Radiological, epidemiological and clinical patterns of pulmonary viral infections

Lung ultrasound and procalcitonin, improving antibiotic management and avoiding radiation exposure in pediatric critical patients with bacterial pneumonia: a randomized clinical trial

BACKGROUND

Pneumonia is a major public health problem with an impact on morbidity and mortality. Its management still represents a challenge. The aim was to determine whether a new diagnostic algorithm combining lung ultrasound (LUS) and procalcitonin (PCT) improved pneumonia management regarding antibiotic use, radiation exposure, and associated costs, in critically ill pediatric patients with suspected bacterial pneumonia (BP).

METHODS

Randomized, blinded, comparative effectiveness clinical trial. Children < 18y with suspected BP admitted to the PICU from September 2017 to December 2019, were included. PCT was determined at admission. Patients were randomized into the experimental group (EG) and control group (CG) if LUS or chest X-ray (CXR) were done as the first image test, respectively. Patients were classified: 1.LUS/CXR not suggestive of BP and PCT < 1 ng/mL, no antibiotics were recommended; 2.LUS/CXR suggestive of BP, regardless of the PCT value, antibiotics were recommended; 3.LUS/CXR not suggestive of BP and PCT > 1 ng/mL, antibiotics were recommended.

RESULTS

194 children were enrolled, 113 (58.2%) females, median age of 134 (IQR 39-554) days. 96 randomized into EG and 98 into CG. 1. In 75/194 patients the image test was not suggestive of BP with PCT < 1 ng/ml; 29/52 in the EG and 11/23 in the CG did not receive antibiotics. 2. In 101 patients, the image was suggestive of BP; 34/34 in the EG and 57/67 in the CG received antibiotics. Statistically significant differences between groups were observed when PCT resulted < 1 ng/ml (p = 0.01). 3. In 18 patients the image test was not suggestive of BP but PCT resulted > 1 ng/ml, all of them received antibiotics. A total of 0.035 mSv radiation/patient was eluded. A reduction of 77% CXR/patient was observed. LUS did not significantly increase costs.

CONCLUSIONS

Combination of LUS and PCT showed no risk of mistreating BP, avoided radiation and did not increase costs. The algorithm could be a reliable tool for improving pneumonia management.

CLINICAL TRIAL REGISTRATION

NCT04217980.

Viral Pneumonias

Viral pneumonia is usually community acquired and caused by influenza, parainfluenza, respiratory syncytial virus, human metapneumovirus, and adenovirus. Many of these infections are airway centric and chest imaging demonstrates bronchiolitis and bronchopneumonia, With the exception of adenovirus infections, the presence of lobar consolidation usually suggests bacterial coinfection. Community-acquired viral pathogens can cause more severe pneumonia in immunocompromised hosts, who are also susceptible to CMV and varicella infection. These latter 2 pathogens are less likely to manifest the striking airway-centric pattern. Airway-centric pattern is distinctly uncommon in Hantavirus pulmonary syndrome, a rare environmentally acquired infection with high mortality.

A prediction model for acute respiratory distress syndrome in immunocompetent adults with adenovirus-associated Pneumonia: a multicenter retrospective analysis

BACKGROUND

In recent years, the number of human adenovirus (HAdV)-related pneumonia cases has increased in immunocompetent adults. Acute respiratory distress syndrome (ARDS) in these patients is the predominant cause of HADV-associated fatality rates. This study aimed to identify early risk factors to predict early HAdV-related ARDS.

METHODS

Data from immunocompetent adults with HAdV pneumonia between June 2018 and May 2022 in ten tertiary general hospitals in central China was analyzed retrospectively. Patients were categorized into the ARDS group based on the Berlin definition. The prediction model of HAdV-related ARDS was developed using multivariate stepwise logistic regression and visualized using a nomogram.

RESULTS

Of 102 patients with adenovirus pneumonia, 41 (40.2%) developed ARDS. Overall, most patients were male (94.1%), the median age was 38.0 years. Multivariate logistic regression showed that dyspnea, SOFA (Sequential Organ Failure Assessment) score, lactate dehydrogenase (LDH) and mechanical ventilation status were independent risk factors for this development, which has a high mortality rate (41.5%). Incorporating these factors, we established a nomogram with good concordance statistics of 0.904 (95% CI 0.844-0.963) which may help to predict early HAdV-related ARDS.

CONCLUSION

A nomogram with good accuracy in the early prediction of ARDS in patients with HAdV-associated pneumonia may could contribute to the early management and effective treatment of severe HAdV infection.

Using Non-Invasive Respiratory Monitoring for COVID-19 Pulmonary Embolism Diagnosis

With the high incidence rate of pulmonary embolism (PE) and pneumonia reported in hospitalized patients with COVID-19, the ability to determine the dominant etiology for severe respiratory distress quickly and accurately is crucial to a patient's well-being. Traditionally, D-dimer blood tests and diagnostic imaging studies would be utilized to determine the presence of a PE or a venous thromboembolism. However, COVID-19 places patients in a prothrombotic state and performing diagnostic imaging studies on all patients with COVID-19 would be impractical, making the need for a simple and reliable method to determine the likelihood of PE or venous thromboembolism a priority for emergency departments. The authors believe the use of non-invasive respiratory monitoring technology to assess lung function in hospitalized patients with COVID-19 can aid in discerning the dominant hypoxia etiology and tailoring of their treatment. Here, the authors outline a case and method of using non-invasive respiratory monitoring of lung function in the successful diagnosis of a PE in a 62-year-old patient with COVID-19.

Application Potential of Luteolin in the Treatment of Viral Pneumonia

Aim of the Review. This study aims to summarize the therapeutic effect of luteolin on the pathogenesis of viral pneumonia, explore its absorption and metabolism in the human body, evaluate the possibility of luteolin as a drug to treat viral pneumonia, and provide a reference for future research. Materials and Methods. We searched MEDLINE/PubMed, Web of Science, China National Knowledge Infrastructure, and Google Scholar and collected research on luteolin in the treatment of viral pneumonia and related diseases since 2003. Then, we summarized the efficacy and potential of luteolin in directly inhibiting viral activity, limiting inflammatory storms, reducing pulmonary inflammation, and treating pneumonia complications. Results and Conclusion. Luteolin has the potential to treat viral pneumonia in multiple ways. Luteolin has a direct inhibitory effect on coronavirus, influenza virus, and respiratory syncytial virus. Luteolin can alleviate the inflammatory factor storm induced by multiple factors by inhibiting the function of macrophages or mast cells. Luteolin can reduce pulmonary inflammation, pulmonary edema, or pulmonary fibrosis induced by multiple factors. In addition, viral pneumonia may cause multisystem complications, while luteolin has extensive protective effects on the gastrointestinal system, cardiovascular system, and nervous system. However, due to the first-pass metabolism mediated by phase II enzymes, the bioavailability of oral luteolin is low. The bioavailability of luteolin can be improved, and its potential value can be further developed by changing the dosage form or route of administration.

Morphological appearance of the B.1.1.7 mutation of the novel coronavirus 2 (SARS-CoV-2) in chest CT

Background

Diagnosing a coronavirus disease 2019 (COVID-19) infection with high specificity in chest computed tomography (CT) imaging is considered possible due to distinctive imaging features of COVID-19 pneumonia. Since other viral non-COVID pneumonia show mostly a different distribution pattern, it is reasonable to assume that the patterns observed caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a consequence of its genetically encoded molecular properties when interacting with the respiratory tissue. As more mutations of the initial SARS-CoV-2 wild-type with varying aggressiveness have been detected in the course of 2021, it became obvious that its genome is in a state of transformation and therefore a potential modification of the specific morphological appearance in CT may occur. The aim of this study was to quantitatively analyze the morphological differences of the SARS-CoV-2-B.1.1.7 mutation and wildtype variant in CT scans of the thorax.

Methods

We analyzed a dataset of 140 patients, which was divided into pneumonias caused by n=40 wildtype variants, n=40 B.1.1.7 variants, n=20 bacterial pneumonias, n=20 viral (non-COVID) pneumonias, and a test group of n=20 unremarkable CT examinations of the thorax. Semiautomated 3D segmentation of the lung tissue was performed for quantification of lung pathologies. The extent, ratio, and specific distribution of inflammatory affected lung tissue in each group were compared in a multivariate group analysis.

Results

Lung segmentation revealed significant difference between the extent of ground glass opacities (GGO) or consolidation comparing SARS-CoV-2 wild-type and B.1.1.7 variant. Wildtype and B.1.1.7 variant showed both a symmetric distribution pattern of stage-dependent GGO and consolidation within matched COVID-19 stages. Viral non-COVID pneumonias had significantly fewer consolidations than the bacterial, but also than the COVID-19 B.1.1.7 variant groups.

Conclusions

CT based segmentation showed no significant difference between the morphological appearance of the COVID-19 wild-type variant and the SARS-CoV-2 B.1.1.7 mutation. However, our approach allowed a semiautomatic quantification of bacterial and viral lung pathologies. Quantitative CT image analyses, such as the one presented, appear to be an important component of pandemic preparedness considering an organism with ongoing genetic change, to describe a potential arising change in CT morphological appearance of possible new upcoming COVID-19 variants of concern.

An algorithm combining procalcitonin and lung ultrasound improves the diagnosis of bacterial pneumonia in critically ill children: The PROLUSP study, a randomized clinical trial

BACKGROUND

Lung ultrasound (LUS) and procalcitonin (PCT) are independently used to improve accuracy when diagnosing lung infections. The aim of the study was to evaluate the accuracy of a new algorithm combining LUS and PCT for the diagnosis of bacterial pneumonia.

METHODS

Randomized, blinded, comparative effectiveness clinical trial. Children <18 years old with suspected pneumonia admitted to pediatric intensive care unit were included, and randomized into experimental group (EG) or control group (CG) if LUS or chest X-Ray (CXR) were done as the first pulmonary image, respectively. PCT was determined. In patients with bacterial pneumonia, sensitivity, specificity, and predictive values of LUS, CXR, and of both combined with PCT were analyzed and compared. Concordance between the final diagnosis and the diagnosis concluded through the imaging test was assessed.

RESULTS

A total of 194 children, with a median age of 134 (interquartile range [IQR]: 39-554) days, were enrolled, 96 randomized into the EG and 98 into the CG. Bacterial pneumonia was diagnosed in 97 patients. Sensitivity and specificity for bacterial pneumonia diagnosis were 78% (95% confidence interval [CI]: 70-85) and 98% (95% CI: 93-99) for LUS, 85% (95% CI: 78-90) and 53% (95% CI: 43-62) for CXR, 90% (95% CI: 83-94) and 85% (95% CI: 76-91) when combining LUS and PCT, and 95% (95% CI: 90-98) and 41% (95% CI: 31-52) when combining CXR and PCT. The positive predictive value for LUS and PCT was 88% (95% C:I 79%-93%) versus 68% (95% CI: 60-75) for CXR and PCT. The concordance between the final diagnosis and LUS had a kappa value of 0.69 (95% CI: 0.62-0.75) versus 0.34 (95% CI: 0.21-0.45) for CXR, (p < 0.001).

CONCLUSIONS

The combination of LUS and PCT presented a better accuracy for bacterial pneumonia diagnosis than combining CXR and PCT. Therefore, its implementation could be a reliable tool for pneumonia diagnosis in critically ill children.

Combining initial chest CT with clinical variables in differentiating coronavirus disease 2019 (COVID-19) pneumonia from influenza pneumonia

Coronavirus disease 2019 (COVID-19) has spread in more than 100 countries and regions around the world, raising grave global concerns. COVID-19 has a similar pattern of infection, clinical symptoms, and chest imaging findings to influenza pneumonia. In this retrospective study, we analysed clinical and chest CT data of 24 patients with COVID-19 and 79 patients with influenza pneumonia. Univariate analysis demonstrated that the temperature, systolic pressure, cough and sputum production could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the clinical features are 0.783 and 0.747, and the AUC value is 0.819. Univariate analysis demonstrates that nine CT features, central-peripheral distribution, superior-inferior distribution, anterior-posterior distribution, patches of GGO, GGO nodule, vascular enlargement in GGO, air bronchogram, bronchiectasis within focus, interlobular septal thickening, could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the CT features are 0.750 and 0.962, and the AUC value is 0.927. Finally, a multivariate logistic regression model combined the variables from the clinical variables and CT features models was made. The combined model contained six features: systolic blood pressure, sputum production, vascular enlargement in the GGO, GGO nodule, central-peripheral distribution and bronchiectasis within focus. The diagnostic sensitivity and specificity for the combined features are 0.87 and 0.96, and the AUC value is 0.961. In conclusion, some CT features or clinical variables can differentiate COVID-19 from influenza pneumonia. Moreover, CT features combined with clinical variables had higher diagnostic performance.