Chest CT abnormalities in COVID-19: a systematic review

Differences in chest imaging between Omicron and non-Omicron coronavirus disease 2019 (COVID-19) patients: a systematic review and meta-analysis

BACKGROUND

Coronavirus disease 2019 (COVID-19) have posed a great threat to human health. We carried out this systematic review and meta-analysis for two objectives. First, to evaluate the differences in lung infection between the Omicron variants and the non-Omicron strains by chest computed tomography (CT); second, to evaluate the differences in chest CT features between COVID-19 patients with the Omicron variants and those with non-Omicron strains in CT-positive cases.

METHODS

We searched PubMed, Embase, Web of Science and China National Knowledge Infrastructure for articles and performed a meta-analysis using Stata 14.0 with a random effects model.

RESULTS

Our study included a total of 8126 patients with COVID-19, 4113 with the Omicron variants, and 4013 with non-Omicron strains. Patients with the Omicron variants were less likely to be CT-positive (OR = 0.14, 95% CI: 0.08-0.25), and further analysis among CT-positive patients was performed. Compared with the CT images of patients with non-Omicron strains, those of patients with the Omicron variants showed atypical pulmonary features (OR = 4.02, 95% CI: 2.31-6.98). Moreover, patients with the Omicron variants typically had lesions that were mainly located in the center of the lung (OR = 4.51, 95% CI: 1.38-14.76) and in a single lobe (OR = 1.72, 95% CI: 1.10-2.70). The patients with the Omicron variants were less likely to have lesions in both lungs (OR = 0.33, 95% CI: 0.15-0.69), more likely to have bronchial wall thickening (OR = 1.99, 95% CI: 1.05-3.77) and less likely to have the crazy-paving pattern (OR = 0.51, 95% CI: 0.33-0.81), linear opacity (OR = 0.26, 95% CI: 0.12-0.60), and vascular enlargement (OR = 0.54, 95% CI: 0.35-0.84).

CONCLUSIONS

Through meta-analysis, which yields the highest level of evidence for evidence-based medicine, we further confirmed that there were significant differences in the distribution and manifestations of lesions between patients with non-Omicron strains and those with the Omicron variants on chest CT. The variation in SARS-CoV-2 has never stopped. Our findings are useful for the diagnosis and treatment of new SARS-CoV-2 variants that may appear in the future and provide a basis for public health decision-making.

PROSPERO REGISTRATION NUMBER

CRD42024581869.

Epidemiological characteristics and clinical treatment of melioidosis: a 11-year retrospective cohort study in Hainan

BACKGROUND

Melioidosis is a tropical infectious disease caused by Burkholderia pseudomallei, characterised by a high case fatality rate.

OBJECTIVES

We summarized the cases of melioidosis at Sanya People's Hospital in Hainan over the past eleven years. This information served as a reference for the epidemiological study, diagnosis, treatment, and prevention of melioidosis in China.

METHODS

A retrospective study was conducted to compile clinical data from 138 melioidosis patients treated at Sanya People's Hospital in Hainan Province between 2012 and 2023. By comparing these data with domestic and international clinical case studies, the study aimed to summarise the epidemiological characteristics, clinical manifestations, and therapeutic regimens of melioidosis in Hainan Island.

RESULTS

This study revealed that 84.1% of melioidosis cases were observed in males (116/138). The predominant age group affected was 40 to 60 years, constituting 58.0% (80/138) of the total cases. Farmers and fishermen represented the primary demographic, accounting for 63.8% (88/138). The peak incidence of melioidosis in Hainan was observed in the wet season (summer and autumn months), representing 79.0% of cases (109/138). The most prevalent comorbidity in melioidosis cases was diabetes mellitus (77.5%). Bacteremic melioidosis was the predominant infection type (81.9%). Compared with the non-bacteremic group, the bacteremic group exhibited significantly higher incidences of complications, disseminated infections, and abnormal chest CT findings (p < 0.001, respectively). Further analysis indicated that patients with melioidosis and abnormal chest CT findings had an increased likelihood of concurrent bacteremia (OR = 7.289, 95%CI 1.608-33.039, p = 0.010). During the acute phase of anti-infective treatment, 37.7% (52/138) of the patients underwent intravenous anti-infective drug therapy for at least 2 weeks. Additionally, 56.5% (78/138) of the patients received carbapenems (Meropenem or Imipenem, MEPN or IPM) as part of their anti-infective therapy. In the eradication phase of treatment, 66.0% (66/100) of the patients completed the recommended treatment duration of at least 12 weeks. Furthermore, the majority (90/100, 90.0%) received monotherapy with trimethoprim-sulfamethoxazole (TMP-SMX).

CONCLUSION

In Hainan Island, the prevalence of melioidosis is notably high among middle-aged male outdoor workers, exhibiting a distinct seasonal pattern with most cases occurring during the summer and autumn months. Bacteremia represents the most common form of melioidosis infection, and abnormal chest CT findings in melioidosis patients serve as a significant hint of bacteremia. Currently, the selection of antimicrobial agents for melioidosis treatment in Hainan Province generally adheres to international guidelines; however, the process requires further standardisation.

Differences of the Chest Images Between Coronavirus Disease 2019 (COVID-19) Patients and Influenza Patients: A Systematic Review and Meta-analysis

Background: Coronavirus disease 2019 (COVID-19) and influenza are two infectious diseases that can pose a great threat to human health. We aimed to compare the differences in chest images between patients with COVID-19 and influenza to deepen the understanding of these two diseases. Methods: We searched PubMed, Embase and Web of Science for articles published before December 25, 2023, and performed a meta-analysis using Stata 14.0 with a random-effects model. The study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Twenty-six articles with 2,159 COVID-19 patients and 1,568 influenza patients were included in the meta-analysis. By comparing chest computed tomography (CT) and chest X-ray, we found that COVID-19 patients had more peripheral lung lesions (OR=3.66, 95% CI: 1.84-7.31). Although COVID-19 patients had more bilateral lung involvement (OR=1.74, 95% CI: 0.90-3.38) and less unilateral lung involvement (OR=0.67, 95% CI: 0.44-1.02), these two results were not statistically significant. Patients with COVID-19 showed more ground-glass opacities (OR=2.83, 95% CI: 1.85-4.32), reverse halo signs (OR=3.47, 95% CI: 2.37-5.08), interlobular septal thickening (OR=2.16, 95% CI: 1.55-3.01), vascular enlargement (OR=5.00, 95% CI: 1.80-13.85) and crazy-paving patterns (OR=2.63, 95% CI: 1.57-4.41) on chest images than patients with influenza. We also found that compared with influenza patients, pleural effusion was rare in COVID-19 patients (OR=0.15, 95% CI: 0.07-0.31). Conclusions: There are some differences in the manifestations and distributions of lesions between patients with COVID-19 and influenza on chest images, which is helpful to distinguish these two infectious diseases.

Predictive factors of mortality in patients admitted to the emergency department for SARS-Cov2 pneumonia

INTRODUCTION

The overcrowding of intensive care units during the corona virus pandemic increased the number of patients managed in the emergency department (ED). The detection timely of the predictive factors of mortality and bad outcomes improve the triage of those patients.

AIM

To define the predictive factors of mortality at 30 days among patients admitted on ED for covid-19 pneumonia.

METHODS

This was a prospective, monocentric, observational study for 6 months. Patients over the age of 16 years admitted on the ED for hypoxemic pneumonia due to confirmed SARS-COV 2 infection by real-time reverse-transcription polymerase chain reaction (rRT-PCR) were included. Multivariate logistic regression was performed to investigate the predictive factors of mortality at 30 days.

RESULTS

463 patients were included. Mean age was 65±14 years, Sex-ratio=1.1. Main comorbidities were hypertension (49%) and diabetes (38%). Mortality rate was 33%. Patients who died were older (70±13 vs. 61±14;p<0.001), and had more comorbidities: hypertension (57% vs. 43%, p=0.018), chronic heart failure (8% vs. 3%, p=0.017), and coronary artery disease (12% vs. 6%, p=0.030). By multivariable analysis, factors independently associated with 30-day mortality were age ≥65 years aOR: 6.9, 95%CI 1.09-44.01;p=0.04) SpO2<80% (aOR: 26.6, 95%CI 3.5-197.53;p=0.001) and percentage of lung changes on CT scan>70% (aOR: 5.6% 95%CI .01-31.29;p=0.04).

CONCLUSION

Mortality rate was high among patients admitted in the ED for covid-19 pneumonia. The identification of predictive factors of mortality would allow better patient management.

Pathology-based radiation dose in computed tomography: investigation of the effect of lung lesions on water-equivalent diameter, CTDIVol and SSDE in COVID-19 patients

Lung lesions can increase the CT number and affect the water-equivalent diameter (Dw), Dw-based conversion factor (CFw), and Dw-based size-specific dose estimate (SSDEw). We evaluated the effect of COVID-19 lesions and total severity score (TSS) on radiation dose considering the effect of automatic tube current modulation (ATCM) and fixed tube current (FTC). A total of 186 chest CT scans were categorised into five TSS groups, including healthy, minimal, mild, moderate and severe. The effective diameter (Deff), Dw, CFw, Deff-based conversion factor (CFeff), volume computed tomography dose index (CTDIVol), pathological dose impact factor (PDIF) 1 and SSDEw were calculated. TSS was correlated with Dw (r = 0.29, p-value = 0.001), CTDIVol (ATCM) (r = 0.23, p = 0.001) and PDIF (r = - 0.51, p-value = 0.001). $\overline{{\mathrm{SSDE}}_{\mathrm{w}}}$ (FTC) was significantly different among all groups. $\overline{{\mathrm{SSDE}}_{\mathrm{w}}}$ (ATCM) was greater for moderate (13%) and mild (14%) groups. Increasing TSS increase the Dw and causes a decrease in CFw and $\overline{{\mathrm{SSDE}}_{\mathrm{w}}}$ (FTC), and can increase $\overline{{\mathrm{SSDE}}_{\mathrm{w}}}$ (ATCM) in some Dw ranges.

Precision Medicine for More Oxygen (P4O2)-Study Design and First Results of the Long COVID-19 Extension

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has led to the death of almost 7 million people, however, with a cumulative incidence of 0.76 billion, most people survive COVID-19. Several studies indicate that the acute phase of COVID-19 may be followed by persistent symptoms including fatigue, dyspnea, headache, musculoskeletal symptoms, and pulmonary functional-and radiological abnormalities. However, the impact of COVID-19 on long-term health outcomes remains to be elucidated. Aims: The Precision Medicine for more Oxygen (P4O2) consortium COVID-19 extension aims to identify long COVID patients that are at risk for developing chronic lung disease and furthermore, to identify treatable traits and innovative personalized therapeutic strategies for prevention and treatment. This study aims to describe the study design and first results of the P4O2 COVID-19 cohort. Methods: The P4O2 COVID-19 study is a prospective multicenter cohort study that includes nested personalized counseling intervention trial. Patients, aged 40-65 years, were recruited from outpatient post-COVID clinics from five hospitals in The Netherlands. During study visits at 3-6 and 12-18 months post-COVID-19, data from medical records, pulmonary function tests, chest computed tomography scans and biological samples were collected and questionnaires were administered. Furthermore, exposome data was collected at the patient's home and state-of-the-art imaging techniques as well as multi-omics analyses will be performed on collected data. Results: 95 long COVID patients were enrolled between May 2021 and September 2022. The current study showed persistence of clinical symptoms and signs of pulmonary function test/radiological abnormalities in post-COVID patients at 3-6 months post-COVID. The most commonly reported symptoms included respiratory symptoms (78.9%), neurological symptoms (68.4%) and fatigue (67.4%). Female sex and infection with the Delta, compared with the Beta, SARS-CoV-2 variant were significantly associated with more persisting symptom categories. Conclusions: The P4O2 COVID-19 study contributes to our understanding of the long-term health impacts of COVID-19. Furthermore, P4O2 COVID-19 can lead to the identification of different phenotypes of long COVID patients, for example those that are at risk for developing chronic lung disease. Understanding the mechanisms behind the different phenotypes and identifying these patients at an early stage can help to develop and optimize prevention and treatment strategies.

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia

BACKGROUND

The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model.

METHODS

LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model.

RESULTS

Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81.

CONCLUSIONS

Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts.

KEY POINTS

We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database

Background and Objective

The spread of coronavirus has been challenging for the healthcare system's proper management and diagnosis during the rapid spread and control of the infection. Real-time reverse transcription-polymerase chain reaction (RT-PCR), though considered the standard testing measure, has low sensitivity and is time-consuming, which restricts the fast screening of individuals. Therefore, computer tomography (CT) is used to complement the traditional approaches and provide fast and effective screening over other diagnostic methods. This work aims to appraise the importance of chest CT findings of COVID-19 and post-COVID in the diagnosis and prognosis of infected patients and to explore the ways and means to integrate CT findings for the development of advanced Artificial Intelligence (AI) tool-based predictive diagnostic techniques.

Methods

The retrospective study includes a 188 patient database with COVID-19 infection confirmed by RT-PCR testing, including post-COVID patients. Patients underwent chest high-resolution computer tomography (HRCT), where the images were evaluated for common COVID-19 findings and involvement of the lung and its lobes based on the coverage region. The radiological modalities analyzed in this study may help the researchers in generating a predictive model based on AI tools for further classification with a high degree of reliability.

Results

Mild to moderate ground glass opacities (GGO) with or without consolidation, crazy paving patterns, and halo signs were common COVID-19 related findings. A CT score is assigned to every patient based on the severity of lung lobe involvement.

Conclusion

Typical multifocal, bilateral, and peripheral distributions of GGO are the main characteristics related to COVID-19 pneumonia. Chest HRCT can be considered a standard method for timely and efficient assessment of disease progression and management severity. With its fusion with AI tools, chest HRCT can be used as a one-stop platform for radiological investigation and automated diagnosis system.

Características clínicas, imagenológicas y de laboratorio de pacientes con COVID-19 según requerimiento de ingreso a UCI en Cali, Colombia

Introducción. Actualmente, hay pocos estudios en Latinoamérica sobre las características demográficas, clínicas y de laboratorio de pacientes con COVID-19 y con requerimiento de ingreso a unidad de cuidados intensivos (UCI). Objetivo. Comparar las características sociodemográficas, clínicas, imagenológicas y de laboratorio de pacientes diagnosticados con COVID-19 atendidos en el servicio de urgencias de una clínica en Cali, Colombia, según requerimiento de ingreso a UCI. Materiales y métodos. Estudio retrospectivo descriptivo de cohorte única realizado en 49 adultos con COVID-19 atendidos en el servicio de urgencias de un hospital de cuarto nivel de atención en Cali, Colombia, en marzo y abril de 2020, los cuales se dividieron en dos grupos: requerimiento de UCI (n=24) y no requerimiento de UCI (n=25). Se realizaron análisis bivariados para determinar las diferencias entre ambos grupos (pruebas de chi-2, exacta de Fisher, t de Student y U de Mann-Whitney), con un nivel de significancia de p<0.05. Resultados. La edad promedio fue 53 años (DE=13) y 29 pacientes fueron hombres. Se encontraron diferencias significativas entre ambos grupos en las siguientes variables: edad promedio (UCI x̅=58 vs. No UCI x̅=49; p=0.020), presencia de diabetes (8 vs. 1; p=0.010), presencia de dificultad respiratoria (20 vs. 11; p=0.007), presencia uni o bilateral de áreas de consolidación (12 vs. 3; p=0.005), mediana del conteo de leucocitos (Med=7570/mm3 vs. Med=5130/mm3; p=0.0013), de neutrófilos (Med=5980/mm3 vs. Med=3450/mm3; p=0,0001) y linfocitos (Med=865/mm3 vs. Med=1400/mm3; p<0,0001), mediana de proteína C reactiva (Med=141,25mg/L vs. Med=27,95mg/L; p<0,001), ferritina (Med=1038ng/L vs. Med=542,5ng/L; p=0.0073) y lactato-deshidrogenasa (Med=391U/L vs, Med=248,5U/L, p=0,0014). Finalmente, 15 pacientes requirieron ventilación mecánica invasiva, 2 presentaron extubación fallida, y en total, 5 fallecieron. Conclusiones. Se observaron diferencias significativas en los valores de varios marcadores inflamatorios, daño celular y parámetros del hemograma entre los pacientes que requirieron admisión a la UCI y los que no, por lo que estas variables podrían emplearse para desarrollar herramientas que contribuyan a establecer el pronóstico de esta enfermedad.

A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes

There is still an urgent need to develop effective treatments to help minimize the cases of severe COVID-19. A number of tools have now been developed and applied to address these issues, such as the use of non-contrast chest computed tomography (CT) for evaluation and grading of the associated lung damage. Here we used a deep learning approach for predicting the outcome of 1078 patients admitted into the Baqiyatallah Hospital in Tehran, Iran, suffering from COVID-19 infections in the first wave of the pandemic. These were classified into two groups of non-severe and severe cases according to features on their CT scans with accuracies of approximately 0.90. We suggest that incorporation of molecular and/or clinical features, such as multiplex immunoassay or laboratory findings, will increase accuracy and sensitivity of the model for COVID-19 -related predictions.

COVID-19 in Children: Update on Diagnosis and Management

In December 2019, a new infectious disease called coronavirus disease 2019 (COVID-19) attributed to the new virus named severe scute respiratory syndrome coronavirus 2 (SARS-CoV-2) was detected. The gold standard for the diagnosis of SARS-CoV-2 infection is the viral identification in nasopharyngeal swab by real-time polymerase chain reaction. Few data on the role of imaging are available in the pediatric population. Similarly, considering that symptomatic therapy is adequate in most of the pediatric patients with COVID-19, few pediatric pharmacological studies are available. The main aim of this review is to describe and discuss the scientific literature on various imaging approaches and therapeutic management in children and adolescents affected by COVID-19. Clinical manifestations of COVID-19 are less severe in children than in adults and as a consequence the radiologic findings are less marked. If imaging is needed, chest radiography is the first imaging modality of choice in the presence of moderate-to-severe symptoms. Regarding therapy, acetaminophen or ibuprofen are appropriate for the vast majority of pediatric patients. Other drugs should be prescribed following an appropriate individualized approach. Due to the characteristics of COVID-19 in pediatric age, the importance of strengthening the network between hospital and territorial pediatrics for an appropriate diagnosis and therapeutic management represents a priority.