Novel Patterns in High-Resolution Computed Tomography in Whipple Pneumonia.

With the use of metagenomic next-generation sequencing, patients diagnosed with Whipple pneumonia are being increasingly correctly diagnosed. We report a series of 3 cases in China that showed a novel pattern of movable infiltrates and upper lung micronodules. After treatment, the 3 patients recovered, and lung infiltrates resolved.

PulmoNet: a novel deep learning based pulmonary diseases detection model.

Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, making the exchange of gas challenging for animals inhaling and exhaling. It varies from gentle and self-limiting such as the common cold and catarrh, to life-threatening ones, such as viral pneumonia (VP), bacterial pneumonia (BP), and tuberculosis, as well as a severe acute respiratory syndrome, such as the coronavirus 2019 (COVID-19). The cost of diagnosis and treatment of pulmonary infections is on the high side, most especially in developing countries, and since radiography images (X-ray and computed tomography (CT) scan images) have proven beneficial in detecting various pulmonary infections, many machine learning (ML) models and image processing procedures have been utilized to identify these infections. The need for timely and accurate detection can be lifesaving, especially during a pandemic. This paper, therefore, suggested a deep convolutional neural network (DCNN) founded image detection model, optimized with image augmentation technique, to detect three (3) different pulmonary diseases (COVID-19, bacterial pneumonia, and viral pneumonia). The dataset containing four (4) different classes (healthy (10,325), COVID-19 (3,749), BP (883), and VP (1,478)) was utilized as training/testing data for the suggested model. The model's performance indicates high potential in detecting the three (3) classes of pulmonary diseases. The model recorded average detection accuracy of 94%, 95.4%, 99.4%, and 98.30%, and training/detection time of about 60/50 s. This result indicates the proficiency of the suggested approach when likened to the traditional texture descriptors technique of pulmonary disease recognition utilizing X-ray and CT scan images. This study introduces an innovative deep convolutional neural network model to enhance the detection of pulmonary diseases like COVID-19 and pneumonia using radiography. This model, notable for its accuracy and efficiency, promises significant advancements in medical diagnostics, particularly beneficial in developing countries due to its potential to surpass traditional diagnostic methods.

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.

A CT-based nomogram for differentiating invasive fungal disease of the lung from bacterial pneumonia.

BACKGROUND: There is an annual increase in the incidence of invasive fungal disease (IFD) of the lung worldwide, but it is always a challenge for physicians to make an early diagnosis of IFD of the lung. Computed tomography (CT) may play a certain role in the diagnosis of IFD of the lung, however, there are no specific imaging signs for differentiating IFD of lung from bacterial pneumonia (BP). METHODS: A total of 214 patients with IFD of the lung or clinically confirmed BP were retrospectively enrolled from two institutions (171 patients from one institution in the training set and 43 patients from another institution in the test set). The features of thoracic CT images of the 214 patients were analyzed on the picture archiving and communication system by two radiologists, and these CT images were imported into RadCloud to perform radiomics analysis. A clinical model from radiologic analysis, a radiomics model from radiomics analysis and a combined model from integrating radiologic and radiomics analysis were constructed in the training set, and a nomogram based on the combined model was further developed. The area under the ROC curve (AUC) of the receiver operating characteristic (ROC) curve was calculated to assess the diagnostic performance of the three models. Decision curve analysis (DCA) was conducted to evaluate the clinical utility of the three models by estimating the net benefit at a range of threshold probabilities. RESULTS: The AUCs of the clinical model for differentiating IFD of lung from BP in the training set and test sets were 0.820 and 0.827. The AUCs of the radiomics model in the training set and test sets were 0.895 and 0.857. The AUCs of the combined model in the training set and test setswere 0.944 and 0.911. The combined model for differentiating IFD of lung from BP obtained the greatest net benefit among the three models by DCA. CONCLUSION: Our proposed nomogram, based on a combined model integrating radiologic and radiomics analysis, has a powerful predictive capability for differentiating IFD from BP. A good clinical outcome could be obtained using our nomogram.

Complicated pneumonia in children.

Complicated community-acquired pneumonia in a previously well child is a severe illness characterised by combinations of local complications (eg, parapneumonic effusion, empyema, necrotising pneumonia, and lung abscess) and systemic complications (eg, bacteraemia, metastatic infection, multiorgan failure, acute respiratory distress syndrome, disseminated intravascular coagulation, and, rarely, death). Complicated community-acquired pneumonia should be suspected in any child with pneumonia not responding to appropriate antibiotic treatment within 48-72 h. Common causative organisms are Streptococcus pneumoniae and Staphylococcus aureus. Patients have initial imaging with chest radiography and ultrasound, which can also be used to assess the lung parenchyma, to identify pleural fluid; CT scanning is not usually indicated. Complicated pneumonia is treated with a prolonged course of intravenous antibiotics, and then oral antibiotics. The initial choice of antibiotic is guided by local microbiological knowledge and by subsequent positive cultures and molecular testing, including on pleural fluid if a drainage procedure is done. Information from pleural space imaging and drainage should guide the decision on whether to administer intrapleural fibrinolytics. Most patients are treated by drainage and more extensive surgery is rarely needed; in any event, in low-income and middle-income countries, resources for extensive surgeries are scarce. The clinical course of complicated community-acquired pneumonia can be prolonged, especially when patients have necrotising pneumonia, but complete recovery is the usual outcome.

Septic shock due to Yersinia pseudotuberculosis infection in an adult immunocompetent patient: a case report and literature review.

BACKGROUND: Yersinia pseudotuberculosis infection can occur in an immunocompromised host. Although rare, bacteremia due to Y. pseudotuberculosis may also occur in immunocompetent hosts. The prognosis and therapeutic strategy, especially for immunocompetent patients with Y. pseudotuberculosis bacteremia, however, remains unknown. CASE PRESENTATION: A 38-year-old Japanese man with a mood disorder presented to our hospital with fever and diarrhea. Chest computed tomography revealed consolidation in the right upper lobe with air bronchograms. He was diagnosed with pneumonia, and treatment with intravenous ceftriaxone and azithromycin was initiated. The ceftriaxone was replaced with doripenem and the azithromycin was discontinued following the detection of Gram-negative rod bacteria in 2 sets of blood culture tests. The isolated Gram-negative rod bacteria were confirmed to be Y. pseudotuberculosis. Thereafter, he developed septic shock. Doripenem was switched to cefmetazole, which was continued for 14 days. He recovered without relapse. CONCLUSIONS: We herein report a case of septic shock due to Y. pseudotuberculosis infection in an adult immunocompetent patient. The appropriate microorganism tests and antibiotic therapy are necessary to treat patients with Y. pseudotuberculosis bacteremia.

Young patient with pneumonia complicated by bronchopleural fistula.

We present the case of a 19 year old female presenting to the Emergency Department with signs of pneumonia and sepsis, with her clinical status deteriorating rapidly to septic shock and respiratory failure. Her pneumonia was complicated by formation of an empyema and a bronchopleural fistula. Bronchopleural fistula (BPF) is a fistula between pleural space and a bronchus. It is an uncommon complication of lung surgery, endobronchial interventions or chest trauma. They are sometimes formed secondary to postoperative pneumonia. Management of BPF requires surgical or bronchoscopic intervention with supportive care. Since a BPF can cause physiological tension pneumothorax, it can lead to significant worsening of respiratory status of these patients. Ventilator settings need to be adjusted to reduce the Positive end expiratory pressure and tidal volume to support these patients. With this case we highlight the importance of recognizing and diagnosing a BPF and timely management of a BPF in the emergency setting to help patients get to the definitive treatment of the fistula.

Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective.

BACKGROUND: It is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition. METHODS: This is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The Lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups. Each lesion patch cluster was described by a characteristic imaging term for comparison. For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity. RESULTS: The 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters. CONCLUSION: Deep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showed correlations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.

Can lung ultrasound differentiate between bacterial and viral pneumonia in children?

PURPOSE: This study evaluates whether LUS can differentiate between bacterial and viral pneumonia in children and thus affect their management. METHODS: The prospective, cross-sectional, analytical study included 200 children under 12 years of age (excluding neonates) with clinical suspicion of pneumonia who had undergone a chest radiograph (CR). The CR and LUS findings were classified as bacterial or viral pneumonia. The final diagnosis was made on the basis of a combination of clinical profile, available routine laboratory investigations and CR diagnosis which was taken as the gold standard for the study and LUS was compared with the gold standard. RESULTS: LUS has a high sensitivity (91%; 95% CI [84-96]) and specificity (91.3%; 95% CI [84-96]) in diagnosing bacterial pneumonia with a high positive predictive value (91.9%; 95% CI [85-96]) and negative predictive value (90.3%; 95% CI [82-95]). For diagnosing viral pneumonia, the sensitivity of LUS was 78.4%; (95% CI [68-86]), specificity was high (90.4%; 95% CI [83-95]) and so was the positive predictive value (87.3%; 95% CI [78-94]) and negative predictive value (91.3%; 95% CI [84-96]). CONCLUSION: LUS has a high accuracy in differentiating between bacterial and viral pneumonia in children and can help in their management by avoiding an ill-advised use of antibiotic therapy.

A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

Procalcitonin and lung ultrasound algorithm to diagnose severe pneumonia in critical paediatric patients (PROLUSP study). A randomised clinical trial.

BACKGROUND: Lung ultrasound (LUS) in combination with a biomarker has not yet been studied. We propose a clinical trial where the primary aims are: 1. To assess whether an algorithm with LUS and procalcitonin (PCT) may be useful for diagnosing bacterial pneumonia; 2. To analyse the sensitivity and specificity of LUS vs chest X-ray (CXR). METHODS/DESIGN: A 3-year clinical trial. INCLUSION CRITERIA: children younger than 18 years old with suspected pneumonia in a Paediatric Intensive Care Unit. Patients will be randomised into two groups: Experimental Group: LUS will be performed as first lung image. CONTROL GROUP: CXR will be performed as first pulmonary image. Patients will be classified according to the image and the PCT: a) PCT < 1 ng/mL and LUS/CXR are not suggestive of bacterial pneumonia (BN), no antibiotic will be prescribed; b) LUS/CXR are suggestive of BN, regardless of the PCT, antibiotic therapy is recommended; c) LUS/CXR is not suggestive of BN and PCT > 1 ng/mL, antibiotic therapy is recommended. CONCLUSION: This algorithm will help us to diagnose bacterial pneumonia and to prescribe the correct antibiotic treatment. A reduction of antibiotics per patient, of the treatment length, and of the exposure to ionizing radiation and in costs is expected. TRIAL REGISTRATION: NCT04217980 .

Imaging findings of Stenotrophomonas maltophilia pneumonia: emphasis on CT findings between immunocompromised and immunocompetent patients.

BACKGROUND: Stenotrophomonas maltophilia (S. maltophilia) is a globally emerging, rare, waterborne, aerobic, gram-negative, multiple-drug-resistant organism, most commonly associated with respiratory tract infection in humans. Computed tomography (CT) findings in patients with S. maltophilia pneumonia are rarely reported. PURPOSE: To compare CT findings between immunocompromised and immunocompetent patients, and to determine characteristic imaging findings of S. maltophilia pneumonia. MATERIAL AND METHODS: CT findings of eight immunocompromised and 29 immunocompetent patients with proven S. maltophilia pneumonia were reviewed retrospectively. Different patterns of CT abnormalities between immunocompromised and immunocompetent patients were compared by Fisher's exact test. RESULTS: Patchy ground-glass opacities (GGOs) were the most common CT findings, present in 36 (97.3%) of the 37 patients. Among the patients with patchy GGOs, consolidation was seen in 29 (78.4%) patients, and centrilobular nodules were noted in 15 (40.5%) patients. The transaxial distribution of the parenchymal abnormalities was predominantly randomly distributed in 30 (81.1%) cases. Regarding longitudinal plane involvement, the predominant zonal distributions were the diffuse distribution (n=23, 62.2%) and the lower lung zone (n=14, 37.8%). None of the patients showed upper lung zone predominance. The proportion of patients with parenchymal CT findings or associated findings in the immunocompromised patients was not significantly different from that of the immunocompetent patients. However, lower lung zone predominance on the longitudinal plane was significantly more common in immunocompetent patients than in immunocompromised patients (14/29 vs. 0/8, P=0.015). And diffuse distribution of parenchymal abnormalities on a longitudinal plane was significantly more frequent in immunocompromised patients than in immunocompetent patients (8/8 vs. 15/29, P=0.015). CONCLUSION: The most common CT patterns of S. maltophilia pneumonia in immunocompromised and immunocompetent patients were patchy GGOs and consolidation. However, in immunocompetent patients, parenchymal abnormalities were more predominately distributed in lower lung zone than in immunocompromised patients; and in immunocompromised patients, parenchymal abnormalities were more diffusely distributed than in immunocompetent patients.

A New Model of Chronic Mycobacterium abscessus Lung Infection in Immunocompetent Mice.

Pulmonary infections caused by Mycobacterium abscessus (MA) have increased over recent decades, affecting individuals with underlying pathologies such as chronic obstructive pulmonary disease, bronchiectasis and, especially, cystic fibrosis. The lack of a representative and standardized model of chronic infection in mice has limited steps forward in the field of MA pulmonary infection. To overcome this challenge, we refined the method of agar beads to establish MA chronic infection in immunocompetent mice. We evaluated bacterial count, lung pathology and markers of inflammation and we performed longitudinal studies with magnetic resonance imaging (MRI) up to three months after MA infection. In this model, MA was able to establish a persistent lung infection for up to two months and with minimal systemic spread. Lung histopathological analysis revealed granulomatous inflammation around bronchi characterized by the presence of lymphocytes, aggregates of vacuolated histiocytes and a few neutrophils, mimicking the damage observed in humans. Furthermore, MA lung lesions were successfully monitored for the first time by MRI. The availability of this murine model and the introduction of the successfully longitudinal monitoring of the murine lung lesions with MRI pave the way for further investigations on the impact of MA pathogenesis and the efficacy of novel treatments.

Risk factors and pathogenic microorganism characteristics for pneumonia in convalescent patients with stroke: A retrospective study of 380 patients from a rehabilitation hospital.

BACKGROUND: Pneumonia is a major complication leading to death after stroke. The risk factors of pneumonia in convalescent patients who have experienced stroke remain poorly defined. METHODS: To identify the risk factors of pneumonia, we applied logistic regression as a statistical method using SPSS23.0 statistical software, based on a sample of 380 patients. And statistical description method was used to analyze pathogens' characteristics and drug resistance. RESULTS: Ultimately, the obtained logistic model has statistical significance (χ2(13) = 91.560, P <0.0005). The sensitivity of the model is 41.7%, the specificity is 97.6%, the positive predictive value is 76.9%, and the negative predictive value is 89.8%. The Barthel index (BI) (OR=1.97, 95% CI: 1.01-3.87), basic lung diseases (OR=4.24, 95% CI: 1.02-17.61), trachea ventilation (OR=6.56, 95% CI: 1.18-36.34), feeding tube (OR=6.06, 95% CI: 2.59-14.18), and hypoproteinemia (OR=3.97, 95% CI: 1.56-10.10) were statistically significant (P<0.05). Among patients who have pneumonia, the proportion of gram-positive bacteria, gram-negative bacteria and fungal infection is 10.00%, 54.29%, 5.71% respectively. The study most frequently isolated Pseudomonas aeruginosa (18.57%), followed by Acinetobacter baumannii (10.00%,) and Klebsiella pneumoniae (10.00%). The drug resistance rate of Pseudomonas aeruginosa, Acinetobacter baumannii and Klebsiella pneumoniae to different antibiotics ranged from 0.00-37.77%, 0.00-85.71% and 0.00-57.14%, respectively. CONCLUSIONS: The lower BI scores, basic lung diseases, trachea ventilation, tube feeding, and hypoproteinemia are independent risk factors of pneumonia among convalescent patients with stroke. The main pathogens that caused pneumonia were gram-negative bacteria, and such organisms have different degrees of resistance to drugs.

Pneumonia due to Pandoraea Apista after evacuation of traumatic intracranial hematomas:a case report and literature review.

BACKGROUND: Pandoraea species is a newly described genus, which is multidrug resistant and difficult to identify. Clinical isolates are mostly cultured from cystic fibrosis (CF) patients. CF is a rare disease in China, which makes Pandoraea a total stranger to Chinese physicians. Pandoraea genus is reported as an emerging pathogen in CF patients in most cases. However, there are few pieces of evidence that confirm Pandoraea can be more virulent in non-CF patients. The pathogenicity of Pandoraea genus is poorly understood, as well as its treatment. The incidence of Pandoraea induced infection in non-CF patients may be underestimated and it's important to identify and understand these organisms. CASE PRESENTATION: We report a 44-years-old man who suffered from pneumonia and died eventually. Before his condition deteriorated, a Gram-negative bacilli was cultured from his sputum and identified as Pandoraea Apista by matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS). CONCLUSION: Pandoraea spp. is an emerging opportunistic pathogen. The incidences of Pandoraea related infection in non-CF patients may be underestimated due to the difficulty of identification. All strains of Pandoraea show multi-drug resistance and highly variable susceptibility. To better treatment, species-level identification and antibiotic susceptibility test are necessary.

Fatal community-acquired Bacillus cereus pneumonia in an immunocompetent adult man: a case report.

BACKGROUND: Bacillus cereus is a gram-positive rod bacterium that is responsible for food poisoning. It is naturally widely distributed, and thus often contaminates cultures. Although it is rarely considered responsible, it can cause serious infections under certain conditions. However, lethal infections, especially in immunocompetent patients, are rare. CASE PRESENTATION: A healthy 60-year-old man developed community-acquired B. cereus pneumonia and alveolar hemorrhage unveiled by abrupt chest pain and hemoptysis with no other advance symptoms. B. cereus induced silent alveolar destruction without any local or systemic inflammatory response. Although the lesion resembled lung anthrax, there was no evidence of Bacillus anthracis toxin. CONCLUSIONS: Some isolates of B. cereus can cause anthrax-like fulminant necrotizing pneumonia in immunocompetent patients. If this type of B. cereus were used as a means of bioterrorism, it may be quite difficult to recognize as bioterrorism. We should keep B. cereus in mind as a potential pathogen of fulminant human infectious disease.

A case of pan-resistant Burkholderia cepacia complex bacteremic pneumonia, after lung transplantation treated with a targeted combination therapy.

We describe a case of one patient with cystic fibrosis who developed a pan-resistant Burkholderia cepacia complex rapidly progressive bacteraemic pneunonia, following bilateral lung transplantation. The patient was treated with a targeted combination antibiotic therapy (meropenem plus ceftazidime/avibactam plus high doses of nebulized colistimethate sodium). Evolution of the disease was complicated by multiple organ system dysfunction. Finally, clinical improvement and microbiological cure was achieved.

Lung ultrasound in children with pneumonia: interoperator agreement on specific thoracic regions.

The objective of this study was to evaluate the interoperator agreement of lung ultrasonography (LUS) on specific thoracic regions in children diagnosed with pneumonia and to compare the findings of the LUS with the chest X-ray. Participants admitted to the ward or PICU underwent LUS examinations performed by an expert and a novice operator. A total of 261 thoracic regions in 23 patients were evaluated. Median age and weight of participants were 30 months and 11.6 kg, respectively. A substantial overall agreement between operators was found for normal lung tissue (κ = 0.615, 95% confidence interval (95% CI) = 0.516-0.715) and for consolidations (κ = 0.635, 95% CI = 0.532-0.738). For B-lines, a moderate agreement was observed (κ = 0.573, 95% CI = 0.475-0.671). An almost perfect agreement was found for pleural effusion (κ = 0.868, 95% CI = 0.754-0.982). The diagnosis of consolidations by LUS showed a high sensitivity (93% for both operators) but a low specificity (14% for expert and 25% for novice operator). While intubated patients presented significantly more consolidations, nonintubated patients presented more normal ultrasound patterns.Conclusion: Even when performed by operators with very distinct degrees of experience, LUS had a good interoperator reliability for detecting sonographic patterns on specific thoracic regions. What is Known: • Lung ultrasound is feasible, safe, and highly accurate for the diagnosis of pneumonia in children; however, it does not allow global visualization of the thorax in a single moment as in chest X-rays, and, similar to the stethoscope, partial thorax assessments must be performed sequentially. What is New: • This is the first study evaluating the agreement of LUS on specific thoracic regions between operators with distinct degrees of experience performing the sonograms. • There is a good agreement between an expert operator and a novice operator who underwent a brief theoretical-practical training program on LUS.

Scan the lung: Point-of-care ultrasound of a pulmonary consolidation with loculated pleural effusion.

Thoracic ultrasound has become an increasingly valuable tool in the evaluation of critically ill patients in the emergency department (ED). The utility of point-of-care ultrasound (POCUS) to identify suspected pneumothorax, pulmonary edema, pleural effusion and pneumonia has been well established (Pagano et al.; Brogi et al.; Cortellaro et al.; Irwin and Cook [1-4]). The 2014 American College of Emergency Physicians (ACEP) Ultrasound Imaging Compendium included lung and pleural ultrasound with the primary indication of identifying pneumothorax and pleural effusion as part of the core POCUS indications for all emergency physicians [5]. We present a unique case in which a patient presented to the ED in respiratory distress. Portable chest X-ray demonstrated near complete opacification of his right hemithorax. POCUS demonstrated a large right sided loculated pleural effusion with associated septations and surrounding consolidation suggestive of a parapneumonic effusion.

Procalcitonin Levels in Critically Ill Children With Status Asthmaticus.

BACKGROUND: Children with status asthmaticus (SA) often present with fever and are evaluated with chest radiographs (CXRs). In the absence of a confirmatory test for bacterial infection, antibiotics are started whenever there are radiological infiltrates or if there is a suspicion of pneumonia. We undertook this study to determine if serum procalcitonin (PCT) levels at admission are altered in critically ill children with SA. We also sought to determine if serum PCT levels are elevated in children with radiological infiltrates or in children who were treated with antibiotics. METHODS: This is a prospective single-center observational study evaluating serum PCT levels in critically ill children with SA. Study subjects included children 1 to 21 years old, admitted to a pediatric intensive care unit (PICU) with SA between March 2012 and April 2013. For the purposes of this study, patients whose CXRs were read by the radiologist as probable bacterial pneumonia was defined as having "radiological bacterial pneumonia," whereas patients who received antibiotics by the treating physician were defined as having "clinician-diagnosed pneumonia." RESULTS: Sixty-one patients with a median age of 7.3 years (interquartile range, 4-10 years) were included in the study. Fifty-one percent were male. Average Pediatric Risk of Mortality III score was 2.7 (SD, 2.9). Three patients (5%) were determined to have radiological bacterial pneumonia, whereas 52 (85%) did not. Six patients (10%) were indeterminate. The mean PCT level for all patients was 0.65 (SD, 1.54) ng/mL, whereas the median PCT level was 0.3 ng/mL. There was no significant difference in the mean PCT levels between the patients with and without clinician-diagnosed pneumonia (0.33 [SD, 0.36] vs 0.69 [SD, 1.67], P = 0.44). Using a PCT cutoff level of 0.5 ng/mL, a significant association was found with the presence of fever (P = 0.004), but no significant association was found with the presence of CXR infiltrates, radiological bacterial pneumonia, hospital length of stay, PICU length of stay, Pediatric Risk of Mortality III scores, or receipt of antibiotics. CONCLUSIONS: Serum PCT level was not elevated to greater than 0.5 ng/mL in 75% of this cohort of critically ill children with SA admitted to PICU. Presence of CXR infiltrates was not associated with higher PCT levels. Large clinical trials are needed to study the diagnostic and predictive role of PCT in this patient population.