Differential diagnosis of pulmonary infections in immunocompromised patients using high-resolution computed tomography

A combined model based on lung CT imaging and clinical characteristics for the diagnosis of AIDS with infection of Talaromyces marneffei

OBJECTIVE

To construct a combined model based on the CT and clinical characteristics of acquired immune deficiency syndrome (AIDS) with infection of Talaromyces marneffei (TM) and evaluate the diagnostic efficacy of the combined model.

METHODS

A retrospective analysis was conducted on 225 patients with AIDS patients with pulmonary infection admitted to the Shanghai Public Health Clinical Center from February 2012 to October 2022. Based on the final microbiological test results, they were divided into a group infected with TM and a group not infected with TM. Univariate and multivariate analyses were conducted on the lung CT imaging and clinical characteristics of these 225 patients. A nomogram was used to construct a combined model, and a calibration plot was drawn to test the diagnostic efficacy of the combined model.

RESULTS

Multivariate logistic regression analysis showed that fever, rash, elevated PLT, elevated Hb, CD4+T lymphocyte count < 50/ul, and lung CT histology features including diffuse punctate and nodular shadows, mass-like consolidation, consolidation shadows, single or multiple lung abscesses or cyst shadows, and pleural effusion were independent risk factors for the diagnosis of AIDS with TM infection (P < 0.05). The construction of a joint model using nomogram and calibration plot showed that the constructed joint model had a high degree of fit.

CONCLUSION

The combined model constructed in this study has certain clinical value for evaluating whether AIDS co-infection is caused by TM.

CLINICAL TRIAL

Not applicable.

Algorithmic Approach to an Abnormal Computed Tomography of the Chest in the Immunocompromised Host

The immunocompromised host is a patient who is at risk for life threatening complications. This article offers a structured approach to interpreting abnormal chest computed tomography (CT) scans in these patients. Immune defects are categorized as innate or adaptive and each is linked to specific infectious risks. CT scan findings are grouped into 5 categories: nodules and/or masses, consolidation or ground glass opacity, large airway abnormalities, pleural effusions, and lymphadenopathy. This algorithmic approach can guide clinicians in establishing a differential diagnosis for immunocompromised patients with abnormal chest CT scans and help them reach a faster and more accurate diagnosis.

How accurate is high-resolution computed tomography of the chest in differentiating between pulmonary invasive fungal infections and other pulmonary infections in children with cancer?

BACKGROUND

Pulmonary invasive fungal infections pose a serious risk for immunocompromised patients. Although diagnostic imaging plays an important role in the early detection of pulmonary invasive fungal infections, radiological differentiation between invasive fungal infection and other pulmonary infections is challenging.

OBJECTIVE

The aim of this study was to assess the accuracy of chest high-resolution computed tomography (HRCT) in the differentiation between pulmonary invasive fungal infections and other pulmonary infections in paediatric cancer patients.

MATERIALS AND METHODS

In this retrospective study, baseline HRCTs of patients with probable or proven invasive fungal infections and other pulmonary infections were blindly assessed by two radiologists, followed by a consensus reading. The scoring form included imaging characteristics and radiological invasive fungal infection probability assessment. Inter-rater reliability was determined with Cohen's kappa.

RESULTS

Chest HRCTs (n = 77) of paediatric cancer patients with pulmonary invasive fungal infections (n = 45) and with other pulmonary infections (n = 32) were evaluated. In the consensus reading, nodules with halo sign and wedge-shaped consolidations were observed significantly more in pulmonary invasive fungal infections than in other pulmonary infections (86.7% vs. 34.4% and 28.9% vs. 9.4%), and ground-glass opacities were observed less frequently (61.4% vs. 87.5%). The kappa values for the individual imaging characteristics ranged from 0.121 to 0.408. Sensitivity of the HRCT to diagnose a pulmonary invasive fungal infection ranged from 0.78 to 0.80, and specificity from 0.66 to 0.88.

CONCLUSION

The accuracy of chest HRCTs in differentiating between invasive fungal infections and other pulmonary infections is poor. There are two main reasons for this: no individual imaging characteristic was found to be able to fully distinguish between invasive fungal infections and other pulmonary infections, and the agreement between radiologists was only moderate.

Diagnostic Accuracy of High-Resolution Computed Tomography (HRCT) in Detecting Pneumocystis carinii in Renal Transplant Patients With Pulmonary Infection: A Study Using Bronchoalveolar Lavage As the Gold Standard

INTRODUCTION

Patients receiving renal transplants have weakened immune systems and are more vulnerable to lung infections.

OBJECTIVES

To determine the diagnostic accuracy of high-resolution computed tomography (HRCT) in detecting pneumocystis carinii in renal transplant patients presenting with pulmonary infection in a tertiary care transplant center, keeping bronchoalveolar lavage (BAL) as the gold standard.

METHODS

 This cross-sectional study was conducted at the Department of Radiology, Sindh Institute of Urology and Transplant, Karachi, from February 14, 2023, to August 13, 2023. Using a non-probability consecutive sampling technique, we enrolled 81 post-renal transplant patients, aged 20 to 60 years, of both genders, who were receiving immunosuppressive therapy and referred to the Radiology Department for HRCT as part of a workup for pulmonary infection. Patients presented with pulmonary infection underwent HRCT and BAL. Diagnostic accuracy was determined and data was analyzed.

RESULTS

In this study, 81 patients were enrolled with a mean age of 35.4±11.3 years. There were 56 (69.1%) male and 25 (30.9%) female patients. The mean duration of renal transplant was 18.09±12.8 months. The mean duration of symptoms was 5.32±2 days. Diabetes was present in 43 (53.1%) patients. The sensitivity of HRCT in diagnosing pneumocystis carinii was 93%, specificity was 91.7%, positive predictive value was 96.4%, negative predictive value was 84.6%, and diagnostic accuracy was 92.5%, taking BAL as the gold standard.

CONCLUSION

HRCT is a good imaging modality to diagnose pneumocystis carinii in renal transplant patients.

The Differential Diagnostic Value of Chest Computed Tomography for the Identification of Pathogens Causing Pulmonary Infections in Patients with Hematological Malignancies

Objective

The role of chest computed tomography (CT) in distinguishing the causative pathogens of pulmonary infections in patients with hematological malignancies (HM) is unclear. The aim of our study was to compare and assess the clinical characteristics, radiologic features and potential differential diagnostic value of CT in HM patients and other different immune statuses patients with pulmonary infections.

Methods

Patients were divided into immunocompetent (105 cases) and immunocompromised groups (99 cases) according to immune status. Immunocompromised patients included the HM group (63 cases) and the non-HM group (42 cases). The basic clinical data and CT findings were collected and statistically analyzed.

Results

Regarding the pathogen distribution, viral, Pneumocystis jirovecii and mixed infections were more common in the immunocompromised group than the immunocompetent (p < 0.01), but viral infections were more common in the HM group than in the non-HM group (p=0.013). Immunocompromised patients had more diverse CT findings and more serious lesions (mostly graded 2-4) than immunocompetent patients. The most common CT findings in HM patients were consolidation and ground-glass opacities (GGO), which were also found in the non-HM group. The overall diagnostic accuracy of CT was lower in immunocompromised patients than in immunocompetent patients (25.7% vs 50.5%, p< 0.01). CT had better diagnostic efficacy for fungi and Pneumocystis jirovecii in HM patients.

Conclusion

CT diagnosis is less efficient in distinguishing the causative pathogens of HM patients. However, CT can help distinguish fungal pneumonia and Pneumocystis jirovecii pneumonia in HM patients.

Clinical Relevance Statement

Our study might facilitate clinical decision-making in fungal pneumonia and Pneumocystis jirovecii pneumonia in HM patients.

How can we optimize the diagnostic and therapeutic approach to pneumonia? Expert opinion-based recommendations

Pneumonia continues to be one of the most frequent infectious syndromes and a relevant cause of death and health resources utilization. The OPENIN ("Optimización de procesos clínicos para el diagnóstico y tratamiento de infecciones") Group is composed of Infectious Diseases specialists and Microbiologists and aims at generating recommendations that can contribute to improve the approach to processes with high impact on the health system. Such task relies on a critical review of the available scientific evidence. The first Group meeting (held in October 2023) aimed at answering the following questions: Can we optimize the syndromic and microbiological diagnosis of pneumonia? Is it feasible to safely shorten the length of antibiotic therapy? And, is there any role for the immunomodulatory strategies based on the adjuvant use of steroids, macrolides or immunoglobulins? The present review summarizes the literature reviewed for that meeting and offers a series of expert recommendations.

Pharmacological effects and mechanism of Maxing Shigan decoction in the treatment of Pseudomonas aeruginosa pneumonia

ETHNOPHARMACOLOGICAL RELEVANCE

Maxing Shigan Decoction (MXSG) is a traditional Chinese Medicine effectively used in respiratory infections and bacterial pneumonia. However, the mechanism of MXSG treating acute Pseudomonas aeruginosa (P. aeruginosa) pneumonia is still unclear.

AIM OF THE STUDY

This study aimed to investigate the therapeutic effects of MXSG on acute P. aeruginosa pneumonia and explore its potential mechanisms.

MATERIALS AND METHODS

HPLC-MS analysis was performed to analyze the chemical composition. Antibacterial effects in vitro were evaluated by minimum inhibitory concentration (MIC). Forty-five male BALB/c mice were divided into control group, model group, levofloxacin group, MXSG-L (7.7 g/kg/d), and MXSG-H group (15.4 g/kg/d). Mice were intranasal instillation with P. aeruginosa to induce acute P. aeruginosa pneumonia model. Levofloxacin and MXSG were administered by oral gavage once a day. After 3 days of treatment, the lung index measurement, micro-CT, arterial blood gas analysis, bacteria load determination, and HE staining were performed. Network pharmacological analysis and transcriptome sequencing were employed to predict the potential mechanisms of MXSG on bacterial pneumonia. The expressions of relating genes were detected by immunofluorescence, Western blot, and RT-PCR.

RESULTS

In vitro, MIC of P. aeruginosa is greater than 500 mg/mL. In the treatment of acute P. aeruginosa pneumonia model, MXSG significantly improved body weight loss, lung index, and pulmonary lesions. MXSG treatment also reduced the bacterial load and ameliorated oxygen saturation significantly. Transcriptomes, immunofluorescence, Western blot, and RT-PCR analysis showed MXSG treating acute P. aeruginosa pneumonia through the IL-17 signaling pathway and HIF-1α/IL-6/STAT3 signaling pathway.

CONCLUSIONS

We demonstrated the efficacy and mechanism of MXSG in the treatment of acute P. aeruginosa pneumonia, which provides a scientific basis for its clinical application.

Pneumonia-Plus: a deep learning model for the classification of bacterial, fungal, and viral pneumonia based on CT tomography

OBJECTIVES

This study aims to develop a deep learning algorithm, Pneumonia-Plus, based on computed tomography (CT) images for accurate classification of bacterial, fungal, and viral pneumonia.

METHODS

A total of 2763 participants with chest CT images and definite pathogen diagnosis were included to train and validate an algorithm. Pneumonia-Plus was prospectively tested on a nonoverlapping dataset of 173 patients. The algorithm's performance in classifying three types of pneumonia was compared to that of three radiologists using the McNemar test to verify its clinical usefulness.

RESULTS

Among the 173 patients, area under the curve (AUC) values for viral, fungal, and bacterial pneumonia were 0.816, 0.715, and 0.934, respectively. Viral pneumonia was accurately classified with sensitivity, specificity, and accuracy of 0.847, 0.919, and 0.873. Three radiologists also showed good consistency with Pneumonia-Plus. The AUC values of bacterial, fungal, and viral pneumonia were 0.480, 0.541, and 0.580 (radiologist 1: 3-year experience); 0.637, 0.693, and 0.730 (radiologist 2: 7-year experience); and 0.734, 0.757, and 0.847 (radiologist 3: 12-year experience), respectively. The McNemar test results for sensitivity showed that the diagnostic performance of the algorithm was significantly better than that of radiologist 1 and radiologist 2 (p < 0.05) in differentiating bacterial and viral pneumonia. Radiologist 3 had a higher diagnostic accuracy than the algorithm.

CONCLUSIONS

The Pneumonia-Plus algorithm is used to differentiate between bacterial, fungal, and viral pneumonia, which has reached the level of an attending radiologist and reduce the risk of misdiagnosis. The Pneumonia-Plus is important for appropriate treatment and avoiding the use of unnecessary antibiotics, and provide timely information to guide clinical decision-making and improve patient outcomes.

CLINICAL RELEVANCE STATEMENT

Pneumonia-Plus algorithm could assist in the accurate classification of pneumonia based on CT images, which has great clinical value in avoiding the use of unnecessary antibiotics, and providing timely information to guide clinical decision-making and improve patient outcomes.

KEY POINTS

• The Pneumonia-Plus algorithm trained from data collected from multiple centers can accurately identify bacterial, fungal, and viral pneumonia. • The Pneumonia-Plus algorithm was found to have better sensitivity in classifying viral and bacterial pneumonia in comparison to radiologist 1 (5-year experience) and radiologist 2 (7-year experience). • The Pneumonia-Plus algorithm is used to differentiate between bacterial, fungal, and viral pneumonia, which has reached the level of an attending radiologist.

Ten Issues to Update in Nosocomial or Hospital-Acquired Pneumonia: An Expert Review

Nosocomial pneumonia, or hospital-acquired pneumonia (HAP), and ventilator-associated pneumonia (VAP) are important health problems worldwide, with both being associated with substantial morbidity and mortality. HAP is currently the main cause of death from nosocomial infection in critically ill patients. Although guidelines for the approach to this infection model are widely implemented in international health systems and clinical teams, information continually emerges that generates debate or requires updating in its management. This scientific manuscript, written by a multidisciplinary team of specialists, reviews the most important issues in the approach to this important infectious respiratory syndrome, and it updates various topics, such as a renewed etiological perspective for updating the use of new molecular platforms or imaging techniques, including the microbiological diagnostic stewardship in different clinical settings and using appropriate rapid techniques on invasive respiratory specimens. It also reviews both Intensive Care Unit admission criteria and those of clinical stability to discharge, as well as those of therapeutic failure and rescue treatment options. An update on antibiotic therapy in the context of bacterial multiresistance, in aerosol inhaled treatment options, oxygen therapy, or ventilatory support, is presented. It also analyzes the out-of-hospital management of nosocomial pneumonia requiring complete antibiotic therapy externally on an outpatient basis, as well as the main factors for readmission and an approach to management in the emergency department. Finally, the main strategies for prevention and prophylactic measures, many of them still controversial, on fragile and vulnerable hosts are reviewed.

Factors associated with pulmonary infection in kidney and kidney-pancreas transplant recipients: a case-control study

OBJECTIVE

To evaluate the etiology of and factors associated with pulmonary infection in kidney and kidney-pancreas transplant recipients.

METHODS

This was a single-center case-control study conducted between December of 2017 and March of 2020 at a referral center for kidney transplantation in the city of Belo Horizonte, Brazil. The case:control ratio was 1:1.8. Cases included kidney or kidney-pancreas transplant recipients hospitalized with pulmonary infection. Controls included kidney or kidney-pancreas transplant recipients without pulmonary infection and matched to cases for sex, age group, and donor type (living or deceased).

RESULTS

A total of 197 patients were included in the study. Of those, 70 were cases and 127 were controls. The mean age was 55 years (for cases) and 53 years (for controls), with a predominance of males. Corticosteroid use, bronchiectasis, and being overweight were associated with pulmonary infection risk in the multivariate logistic regression model. The most common etiologic agent of infection was cytomegalovirus (in 14.3% of the cases), followed by Mycobacterium tuberculosis (in 10%), Histoplasma capsulatum (in 7.1%), and Pseudomonas aeruginosa (in 7.1%).

CONCLUSIONS

Corticosteroid use, bronchiectasis, and being overweight appear to be risk factors for pulmonary infection in kidney/kidney-pancreas transplant recipients, endemic mycoses being prevalent in this population. Appropriate planning and follow-up play an important role in identifying kidney and kidney-pancreas transplant recipients at risk of pulmonary infection.

Clinical implications of Golgi protein 73 and granulocyte colony-stimulating factor and their related factors in children with bronchopneumonia

OBJECTIVE

To investigate the clinical implications of Golgi glycoprotein 73 (GP73) and granulocyte colony-stimulating factor (G-CSF) in children with bronchopneumonia (BP).

METHODS

Seventy-two children with BP (observation group) and 81 healthy children (control group) consecutively brought to the present study's hospital between June 2019 and October 2020 were enrolled. GP73 and G-CSF levels were determined to analyze their diagnostic value for pediatric BP. High-sensitivity C-reactive protein (hs-CRP) was also measured. The clinical implications of GP73 and G-CSF in pediatric BP complicated with respiratory failure and their connections with the inflammatory response were discussed.

RESULTS

GP73 and G-CSF levels were remarkably higher in the observation group (p < 0.05). The sensitivity and specificity of combined detection (GP73+G-CSF) in predicting pediatric BP were 72.22% and 86.42%, respectively (p < 0.001). GP73 and G-CSF, which are closely related to X-ray classification and complications in the observation group, decreased after treatment and were positively correlated with hs-CRP (p < 0.05), especially in children complicated with respiratory failure. Regression analysis identified the independence of the course of the disease, hs-CRP, X-ray classification, GP73, and G-CSF as influencing factors of respiratory failure in children with BP (p < 0.05).

CONCLUSION

GP73 and G-CSF, with elevated levels in children with BP, are strongly linked to disease progression and are independent influencing factors of respiratory failure, which may be the key to diagnosing and treating pediatric BP in the future.

High-resolution CT findings of pulmonary infections in patients with hematologic malignancy: comparison between patients with or without hematopoietic stem cell transplantation

Purpose

To evaluate the high-resolution CT (HRCT) findings of pulmonary infections in patients with hematologic malignancy and compare them between patients with or without hematopoietic stem cell transplantation (HSCT).

Materials and methods

A total of 128 patients with hematologic malignancy and pulmonary infection were included in this study. The diagnoses of the patients consisted of bacterial pneumonia (37 non-HSCT cases and 14 HSCT cases), pneumocystis pneumonia (PCP) (29 non-HSCT cases and 11 HSCT cases), and fungal infection other than PCP (20 non-HSCT cases and 17 HSCT cases). Two chest radiologists retrospectively evaluated the HRCT criteria and compared them using chi-squared tests and a multiple logistic regression analysis.

Results

According to the multiple logistic regression analysis, nodules were an indicator in HSCT patients with PCP (p = 0.025; odds ratio, 5.8; 95% confidence interval, 1.2–26.6). The centrilobular distribution of nodules was the most frequent (n = 4, 36%) in HSCT patients with PCP. A mosaic pattern was an indicator of PCP in both HSCT and non-HSCT patients. There were no significant differences in other infections.

Conclusion

The mosaic pattern could be an indicator of PCP in both HSCT and non-HSCT patients. Nodules with centrilobular distribution might be relatively frequent HRCT findings of PCP in HSCT patients.

Imaging Approach to Pulmonary Infections in the Immunocompromised Patient

Pulmonary infections are the major cause of morbidity and mortality in immunocompromised patients and almost one-third of intensive care unit patients with pulmonary infections belong to the immunocompromised category. Multiple organisms may simultaneously infect an immunocompromised patient and the overwhelming burden of mixed infections further predisposes critically ill patients to acute hypoxemic respiratory failure. Notwithstanding that lung ultrasound is coming into vogue, the primary imaging investigation is a chest radiograph, followed by thoracic CT scan. This review based on our experience at tertiary care teaching hospitals provides insights into the spectrum of imaging features of various pulmonary infections occurring in immunocompromised patients. This review is unique as, firstly, the imaging spectrum described by us is categorized on basis of the etiological infective agent, comprehensively and emphatically correlated with the clinical setting of the patient. Secondly, a characteristic imaging pattern is emphasized in the clinical setting-imaging-pattern conglomerate, to highlight the most likely diagnosis possible in such a combination. Thirdly, the simulating conditions for a relevant differential diagnosis are discussed in each section. Fourthly, not only are the specific diagnostic and tissue sampling techniques for confirmation of the suspected etiological agent described, but the recommended pharmaco-therapeutic agents are also enumerated, so as to provide a more robust insight to the radiologist. Last but not the least, we summarize and conclude with a diagnostic algorithm, derived by us from the characteristic illustrative cases. The proposed algorithm, illustrated as a flowchart, emphasizes a diagnostic imaging approach comprising: correlation of the imaging pattern with clinical setting and with associated abnormalities in the thorax and in other organs/systems, which is comprehensively analyzed in arriving at the most likely diagnosis. Since a rapid evaluation and emergent management of such patients is of pressing concern not only to the radiologist, but also for the general physicians, pulmonologists, critical care specialists, oncologists and transplant surgery teams, we believe our review is very informative to a wide spectrum reader audience.

Developing a pneumonia diagnosis ontology from multiple knowledge sources

Background: Pneumonia is difficult to differentiate from other pulmonary diseases because it shares many symptoms with these diseases. Diagnosing pneumonia in clinical practice would benefit from having access to a codified representation of clinical knowledge. An ontology represents a well-established paradigm for such codification. Objectives: The goal of this research is to create Pneumonia Diagnosis Ontology (PNADO) that brings together the medical knowledge dispersed among multiple medical knowledge sources. Material and Methods: We used several clinical practice guidelines (CPGs) describing the pneumonia diagnostic process as a starting point in developing PNADO. Preliminary version of PNADO was subsequently expanded to cover a broader range of the concepts by reusing ontologies from Open Biological and Biomedical Ontology (OBO) Foundry and BioPortal. PNADO was evaluated by examining relevant concepts from the pneumonia-specific systematic reviews, using patient data from the MIMIC-III clinical dataset, and by clinical domain experts. Results: PNADO is a comprehensive ontology and has a rich set of classes and properties that cover different types of pneumonia, pathogens, symptoms, clinical signs, laboratory tests and imaging, clinical findings, complications, and diagnoses. Conclusion: PNADO unifies pneumonia diagnostic concepts from multiple knowledge sources. It is available in the BioPortal repository.

Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT

BACKGROUND

Even though antibiotics agents are widely used, pneumonia is still one of the most common causes of death around the world. Some severe, fast-spreading pneumonia can even cause huge influence on global economy and life security. In order to give optimal medication regimens and prevent infectious pneumonia's spreading, recognition of pathogens is important.

METHOD

In this single-institution retrospective study, 2,353 patients with their CT volumes are included, each of whom was infected by one of 12 known kinds of pathogens. We propose Deep Diagnostic Agent Forest (DDAF) to recognize the pathogen of a patient based on ones' CT volume, which is a challenging multiclass classification problem, with large intraclass variations and small interclass variations and very imbalanced data.

RESULTS

The model achieves 0.899 ± 0.004 multi-way area under curves of receiver (AUC) for level-I pathogen recognition, which are five rough groups of pathogens, and 0.851 ± 0.003 AUC for level-II recognition, which are 12 fine-level pathogens. The model also outperforms the average result of seven human readers in level-I recognition and outperforms all readers in level-II recognition, who can only reach an average result of 7.71 ± 4.10% accuracy.

CONCLUSION

Deep learning model can help in recognition pathogens using CTs only, which might help accelerate the process of etiological diagnosis.

Effectiveness of Chest CT in Children: CT Findings in Relation to the Clinical Question

PURPOSE

 To estimate the effectiveness and efficiency of chest CT in children based on the suspected diagnosis in relation to the number of positive, negative, and inconclusive CT results.

MATERIALS AND METHODS

 In this monocentric retrospective study at a university hospital with a division of pediatric radiology, 2019 chest CT examinations (973 patients; median age: 10.5 years; range: 2 days to 17.9 years) were analyzed with regards to clinical data, including the referring department, primary questions or suspected diagnosis, and CT findings. It was identified if the clinical question was answered, whether the suspected diagnosis was confirmed or ruled out, and if additional findings (clinically significant or minor) were detected.

RESULTS

 The largest clinical subgroup was the hematooncological subgroup (n = 987), with frequent questions for inflammation/pneumonia (66 % in this subgroup). Overall, CT provided conclusive results in 97.6 % of all scans. In 1380 scans (70 %), the suspected diagnosis was confirmed. In 406/2019 cases (20 %), the CT scan was negative also in terms of an additional finding. In 8 of 9 clinical categories, the proportion of positive results was over 50 %. There were predominantly negative results (110/179; 61 %) in pre-stem cell transplant evaluation. In the subgroup of trauma management, 81/144 exams (57 %) showed positive results, including combined injuries (n = 23). 222/396 (56 %) of all additional findings were estimated to be clinically significant.

CONCLUSION

 In a specialized center, the effectiveness of pediatric chest CT was excellent when counting the conclusive results. However, to improve efficiency, the clinical evaluation before imaging appears crucial to prevent unnecessary CT examinations.

KEY POINTS

  · Pediatric chest CT in specialized centers has a high diagnostic value.. · CT identifies relevant changes besides the working hypothesis in clinically complex situations.. · Pre-CT clinical evaluation is crucial, especially in the context of suspected pneumonia..

CITATION FORMAT

· Esser M, Tsiflikas I, Kraus MS et al. Effectiveness of Chest CT in Children: CT Findings in Relation to the Clinical Question. Fortschr Röntgenstr 2021; DOI: 10.1055/a-1586-3023.

Differentiation of pulmonary complications with extensive ground-glass attenuation on high-resolution CT in immunocompromised patients

Purpose

The purpose of this study was to compare the high-resolution CT (HRCT) findings of pulmonary infectious and noninfectious complications with extensive ground-glass attenuation (GGA) in immunocompromised patients.

Materials and methods

One hundred fifty-two immunocompromised patients with pulmonary complications that showed extensive GGA (> 50% of the whole lung on HRCT) were included in this study. The diagnoses of the 152 patients were as follows: pneumocystis pneumonia (PCP), n = 82; drug-induced pneumonia, n = 38; bacterial pneumonia, n = 9; cytomegalovirus pneumonia, n = 6; idiopathic pneumonia syndrome, n = 6; diffuse alveolar hemorrhage (DAH), n = 4; fungal infection, n = 3; tuberculosis, n = 2 and pulmonary edema, n = 2. Two chest radiologists retrospectively evaluated the CT criteria, which consisted of 12 findings.

Results

The nodule (p = 0.015), the bronchovascular bundle (BVB) thickening (p = 0.001), and the interlobular septum (ILS) thickening (p = 0.002) were significantly infrequent in PCP. The ILS thickening was significantly frequent in drug-induced pneumonia (p < 0.001) though it was also frequent in other noninfectious and infectious diseases. The BVB thickening was significantly frequent in bacterial pneumonia (p = 0.005). The nodule was significantly frequent in DAH (p = 0.049).

Conclusion

Nodules, BVB thickening, and ILS thickening could be useful HRCT findings for the differential diagnosis of pulmonary complications in immunocompromised patients with extensive GGA.

Pro: Bronchoscopy is essential for pulmonary infections in patients with haematological malignancies

Up to 60% of patients with haematological malignancy will develop pulmonary infiltrates at some point in their disease course. Bronchoscopy should be used early in patients without respiratory failure as diagnostic yield is highest in the first 1–2 days of illness. Perceptions that patients with haematological malignancy are at higher risk of complications from bronchoscopy has led to a reluctance to perform the procedure. However, cohort studies have not demonstrated any increase in complications for this specific patient group. Common concerns include mucosal injury, respiratory impairment and haemorrhage. However, prospective cohort studies demonstrate that this patient group do not experience a higher than baseline level of complications. Specific pathogen diagnosis reduces morbidity and mortality in lung infection. Additionally, complex infections with multidrug-resistant organisms, the increasing prevalence of which is largely driven by empirical antibiotic use, make specific diagnosis more crucial than ever if we are to maintain our ability to manage myelosuppressive therapies and stem cell transplant.

Early bronchoscopy reduces morbidity and mortality in haematological malignancy patients with pulmonary infiltratehttps://bit.ly/302uqYv

Pneumocystis jirovecii Pneumonia in Children with Hematological Malignancies: Diagnosis and Approaches to Management

Pneumocystis jirovecii pneumonia (PJP) is an opportunistic infection that mostly affects children with suppressed cellular immunity. PJP was the most common cause of infectious death in children with acute lymphoblastic leukemia prior to the inclusion of cotrimoxazole prophylaxis as part of the standard medical care in the late 1980s. Children with acute leukemia, lymphomas, and those undergoing hematopoietic stem cell transplantation, especially allogeneic transplantation, are also at high risk of PJP. Persistent lymphopenia, graft versus host disease, poor immune reconstitution, and lengthy use of corticosteroids are significant risk factors for PJP. Active infection may be due to reactivation of latent infection or recent acquisition from environmental exposure. Intense hypoxemia and impaired diffusing capacity of the lungs are hallmarks of PJP, while computerized tomography of the lungs is the diagnostic technique of choice. Immunofluorescence testing with monoclonal antibodies followed by fluorescent microscopy and polymerase chain reaction testing of respiratory specimens have emerged as the best diagnostic methods. Measurement of (1-3)-β-D-glucan in the serum has a high negative predictive value in ruling out PJP. Oral cotrimoxazole is effective for prophylaxis, but in intolerant patients, intravenous and aerosolized pentamidine, dapsone, and atovaquone are effective alternatives. Ιntravenous cotrimoxazole is the treatment of choice, but PJP has a high mortality even with appropriate therapy.

Pre-Hematopoietic Stem Cell Transplantation Lung Computed Tomography as an Alternative to the Pulmonary Function Test during the COVID-19 Pandemic

•

The pulmonary function test (PFT) is an important test for risk stratification before allogeneic hematopoietic stem cell transplantation (allo-HCT), although it might be preferable to avoid unnecessary PFT during the COVID-19 pandemic.

•

Our computed tomography (CT)-based model for predicting normal PFT showed relatively high specificity (>80%).

•

PFT might be omitted in patients with normal CT findings before allo-HCT.

The pulmonary function test (PFT) is an important test for risk stratification before allogeneic transplantation (allo-HCT). However, it might be preferable to avoid PFT as much as possible in the recent era of coronavirus disease 2019 (COVID-19), because PFT requires forced expirations and might produce aerosols, increasing the risk of COVID-19 transmission. Therefore, we tried to predict normal PFT results before allo-HCT based on computed tomography (CT) findings. This study included 390 allo-HCT recipients at our center for whom lung CT images and PFT results before allo-HCT were available. Abnormal CT findings were less likely to be observed in the normal PFT group (47.0% versus 67.4%, P = .015), with a high negative predictive value of 92.9%. In a multivariate analysis, normal CT was significantly associated with normal PFT (odds ratio, 2.47; 95% confidence interval, 1.22 to 4.97; P = .012). A model for predicting normal PFT was constructed based on the results of a multivariate analysis, and the area under the curve of the receiver operating characteristic analysis was 0.656, which gave a sensitivity of 45.5% and a specificity of 86.0%. The relatively high specificity of the model suggested that PFT can be omitted in patients with normal CT findings before allo-HCT.

CT differential diagnosis of COVID-19 and non-COVID-19 in symptomatic suspects: a practical scoring method

BACKGROUND

Although typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis.

METHODS

Thirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19).

RESULTS

Older age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p < 0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score > 4, a sensitivity of 100% and a specificity of 23.26% for a score > 0, and a sensitivity of 86.67% and a specificity of 67.44% for a score >  2.

CONCLUSIONS

With a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions.

Chitotriosidase: a marker and modulator of lung disease

Chitotriosidase (CHIT1) is a highly conserved and regulated chitinase secreted by activated macrophages; it is a member of the 18-glycosylase family (GH18). CHIT1 is the most prominent chitinase in humans, can cleave chitin and participates in the body's immune response and is associated with inflammation, infection, tissue damage and remodelling processes. Recently, CHIT1 has been reported to be involved in the molecular pathogenesis of pulmonary fibrosis, bronchial asthma, COPD and pulmonary infections, shedding new light on the role of these proteins in lung pathophysiology. The potential roles of CHIT1 in lung diseases are reviewed in this article.

This is the first review of chitotriosidase in lung diseasehttp://bit.ly/2LpZUQI