discharged on day 22. A liver biopsy performed 44 days post-discharge showed T-cell-dominant inflammatory cell infiltration, mainly in the portal region. Some hepatocytes showed fatty degeneration.We report a case of moderate COVID-19 in which histological hepatitis persisted after a substantial period had passed since the initial infection had cleared and associated transaminase elevations had resolved, with a comparison of serum cytokine dynamics.

Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study

BACKGROUND

Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death.

METHODS

DeHFt comprises two steps. First, a deep-learning-based segmentation model (3D residual-UNet) is trained (N.ß=.ß80) to segment the liver and spleen. Second, CT attenuation is estimated using slice-based and volumetric-based methods. DeHFt-based mean liver and liver-to-spleen attenuation are compared with an expert's ROI-based measurements. We further obtained the liver-to-spleen attenuation ratio in a large multi-site cohort of patients with COVID-19 infections (D1, N.ß=.ß805; D2, N.ß=.ß1917; D3, N.ß=.ß169) using the DeHFt pipeline and investigated the association between HS and COVID-19 infections severity.

FINDINGS

The DeHFt pipeline achieved a dice coefficient of 0.95, 95% CI [0.93...0.96] on the independent validation cohort (N.ß=.ß49). The automated slice-based and volumetric-based liver and liver-to-spleen attenuation estimations strongly correlated with expert's measurement. In the COVID-19 cohorts, severe infections had a higher proportion of patients with HS than non-severe infections (pooled OR.ß=.ß1.50, 95% CI [1.20...1.88], P.ß<.ß.001).

INTERPRETATION

The DeHFt pipeline enabled accurate segmentation of liver and spleen on non-contrast CTs and automated estimation of liver and liver-to-spleen attenuation ratio. In three cohorts of patients with COVID-19 infections (N.ß=.ß2891), HS was associated with disease severity. Pending validation, DeHFt provides an automated CT-based metabolic risk assessment.

FUNDING

For a full list of funding bodies, please see the Acknowledgements.

Investigation of comorbidities of COVID-19 patients with hepatosteatosis using latent class analysis

Introduction

Coronavirus Disease 2019 (COVID-19) disease first appeared in Wuhan, China in December 2019. Subsequently, the pandemic spread rapidly throughout the entire world. The number of people who died from COVID-19 is rising daily due to the growing number cases. This retrospective study aims to classify patients with hepatosteatosis (HS) who had COVID-19, depending on additional disease characteristics and to compare survival times and death rates.

Material and methods

The study included 433 individuals with COVID-19 and HS at Amasya University Sabuncuoglu Serefeddin Education and Research Hospital. Additional disease characteristics of patients with HS were analyzed using latent class analysis (LCA) and the patients were divided into two groups.

Results

The study results indicate that the survival time of the first group, which was formed as a result of the LCA, was significantly lower than that of the second group (p = 0.038). The rate of diabetes, coronary artery disease, chronic rhythm disorder, chronic obstructive pulmonary disease (COPD) and chronic kidney disease was significantly higher in group 1 than in group 2 (respectively p < 0.001; p < 0.001; p < 0.001; p < 0.001; p = 0.015).

Discussion

In patients with HS, the presence of diabetes, coronary artery disease, chronic rhythm problem, COPD, and chronic renal disorders contributes to an increase in death rates due to COVID-19.

The Prognostic Value of Vertebral Bone Density on Chest CT in Hospitalized COVID-19 Patients

The aim of this study is to evaluate the prognostic value of the vertebral bone mineral density (BMD) on chest computed tomography (CT) in COVID-19 patients. The chest CT of hospitalized patients with COVID-19 pneumonia were evaluated for Pneumonia Severity Score (PSS) as the ratio of the volume of involved lung parenchyma to the total lung volume. In addition, BMD was manually measured from the vertebral corpus using axial CT images. The relationships of clinical variables, PSS and vertebral BMD with patient outcomes, namely mortality, intensive care unit (ICU) admission and mechanical ventilation were investigated. Lower BMD was defined as ≤100 HU. The study included 209 patients (118 males, 56.4%). As a result of the univariate analysis, the rates of mortality, ICU admission and mechanical ventilation were 17.2% (n = 36), 24.8% (n = 52), and 20.6% (n = 43), respectively, and they were significantly higher among the patients with lower BMD (38.1 vs 13.0%, p < 0.001; 33.4 vs 21.2%, p = 0.002; and 38.1 vs 8.2%, p < 0.001, respectively). In the mortality group, PSS was significantly higher (median, 9 vs 5; p < 0.001) and vertebral BMD was significantly lower (median, 83 vs 139; p < 0.001). Severe clinical incidence was significantly higher in patients with lower BMD compared to those with higher BMD (39.7 vs 24.7% and p = 0.028). There was a significant correlation between clinical classification and lower BMD (r = 0.152 and p = 0.028). The multivariate analysis revealed vertebral BMD [odds ratio (OR), 1.028; 95% CI, 1.011-1.045, p = 0.001) and lower BMD (OR, 4.682; 95% CI, 1.784-12.287, p = 0.002) as significant independent predictors of mortality. Vertebral BMD is a strong independent predictor of mortality that is reproducible and can be easily evaluated on the chest CT images of COVID-19 patients.