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?. Rofo 2025; 197: 648-656.

The early diagnosis of opportunistic infections is a critical concern for patient care worldwide, particularly in the context of the COVID-19 pandemic. This review examines the challenges and advancements in the management and early diagnosis of opportunistic fungal infections, which have become increasingly prominent during the pandemic. Using multiple sources, including curated databases such as PubMed and Scopus, as well as Google Scholar for broader literature searches, we systematically reviewed studies on COVID-19-associated fungal infections, with a focus on candidiasis, mucormycosis, and aspergillosis. The inclusion criteria encompassed peer-reviewed articles, clinical case reports, and cohort studies that discussed diagnostic methods, clinical outcomes, and treatment responses. Data were systematically extracted and analyzed to identify key trends and gaps in current diagnostic practices. Given the significance of opportunistic fungal infections-particularly the selected species-this review provides a comprehensive analysis of diagnostic challenges and advancements in the context of COVID-19 and beyond. Currently, there is no definitive strategy for effectively addressing these opportunistic pathogens, highlighting the need for continued research and innovation. Despite advancements in medical technology, opportunistic fungal infections continue to pose significant challenges to early and accurate diagnosis. The COVID-19 pandemic has exacerbated these challenges, with secondary fungal infections contributing to increased morbidity and mortality rates. This review highlights the complexities of diagnosing fungal coinfections and emphasizes the urgent need for improved diagnostic strategies. Enhancing the early and accurate detection of these infections is critical for effective patient management, particularly during viral pandemics. Addressing the challenges outlined in this review requires innovative diagnostic approaches to improve patient outcomes and reduce the burden of opportunistic infections on global healthcare systems.

Integrating multimodal data, such as pathology images and genomics, is crucial for understanding cancer heterogeneity, personalized treatment complexity, and enhancing survival prediction. However, most current prognostic methods are limited to a single domain of histopathology or genomics, inevitably reducing their potential for accurate patient outcome prediction. Despite advancements in the concurrent analysis of pathology and genomic data, existing approaches inadequately address the intricate intermodal relationships.

This paper introduces the CPathomic method for multimodal data-based survival prediction. By leveraging whole slide pathology images to guide local pathological features, the method effectively mitigates significant intermodal differences through a cross-modal representational contrastive learning module. Furthermore, it facilitates interactive learning between different modalities through cross-modal and gated attention modules.

The extensive experiments on five public TCGA datasets demonstrate that CPathomic framework effectively bridges modality gaps, consistently outperforming alternative multimodal survival prediction methods.

The model we propose, CPathomic, unveils the potential of contrastive learning and cross-modal attention in the representation and fusion of multimodal data, enhancing the performance of patient survival prediction.

Significant progress has been made in our understanding of the acute and chronic clinical and radiological manifestations of coronavirus-19 (COVID-19). This article provides an updated review on pulmonary COVID-19, while highlighting the key imaging features that can identify and distinguish acute COVID-19 pneumonia and its chronic sequelae from other diseases.

Acute COVID-19 pneumonia typically presents with manifestations of organizing pneumonia on computed tomography (CT). In cases of severe disease, patients clinically progress to acute respiratory distress syndrome, which manifests as diffuse alveolar damage on CT. The most common chronic imaging finding is ground-glass opacities, which commonly resolves, as well as subpleural bands and reticulation. Pulmonary fibrosis is an overall rare complication of COVID-19, with characteristic features, including architectural distortion, and traction bronchiectasis.

Chest CT can be a helpful adjunct tool in both diagnosing and managing acute COVID-19 pneumonia and its chronic sequelae. It can identify high-risk cases and guide decision-making, particularly in cases of severe or complicated disease. Follow-up imaging can detect persistent lung abnormalities associated with long COVID and guide appropriate management.

The use of lung ultrasound (LUS) has recently become vital in the diagnosis and prognosis of various respiratory diseases. Its role in COVID-19 requires further investigation.

Twenty-five consecutive, non-ICU hospitalized COVID-19 patients were included. LUS was performed on admission and sequentially every 3 days at 8 points in the chest. Based on the LUS findings a score was designed. Logarithmic regression models and ROC curve analysis were applied.

A statistically significant positive correlation was found between LUS score at admission and the severity of SARS-COV-2 infection. Higher LUS score was significantly associated with lower PaO2/FiO2 ratio, use of HFNC, longer hospitalization and greater extent of chest CT infiltrates. A significant association between LUS score and risk of death or intubation or HFNC was found. For one point of increase in the score, risk of death or intubation or HFNC increased 1.93-fold (95% CI 1.02 to 3.65). The predictive role of the score was very satisfactory (area under the ROC curve = 0.87).

Lung ultrasound findings were significantly positively associated with clinical and radiological markers of severity of SARS-CoV-2 pneumonia. It therefore constitutes a promising and reliable technique for assessing pneumonia, comparable to chest CT.

The long-term symptomatology of COVID-19 has yet to be comprehensively described. The aim of the study was to describe persistent COVID-19 symptoms in a cohort of hospitalized and home-isolated patients.

A retrospective cohort study was conducted on long COVID patients. Long COVID symptoms were identified, and patients were divided into hospitalized (in-patients) and home-isolated (out-patients), as well as according to the number of symptoms. Patients were examined by a multidisciplinary medical team. Blood tests, high resolution chest computed tomography (CT), and physical and infectious examinations were performed. Finally, in-patients were evaluated at 2 time-points: on hospital admission (T0) and 3 months after discharge (Tpost).

There were 364 COVID-19 patients enrolled; 82% of patients reported one or more symptoms. The most reported symptom was fatigue. Chest CT showed alteration in 76% of patients, and pulmonary function alterations were observed in 44.7% of patients. A higher risk of presenting at least one symptom was seen in patients treated with corticosteroid, and a higher risk of presenting chest CT residual lesion was observed in hospitalized patients and in patients that received hydroxychloroquine treatment. Moreover, a higher risk of altered pulmonary function was observed in older patients.

Long-term sequelae are present in a remarkable number of long COVID patients and pose a new challenge to the health care system to identify long-lasting effects and improve patients' well-being. Multidisciplinary teams are crucial to develop preventive measures, and clinical management strategies.

COVID-19 patients may have residual pulmonary alterations after the acute disease, with fibrotic-like alterations. Since metalloproteinases (MMP) and their regulators may be involved in inflammation and abnormal repair processing, we aimed to investigate the correlations between MMP-9, a tissue inhibitor of metalloproteinases (TIMP-1) and chest CT abnormalities in acute phase and mid-term follow-up.

COVID-19 patients with plasma analyses and CT scans performed at acute onset and 3 months after discharge (T post) were evaluated. MMP-9, TIMP-1, and MMP-9/TIMP-1 ratio were analyzed. CT extents of COVID-19 pneumonia and fibrotic-like alterations were visually scored (score range 0-25). Spearman rank correlation analysis (p-value <.05) was computed between acute and mid-term plasma analyses and CT scores.

39 patients were enrolled. At hospital admission, MMP-9, TIMP-1, and MMP-9/TIMP-1 had a median of 240.5 ng/mL, 258.8 ng/mL, and 0.9. The median CT global and fibrotic-like scores were 9 and 6. At T post, MMP-9 and TIMP-1 were not statistically different (p-value <.05). There was a reduction of CT global score (p-value = .00007). A significant correlation was found between MMP-9 and CT global score at hospital admission (ρ = 0.456, p-value = .003) and between MMP-9/TIMP-1 ratio and CT global score at hospital admission (ρ = 0.406, p-value = .009). No other significant correlations were found between plasma enzymes and CT alterations, both in acute and mid-term follow-up.

MMP-9 plasma levels and MMP-9/TIMP-1 ratio correlate with lung involvement during the acute phase. None of the levels of MMP-9, TIMP-1, and MMP-9/TIMP-1 ratio may be adopted as predictors of residual pulmonary alterations in mid-term follow-up.

To evaluate the incidence of venous thromboembolism (VTE) in hospitalized patients with COVID-19 who underwent diagnostic tests for suspected VTE, and to correlate the IMPROVE-DD score with the incidence of VTE in this cohort.

This retrospective study included consecutive patients with COVID-19 and suspected VTE, admitted between March 2020 and September 2021 at a private hospital in Salvador (BA), Brazil, who underwent lower or upper limb venous Doppler ultrasound or chest angiotomography. Descriptive analyses and comparisons using the chi-square test were performed to identify factors potentially associated with the risk of VTE.

A total of 517 patients were included, with an in-hospital VTE incidence of 18.6% (96 events). Risk factors significantly associated with VTE included obesity, ICU admission, central venous catheter use, longer hospital stays, greater lung tomographic involvement/severity, the need for mechanical ventilation, D-dimer levels at least twice the upper limit of normal (2xULN), and the IMPROVE-DD score. The mean IMPROVE-DD score among patients with VTE was 4.7 (±3) versus 3.3 (±2.4) in those without VTE (p < 0.0001). D-dimer 2xULN was sensitive in identifying 94% of the 96 patients with VTE (p < 0.0001). The in-hospital mortality rate was 14.1%, with higher rates observed in patients with VTE (24%) compared to those without VTE (11.9%) (p = 0.003).

The incidence of VTE in hospitalized COVID-19 patients was high and correlated with increased mortality. The IMPROVE-DD score effectively identified patients at risk for in-hospital VTE, suggesting it could help to identify a high-risk subgroup that may benefit from extended thromboprophylaxis.

Sepsis is a leading cause of mortality in critically ill patients, arising from a dysregulated immune response to infection. While traditionally associated with bacterial pathogens, severe COVID-19 can induce a sepsis-like syndrome, characterized by systemic inflammation, endothelial dysfunction, and coagulation abnormalities. This study aimed to assess the prognostic value of age, inflammatory markers, coagulation dysfunction, comorbidity burden, and lung involvement on computer tomography (CT) scans in predicting poor outcomes. We conducted a prospective cohort study including 163 patients diagnosed with COVID-19-related sepsis. Univariate and multivariable logistic regression analyses were performed to identify the independent predictors of unfavorable outcomes. Higher D-dimer (OR: 1.417, p = 0.020) and C-reactive protein (CRP) levels (OR: 1.010, p = 0.027) were independently associated with poor outcomes. A greater than 50% lung involvement on CT (OR: 1.774, p = 0.025) was also a significant predictor. The Charleson Comorbidity Index (CCI) showed a strong trend toward significance (p = 0.065), while age lost statistical significance after adjusting for comorbidities. Our findings suggest that D-dimers, CRP, and lung involvement on CT are key independent predictors of poor outcomes in COVID-19-related sepsis. These results emphasize the importance of inflammatory and coagulation markers, alongside comorbidity burden, in early risk assessment. Further prospective studies are warranted to refine predictive models for severe COVID-19 cases complicated by sepsis.

BACKGROUND Atrial fibrillation (AF) is a common arrhythmia in the general population and the most frequently presented arrhythmia in the intensive care unit. We investigated the effects of AF on the outcomes of critical COVID-19 patients, especially focusing on differences between chronic (CAF) and new-onset AF (NOAF) during critical disease. MATERIAL AND METHODS In this case-control study, we investigated the association of CAF and NOAF as an exposure, with in-hospital mortality as an outcome. We identified 2 patient groups, NOAF and CAF, which were compared with controls (all other hospitalized patients with critical COVID-19 pneumonia). No specific selection or matching was performed. The chi-square test was used for categorical variables; t test and Mann-Whitney U tests were used for continuous variables, depending on distribution. P<0.05 was considered significant. RESULTS In-hospital mortality was significantly higher in the NOAF group, while in the CAF group, it was similar to that of the control group. The NOAF group had significantly higher markers of inflammation and more severe acute respiratory distress syndrome (ARDS), measured with computed tomography. NOAF was strongly associated with in-hospital death, with OR 6.392 (95% CI, 2.758-14.815), P<0.000. In comparison, the CAF group was older and had more cardiovascular comorbidities, with similar markers of inflammation and severity of ARDS as the control group. CONCLUSIONS NOAF in COVID-19 was linked with significant risk of death, being a sign of extreme cardiac, pulmonary, and metabolic instability. NOAF should be considered as an important marker of instability and predictor of poor outcomes among patients with COVID-19.

Several hematological and biochemical parameters have been related to the COVID-19 infection severity and outcomes. However, less is known about clinical indicators reflecting lung involvement of COVID-19 patients at hospital admission. Computed tomography (CT) represents an established imaging tool for the detection of lung injury, and the quantitative analysis software CALIPER has been used to assess lung involvement in COVID-19 patients. Herein, the relationship between the lung involvement expressed by CALIPER interstitial lung disease (ILD) percentage and a set of blood parameters related to tissue oxygenation and damage in COVID-19 patients at hospital admission was evaluated.

We performed a retrospective and a prospective study involving 321 and 75, respectively, COVID-19-positive patients recruited from Pisa University Hospital. The association between CALIPER ILD percentages and selected blood parameters was investigated by a regression tree approach, after multiple imputations of the dataset missing values.

High serum lactate dehydrogenase (LDH) values appeared to be predictive of high CALIPER ILD percentages at hospital admission in both retrospective and prospective datasets, even if the predictive performance of the algorithm was not optimal.

LDH levels could be evaluated as a tool for early identification of COVID-19 patients at risk of extensive lung injury, as well as in fast screening procedures before hospitalization.

This study aimed to assess chest CT abnormalities and pulmonary function at 6-month and 1-year follow-ups in coronavirus disease 2019 (COVID-19) pneumonia patients of the China epidemic in the turn of 2022-2023.

A total of 156 hospitalized patients with COVID-19 pneumonia admitted between 29 November 2022 and 10 February 2023 were prospectively assessed at 6-month and 1-year follow-ups. Characteristics and CT scores of pulmonary abnormalities and pulmonary function were compared between different follow-up time points. The correlation of CT abnormalities and pulmonary function at 1-year were evaluated.

Over 1 year, the proportion of pulmonary abnormalities gradually decreased (initial, 100%, 156/156; 6-month, 57.1%, 89/156; and 1-year, 37.8%, 59/156; P < 0.001), whereas fibrotic changes increased (initial, 6.4%, 10/156; 6-month, 14.1%, 22/156; and 1-year, 14.7%, 23/56; P < 0.001). Compared to participants of the subgroup with nonfibrotic changes, diffusion capacity of the lung for carbon monoxide (DLCO)(P = 0.01) and DLCO less than 80% predicted (P < 0.001) showed significantly decrease in participants of the subgroup with fibrotic changes. The extent of fibrotic changes was strongly correlated with lower DLCO (r = -0.734, P < 0.001).

Fibrotic changes might show a tendency to persist over time and correlate strongly with impairment of diffusion function, thus requiring more attention in future follow-ups.

The long-term effects and outcomes of human mesenchymal stem cell (MSC) therapy in patients with severe coronavirus disease 2019 (COVID-19) remain poorly understood. This study aimed to evaluate the extended safety and efficacy of MSC treatment in severe patients with COVID-19 who participated in our earlier randomized, double-blind, placebo-controlled clinical trial, with follow-up conducted over 3 years.

One hundred patients with severe COVID-19 were randomized to receive either an MSC infusion (n = 65, 4 × 107 cells/dose, on days 0, 3, and 6) or a placebo, with both groups receiving the standard of care. At 36 months post-MSC therapy, patients were followed up to long-term safety and efficacy, particularly the effects of MSC therapy on persistent COVID-19 symptoms. Evaluated outcomes included lung imaging results, 6-min walking distance (6-MWD), pulmonary function test results, quality of life scores based on the Short Form-36 (SF-36) health survey, Long COVID symptoms, new-onset comorbidities, tumor marker levels, and rates of COVID-19 reinfection.

Three years post-treatment, 46.94% (23/49) of patients in the MSC group and 34.48% (10/29) in the placebo group showed normal findings on computed tomography (CT) images (odds ratio [OR] = 1.68, 95% confidence interval [CI]: 0.65-4.34). The general health (GH) score from the SF-36 was higher in the MSC group (67.0) compared to the placebo group (50.0), with a difference of 12.86 (95% CI: 1.44-24.28). Both groups showed similar results for total lung severity scores (TSS), 6-MWD, pulmonary function tests, and Long COVID symptoms. No significant differences between groups were observed in new-onset complications (including tumorigenesis) or tumor marker levels. After adjusting for China's dynamic zero-COVID-19 strategy, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection rates were 53.06% (26/49) in the MSC group and 67.86% (19/28) in the placebo group (OR = 0.54, 95% CI: 0.20-1.41).

These findings support the long-term safety of MSC therapy in patients with severe COVID-19 over 3 years. MSC treatment may offer potential benefits for lung recovery and improved quality of life in patients experiencing Long COVID symptoms.

ClinicalTrials.gov, NCT04288102. Registered 28 February 2020, https://clinicaltrials.gov/study/NCT04288102 .

Background and Objectives: Cardiac involvement in COVID-19 has been confirmed during the acute stage of the infection. However, the prevalence and spectrum of post-infectious cardiac dysfunction remain incompletely clarified. The objective of our study was to evaluate the frequency of echocardiographic changes 2 years after hospitalization for moderate and severe COVID-19 in patients with no previously known cardiac pathology. Material and Methods: We conducted a retrospective cohort study analyzing severity markers of COVID-19 infection and echocardiographic parameters assessed ≥2 years after the acute illness, based on recent guideline recommended algorithm for echocardiographic diagnostic of left ventricular (LV) dysfunction. Results: The study included 50 Caucasian patients, 60% male, 54% aged < 65 years, and 32% with severe forms of the disease. The primary comorbidities were hypertension, obesity, and diabetes. COVID-19 severity correlated with the computed tomography (CT) lung lesion score and a neutrophil-to-lymphocyte ratio >6 but was not associated with post-COVID-19 echocardiographic changes. Left ventricular ejection fraction (LVEF) was reduced in only 18% of cases, but global longitudinal strain (GLS) impairment was observed in 46% of patients, contributing to the LV systolic subclinical dysfunction in 61%. Impaired LV diastolic disfunction with normal pressure filling was present in 30.61% of cases and with elevated pressure 10.2%. Conclusions: COVID-19 is an independent predictive factor for GLS impairment, which can indicate myocardial contractile dysfunction, even in patients with asymptomatic heart disease. This underscores the importance of regular echocardiographic monitoring for patients recovering from moderate to severe COVID-19.

Pulmonary perfusion defects have been observed in patients with coronavirus disease 2019 (COVID-19). Currently, there is a need for further data on non-contrast-enhanced MRI in COVID patients. The early identification of heterogeneity in pulmonary perfusion defects among COVID-19 patients is beneficial for their timely clinical intervention and management.

To investigate the utility of phase-resolved functional lung (PREFUL) MRI in detecting pulmonary perfusion disturbances in individuals with postacute COVID-19 syndrome (PACS).

Prospective.

Forty-four participants (19 females, mean age 64.1 years) with PACS and 44 healthy subjects (19 females, mean age 59.5 years). Moreover, among the 44 patients, there were 19 inpatients and 25 outpatients; 19 were female and 25 were male; 18 with non-dyspnea and 26 with dyspnea.

3-T, two-dimensional (2D) spoiled gradient-echo sequence.

Ventilation and perfusion-weighted maps were extracted from five coronal slices using PREFUL analysis. Subsequently, perfusion defect percentage (QDP), ventilation defect percentage (VDP), and ventilation-perfusion match healthy (VQM) were calculated based on segmented lung parenchyma ventilation and perfusion-weighted maps. Additionally, clinical features, including demographic data (such as sex and age) and serum biomarkers (such as D-dimer levels), were evaluated.

Spearman correlation coefficients to explore relationships between clinical features and QDP, VDP, and VQM. Propensity score matching analysis to reduce the confounding bias between patients with PACS and healthy controls. The Mann-Whitney U tests and Chi-squared tests to detect differences between groups. Multivariable linear regression analyses to identify factors related to QDP, VDP, and VQM. A P-value <0.05 was considered statistically significant.

QDP significantly exceeded that of healthy controls in individuals with PACS (39.8% ± 15.0% vs. 11.0% ± 4.9%) and was significantly higher in inpatients than in outpatients (46.8% ± 17.0% vs. 34.5% ± 10.8%). Moreover, males exhibited pulmonary perfusion defects significantly more frequently than females (43.9% ± 16.8% vs. 34.4% ± 10.2%), and dyspneic participants displayed significantly higher perfusion defects than non-dyspneic patients (44.8% ± 15.8% vs. 32.6% ± 10.3%). QDP showed a significant positive relationship with age (β = 0.50) and D-dimer level (β = 0.72).

PREFUL MRI may show pulmonary perfusion defects in patients with PACS. Furthermore, perfusion impairments may be more pronounced in males, inpatients, and dyspneic patients.

2 TECHNICAL EFFICACY: Stage 2.

To investigate the prognostic value of chest computed tomography (CT), platelet count (PLT), serum C-reactive protein (CRP) level, and oxygenation index (OI) in patients with severe community-acquired pneumonia (CAP).

We conducted a retrospective analysis of clinical data collected from 226 patients with CAP who received treatment in Huzhou Central Hospital from February 2022 to November 2023. Patients were divided into two groups based on pneumonia severity: Severe group (patients with severe CAP, n=113) and Typical group (patients with typical pneumonia, n=113). Differences in CT score, PLT, CRP, and OI levels between the two groups were analyzed, as well as the prognostic value of the combined CT score, PLT, CRP, and OI levels in severe CAP.

The CT Score and CRP level in the Severe group were significantly higher than those in the Typical group, whereas PLT and OI were significantly lower (P<0.05). Of 113 patients with severe pneumonia, 42 died and 71 survived. The CT Score and CRP level in the death group were significantly higher, whereas PLT and OI were lower compared to the survival group (P<0.05). The area under the ROC curve of the combined CT Score, PLT, CRP, OI for the prediction of death in patients with severe CAP was 0.970, sensitivity was 85.7, and specificity was 93.0, which was higher than that of each index alone.

The combined chest CT, PLT, CRP, and OI have high prognostic value for severe CAP.

To compare the effectiveness of the awake-prone position on relevant clinical outcomes in patients with COVID-19-related acute respiratory failure requiring high-flow nasal oxygen between different waves in Argentina.

This multicenter, prospective cohort study included adult patients with COVID-19-related acute respiratory failure requiring high-flow nasal oxygen. The main exposure position was the awake-prone position (≥ 6 hours/day) compared to the non-prone position. The primary outcome was endotracheal intubation, and the secondary outcome was in-hospital mortality. The inverse probability weighting-propensity score was used to adjust the conditional probability of treatment assignment. We then adjusted for contextual variables that varied over time and compared the effectiveness between the first and second waves.

A total of 728 patients were included: 360 during the first wave and 368 during the second wave, of whom 195 (54%) and 227 (62%) remained awake-prone for a median (p25 - 75) of 12 (10 - 16) and 14 (8 - 17) hours/day, respectively (Awake-Prone Position Group). The ORs (95%CIs) for endotracheal intubation in the Awake-Prone Position Group were 0.25 (0.13 - 0.46) and 0.19 (0.09 - 0.31) for the first and second waves, respectively (p = 0.41 for comparison between waves). The ORs for in-hospital mortality in the awake-prone position were 0.35 (0.17 - 0.65) and 0.22 (0.12 - 0.43), respectively (p = 0.44 for comparison between waves).

The awake-prone position was associated with a reduction in the risk of endotracheal intubation and in-hospital mortality. These effects were independent of the context in which the intervention was applied, and no differences were observed between the different waves.

Children infected with human adenovirus (HAdV) were at particularly high risk of developing severe disease, but the risk factors of severe conditions are poorly understood.

To explore the risk factors for developing into severe conditions in pediatric patients with HAdV infection by analyzing baseline epidemiological data, clinical characteristics, and computed tomography (CT) imaging features.

In this retrospective study, 267 children with HAdV infection were included between October 2016 and September 2021 at Tongji Hospital, Wuhan, China. A descriptive analysis was conducted on the epidemiological and clinical data, as well as patient outcomes. CT manifestations were semi-quantitatively scored based on the presence of consolidation, ground-glass opacities, and emphysema in each lung lobe.

Our analysis revealed that there was a statistically significant difference in the rate of abnormalities observed on chest CT imaging (P = 0.007) and the imaging characteristics of chest CTs (P = 0.002) when comparing severe and mild cases. We found that co-infection with two or more additional pathogens occurred more frequently in severe cases. Additionally, the proportion of lymphocytes in laboratory tests was significantly lower in patients with severe conditions. Furthermore, both the proportions and scores of consolidations were markedly higher in each lung lobe among the severe cases.

Our findings may assist in identifying children hospitalized with HAdV who are at increased risk for severe conditions, thereby facilitating more aggressive treatment and care strategies.

The impact of COVID-19 goes beyond its acute form and can lead to the persistence of symptoms and the emergence of systemic disorders, defined as long-term COVID.

We performed a cross-sectional study that included patients over 18 years of age who recovered from the severe form of COVID-19 at least 60 days after their discharge. Patients and controls were enrolled to undergo transthoracic echocardiography (TTE) using a more sensitive tool, myocardial work, in combination with cardiopulmonary exercise testing (CPET).

A total of 52 patients and 31 controls were enrolled. Significant differences were observed in ejection fraction (LVEF; 62 ± 7 vs. 66 ± 6 %; p = 0.007), global longitudinal strain (LVGLS; -18.7 ± 2.6 vs. -20.4 ± 1.4 %; p = 0.001), myocardial wasted work (GWW; 152 ± 81 vs. 101 ± 54 mmHg; p = 0.003), and myocardial work efficiency (GWE; 93 ± 3 vs. 95 ± 2 %; p = 0.002). We found a significant difference in peak VO2 (24.4 ± 5.4 vs. 33.4 ± 8.8 mL/kg/min; p < 0.001), heart rate (160 ± 14 vs. 176 ± 11 bpm; p < 0.001), ventilation (84.6 ± 22.6 vs. 104.9 ± 27.0 L/min; p < 0.001), OUES% (89 ± 16 vs. 102 ± 22 %; p = 0.002), T ½ (120.3 ± 32 vs. 97.6 ± 27 s; p = 0.002) and HRR at 2 min (-36 ± 11 vs. -43 ± 13 bpm; p = 0.010).

Our findings revealed an increased wasted work, with lower myocardial efficiency, significantly reduced aerobic exercise capacity, and abnormal heart rate response during recovery, which may be related to previously described late symptoms. The reduction in functional capacity during physical exercise is partly associated with a decrease in resting myocardial work efficiency. These findings strongly indicate the need to determine whether these manifestations persist in the long term and their impact on cardiovascular health and quality of life in COVID-19 survivors.

It remains unclear whether pulmonary fibrosis-like changes differ in patients with different SARS-CoV-2 variants. This study aimed to compare pulmonary fibrotic changes between two SARS-CoV-2 variant periods (delta vs. pre-delta) in critically ill patients with SARS-CoV-2 pneumonia. Clinical data and chest CT images of patients with SARS-CoV-2 pneumonia receiving mechanical ventilation were collected from 10 hospitals in South Korea over two periods: delta (July-December, 2021; n = 64) and pre-delta (February, 2020-June, 2021; n = 120). Fibrotic changes on chest CT were evaluated through visual assessment. Of 184 patients, the mean age was 64.6 years, and 60.5% were ale. Fibrosis-like changes on chest CT (median 51 days from enrollment to follow up CT scan, interquartile range 27-76 days) were identified in 75.3%. Delta group showed more fibrosis-like changes (≥ 2) (69.8% vs. 43.1%, P = 0.001) and more frequent reticulation and architectural distortion+/-parenchymal band than pre-delta group. Even after propensity score matching with clinical variables, delta group had more severe (≥ 2) fibrosis-like changes (71.4% vs. 38.8%, P = 0.001), and more frequent reticulation and architectural distortion+/-parenchymal band than pre-delta group. Our data suggest that critically ill patients with SARS-CoV-2 in delta period had more severe pulmonary fibrosis-like changes than those in pre-delta period.

The aim of this study is to examine the prognostic role of initial chest computed tomography severity score index (CTSS) and its association with demographic, socio-epidemiological, and clinical parameters in COVID-19 hospitalized patients. A retrospective study included patients who were hospitalized in the COVID Hospital of the Clinical Hospital Center Kosovska Mitrovica from July 2020 to March 2022. We compared patient characteristics and outcome of their hospital stay with values of CT severity score (mild, moderate, and severe form of the disease). Patients with severe disease were statistically significantly older, they treated more days, and they presented statistically significant highest mortality rate compared to mild and moderate forms. Smokers and obese were significantly more frequent among patients with higher CT, while vaccinated patients were more common among those with a mild form. Biochemical parameters at admission also showed statistical significance between the examined groups. We can conclude that by employing the initial CT severity score as the strongest predictor of mortality, it is possible to predict the outcome in hospitalized patients. A comprehensive examination of the patient upon admission, including determining the extent of inflammatory changes in the lungs using computed tomography, the levels of oxygen saturation, and other laboratory parameters, can assist doctors in making an adequate clinical evaluation and apply appropriate therapeutic protocols in the treatment of COVID-19.

In blunt chest trauma, patient management is challenging because clinical guidelines miss tools for risk assessment. No clinical scale reliably measures the severity of cases and the chance of complications.

The objective of the study was to optimize the management of patients with blunt chest trauma by creating models prognosticating the transfer to the intensive care unit and in-hospital length of stay (LOS).

The study cohort consisted of 212 cases. We retrieved information on the cases from the hospital's trauma registry. After segmenting the lungs with Lung CT Analyzer, we performed volumetric feature extraction with data-characterization algorithms in PyRadiomics.

To predict whether the patient will require intensive care, we used the three groups of findings: ambulance, admission, and radiomics data. When trained on the ambulance data, the models exhibited a borderline performance. The metrics improved after we retrained the models on a combination of ambulance, laboratory, radiologic, and physical examination data (81.5% vs. 94.4% Sn). Radiomics data were the top-accurate predictors (96.3% Sn). Age, vital signs, anthropometrics, and first aid time were the best-performing features collected by the ambulance service. Laboratory findings, AIS scores for the lower extremity, abdomen, head, and thorax constituted the top-rank predictors received on admission to the hospital. The original first-order kurtosis had the highest predictive value among radiomics data. Top-informative radiomics features were derived from the right hemithorax because the right lung is larger. We constructed regression models that can adequately reflect the in-hospital LOS. When trained on different groups of data, the machine-learning regression models showed similar performance (MAE/ROV ≈ 8%). Anatomic scores for the body parts other than thorax and laboratory markers of hemorrhage had the highest predictive value. Hence, the number of injured body parts correlated with the case severity.

The study findings can be used to optimize the management of patients with a chest blunt injury as a specific case of monotrauma. The models we built may help physicians to stratify patients by risk of worsening and overcome the limitations of existing tools for risk assessment. High-quality AI models trained on radiomics data demonstrate superior performance.

Hypokalemia is a common electrolyte disorder observed in patients afflicted with coronavirus disease 2019 (COVID-19). When COVID-19 is accompanied by pulmonary infection, chest computed tomography (CT) is the preferred diagnostic modality. This study aimed to explore the relationship between CT semi-quantitative score reflecting the degree of pulmonary infection and hypokalemia from COVID-19 patients.

A single-center, cross-sectional study was conducted to investigate patients diagnosed with COVID-19 between December 2022 and January 2023 who underwent chest CT scans upon admission revealing typical signs. These patients were categorized into two groups based on their blood potassium levels: the normokalemia group and the hypokalemia group. Medical history, symptoms, vital signs, laboratory data, and CT severity score were compared. Binary regression analysis was employed to identify risk factors associated with hypokalemia in COVID-19 patients with pulmonary infection.

A total of 288 COVID-19 patients with pulmonary infection were enrolled in the study, of which 68 (23.6%) patients had hypokalemia. The CT severity score was found to be higher in the hypokalemia group compared to the normokalemia group [4.0 (3.0-5.0) vs. 3.0 (2.0-4.0), p = 0.001]. The result of binary logistic regression analysis revealed that after adjusting for sex, vomiting, sodium, and using potassium-excretion diuretics, higher CT severity score was identified as an independent risk factor for hypokalemia (OR = 1.229, 95% CI = 1.077-1.403, p = 0.002).

In this cohort of patients, semi-quantitative CT score reflecting the degree of pulmonary infection may serve as a risk factor of hypokalemia in COVID-19 patients.

Patients with severe Coronavirus Disease 2019 (COVID-19) can experience protein loss due to the inflammatory response and energy consumption, impairing immune function. The presence of excessive visceral and heart fat leads to chronic long-term inflammation that can adversely affect immune function and, thus, outcomes for these patients. We aimed to explore the roles of prognostic nutrition index (PNI) and quantitative fat assessment based on computed tomography (CT) scans in predicting the outcomes of patients with severe COVID-19.

A total of 130 patients with severe COVID-19 who were treated between December 1, 2022, and February 28, 2023, were retrospectively enrolled. The patients were divided into survival and death groups. Data on chest CT examinations following admission were collected to measure cardiac adipose tissue (CAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) and to analyze the CT score of pulmonary lesions. Clinical information and laboratory examination data were collected. Univariate and multivariate logistic regression analyses were used to explore the risk factors associated with death, and several multivariate logistic regression models were established.

Of the 130 patients included in the study (median age, 80.5 years; males, 32%), 68 patients died and 62 patients survived. PNI showed a strong association with the outcome of severe COVID-19 (p < 0.001). Among each part of the fat volume obtained based on a CT scan, SAT showed a significant association with the mortality of severe COVID-19 patients (p = 0.007). However, VAT and CAT were not significantly correlated with the death of patients. In the multivariate models, SAT had a higher predictive value than PNI; the area under the curve (AUC) of SAT was 0.844, which was higher than that of PNI (AUC = 0.833), but in the model of the combination of the two indexes, the prediction did not improve (AUC = 0.830), and SAT lost its significance (p = 0.069).

Subcutaneous adipose tissue measured by computed tomography and PNI were found to be independent predictors of death in patients with severe COVID-19.

Previous studies have shown that patients with pre-existing chronic obstructive pulmonary diseases (COPD) were more likely to be infected with coronavirus disease (COVID-19) and lead to more severe lung lesions. However, few studies have explored the severity and prognosis of COVID-19 patients with different phenotypes of COPD.

The aim of this study is to investigate the value of the deep learning and radiomics features for the severity evaluation and the nucleic acid turning-negative time prediction in COVID-19 patients with COPD including two phenotypes of chronic bronchitis predominant patients and emphysema predominant patients.

A total of 281 patients were retrospectively collected from Hohhot First Hospital between October 2022 and January 2023. They were divided to three groups: COVID-19 group of 95 patients, COVID-19 with emphysema group of 94 patients, COVID-19 with chronic bronchitis group of 92 patients. All patients underwent chest computed tomography (CT) scans and recorded clinical data. The U-net model was pretrained to segment the pulmonary involvement area on CT images and the severity of pneumonia were evaluated by the percentage of pulmonary involvement volume to lung volume. The 107 radiomics features were extracted by pyradiomics package. The Spearman method was employed to analyze the correlation of the data and visualize it through a heatmap. Then we establish a deep learning model (model 1) and a fusion model (model 2) combined deep learning with radiomics features to predict nucleic acid turning-negative time.

COVID-19 patients with emphysema was lowest in the lymphocyte count compared to COVID-19 patients and COVID-19 companied with chronic bronchitis, and they have the most extensive range of pulmonary inflammation. The lymphocyte count was significantly correlated with pulmonary involvement and the time for nucleic acid turning negative (r=-0.145, P < 0.05). Importantly, our results demonstrated that model 2 achieved an accuracy of 80.9% in predicting nucleic acid turning-negative time.

The pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement of COVID-19 patients. Deep learning and radiomics features may provide more information to accurately predict the nucleic acid turning-negative time, which is expected to play an important role in clinical practice.

Coronavirus disease 2019 (COVID-19) still poses a threat to people's physical and mental health. We proposed a new semi-quantitative visual classification method for COVID-19, and this study aimed to evaluate the clinical usefulness and feasibility of lung field-based severity score (LFSS).

This retrospective study included 794 COVID-19 patients from two hospitals in China between December 2022 and January 2023. Six lung fields on the axial computed tomography (CT) were defined. LFSS and eighteen clinical characteristics were evaluated. LFSS was based on summing up the parenchymal opacification involving each lung field, which was scored as 0 (0%), 1 (1-24%), 2 (25-49%), 3 (50-74%), or 4 (75-100%), respectively (range of LFSS from 0 to 24). Total pneumonia burden (TPB) was calculated using the U-net model. The correlation between LFSS and TPB was analyzed. After performing logistic regression analysis, an LFSS-based model, clinical-based model and combined model were developed. Receiver operating characteristic curves were used to evaluate and compare the performance of three models.

LFSS, age, chronic liver disease, chronic kidney disease, white blood cell, neutrophils, lymphocytes and C-reactive protein differed significantly between the non-critical and critical group (all P<0.05). There was a strong positive correlation of LFSS and TPB (Pearson correlation coefficient =0.767, P<0.001). The area under curves of LFSS-based model, clinical-based model and combined model were 0.799 [95% confidence interval (CI): 0.770-0.827], 0.758 (95% CI: 0.727-0.788), and 0.848 (95% CI: 0.821-0.872), respectively.

The LFSS derived from chest CT may be a potential new tool to help identify COVID-19 patients at high risk of progressing to critical disease.

CT-scan and inflammatory and coagulation biomarkers could help in prognostication of COVID-19 in patients on ICU admission.

The objectives of this study were to measure the prognostic value of the extent of lung parenchymal lesions on computed tomography (CT) and of several coagulation and inflammatory biomarkers, and to explore the characteristics of the patients depending on the extent of lung parenchymal lesions.

Retrospective monocentric observational study achieved on a dataset collected prospectively.

Medical ICU of the university hospital of Clermont-Ferrand, France.

All consecutive adult patients aged ≥18 years admitted between 20 March, 2020 and 31 August, 2021 for COVID-19 pneumonia.

Characteristics at baseline and during ICU stay, and outcomes at day 60 were recorded. The extent of lung parenchyma lesions observed on the chest CT performed on admission was established by artificial intelligence software.

Several clinical characteristics and laboratory features were collected on admission including plasma interleukin-6, HLA-DR monocytic-expression rate (mHLA-DR), and the extent of lung parenchymal lesions. Factors associated with day-60 mortality were investigated by uni- and multivariate survival analyses.

270 patients were included. Inflammation biomarkers including the levels of neutrophils, CRP, ferritin and Il10 were the indices the most associated with the severity of the extent of the lung lesions. Patients with more extensive lung parenchymal lesions (≥ 75%) on admission had higher CRP serum levels. The extent of lung parenchymal lesions was associated with a decrease in the PaO2/FiO2 ratio(p<0.01), fewer ventilatory-free days (p = 0.03), and a higher death rate at day 60(p = 0.01). Extent of the lesion of more than 75% was independently associated with day-60 mortality (aHR = 1.72[1.06; 2.78], p = 0.03). The prediction of death at day 60 was improved when considering simultaneously biological and radiological markers obtained on ICU admission (AUC = 0.78).

The extent of lung parenchyma lesions on CT was associated with inflammation, and the combination of coagulation and inflammatory biomarkers and the extent of the lesions predicted the poorest outcomes.

To explore and validate the value of clinical parameters combined with plasma biomarkers for predicting acute respiratory distress syndrome (ARDS) in patients of high risks in the surgical intensive care unit (SICU).

We conducted a prospective, observational study from January 2020 to December 2023, which enrolled 263 patients of high risks in the SICU of Peking University Third Hospital consecutively; they were classified as ARDS and non-ARDS according to whether ARDS occurred after enrollment. Collected clinical characteristics and blood samples within 24 hr of admission to SICU. Blood samples from the first day to the seventh day of SICU were collected from patients without ARDS, and patients with ARDS were collected until 1 day after ARDS onset, forming data based on time series. ELISA and CBA were used to measure plasma biomarkers. Endpoint of the study was the onset of ARDS. Cox proportional hazard regression analysis was used to find independent risk factors of the onset of ARDS, then constructed a nomogram and tested its goodness-of-fit.

About 84 of 263 patients ended with ARDS. Univariate analysis found 15 risk factors showed differences between ARDS and non-ARDS, namely, interleukin 6, interleukin 8 (IL-8), angiopoietin Ⅱ, LIPS, APACHEⅡ, SOFA, PaO2/FiO2, age, sex, shock, sepsis, acute abdomen, pulmonary contusion, pneumonia, hepatic dysfunction. We included factors with p  < 0.2 in multivariate analysis and showed LIPS, PaO2/FiO2, IL-8, and receptor for advanced glycation end-products (RAGE) of the first day were independent risk factors for ARDS in SICU, a model combining them was good in predicting ARDS (C-index was 0.864 in total patients of high risks). The median of the C-index was 0.865, showed by fivefold cross-validation in the train cohort or validation cohort. The calibration curve shows an agreement between the probability of predicting ARDS and the actual probability of occurrence. Decision curve analysis indicated that the model had clinical use value. We constructed a nomogram that had the ability to predict ARDS in patients of high risks in SICU.

LIPS, PaO2/FiO2, plasma IL-8, and RAGE of the first day were independent risk factors of the onset of ARDS. The predictive ability for ARDS can be greatly improved when combining clinical parameters and plasma biomarkers.

Long COVID is defined as persistency of symptoms, such as exertional dyspnea, twelve weeks after recovery from SARS-CoV-2 infection.

To investigate ventilatory efficiency by the use of cardiopulmonary exercise testing (CPET) in patients with exertional dyspnea despite normal basal spirometry after 18 (T18) and 36 months (T36) from COVID-19 pneumonia.

One hundred patients with moderate-critical COVID-19 were prospectively enrolled in our Long COVID program. Medical history, physical examination and lung high-resolution computed tomography (HRCT) were obtained at hospitalization (T0), 3 (T3) and 15 months (T15). All HRCTs were revised using a semi-quantitative CT severity score (CSS). Pulmonary function tests were obtained at T3 and T15. CPET was performed in a subset of patients with residual dyspnea (mMRC ≥ 1), at T18 and at T36.

Remarkably, at CPET, ventilatory efficiency was reduced both at T18 (V'E/V'CO2 slope = 31.4±3.9 SD) and T36 (V'E/V'CO2 slope = 31.28±3.70 SD). Furthermore, we identified positive correlations between V'E/V'CO2 slope at T18 and T36 and both percentage of involvement and CSS at HRCT at T0, T3 and T15. Also, negative linear correlations were found between V'E/V'CO2 slope at T18 and T36 and DLCO at T3 and T15.

At eighteen months from COVID-19 pneumonia, 20 % of subjects still complains of exertional dyspnea. At CPET this may be explained by persistently reduced ventilatory efficiency, possibly related to the degree of lung parenchymal involvement in the acute phase of infection, likely reflecting a damage in the pulmonary circulation.

Chest computed tomography (CT) scan is useful to evaluate the type and extent of lung lesions in coronavirus disease 2019 (COVID-19) pneumonia. This study explored the association between radiological parameters and various circulating serum-derived markers, including microRNAs, in older patients with COVID-19 pneumonia.

A retrospective analysis was designed to study geriatric patients (≥75 years) with COVID-19 pneumonia, who underwent chest CT scan on admission, and for whom clinical data and serum samples were obtained. To quantify the extent of lung involvement, CT-score, the percentage of healthy lung (HL%), the percentage of ground glass opacity (GGO%), and the percentage of lung consolidation were assessed using computer-aided tools. The association of these parameters with two circulating microRNAs, miR-483-5p and miR-320b, previously identified as biomarkers of mortality risk in COVID-19 geriatric patients, was tested.

A total of 73 patients with COVID-19 pneumonia were evaluable (median age 85 years; interquartile range 82-90 years). Among chest CT-derived parameters, the percentage of lung consolidation (HR 1.08, 95% CI 1.02-1.14), CT-score (HR 1.14, 95% CI 1.03-1.25), and HL% (HR 0.97, 95% CI 0.95-0.99) emerged as significant predictors of mortality, whereas non-significant trends toward increased mortality were observed in patients with higher GGO%. We also found a significant positive association between serum miR-483-5p and GGO% (correlation coefficient 0.28; P = 0.018) and a negative association with HL% (correlation coefficient -0.27; P = 0.023).

Overall, the extent of lung consolidation can be confirmed as a prognostic parameter of COVID-19 pneumonia in older patients. Among various serum-derived markers, miR-483-5p can help in exploring the degree of lung involvement, due to its association with higher GGO% and lower HL%. Geriatr Gerontol Int 2024; 24: 962-972.