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.

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.

Radiographic findings of adenoviral pneumonia in children.

OBJECTIVE: Adenovirus pneumonia is a common cause of community-acquired pneumonia in children and can mimic bacterial pneumonia, but there are few publications on its radiographic features. This study has evaluated the chest radiography findings of community-acquired adenovirus pneumonia in children. The frequency of radiological findings mimicking bacterial pneumonia was investigated. The clinical features of patients with adenovirus pneumonia possessing radiological findings mimicking bacterial pneumonia were also evaluated. MATERIALS AND METHODS: The chest radiographs of patients diagnosed with adenovirus pneumonia were retrospectively reviewed. The chest radiographs were interpreted independently by a pediatric infectious disease specialist and a pediatric radiologist. Chest radiography findings mimicking bacterial pneumonia (bacterial-like) were specified as consolidation +/- pleural effusion. Other findings on chest radiography or a completely normal chest X-ray were specified as findings that were compatible with "typical viral pneumonia". RESULTS: A total of 1407 patients were positive for adenovirus with respiratory multiplex PCR. The 219 patients who met the study criteria were included in the study. Chest radiographs were normal in 58 (26.5 %) patients. The chest radiograph findings mimicked bacterial pneumonia in 41 (18.7 %) patients. CONCLUSION: Adenovirus pneumonia occurs predominantly in children aged five years and younger, as with other viral pneumonias. The radiographic findings in adenovirus pneumonia are predominantly those seen in viral pneumonia. Increasing age and positivity for only adenovirus without other viruses on respiratory multiplex PCR were associated with the chest radiograph being more likely to be "bacterial-like". Adenovirus may lead to lobar/segmental consolidation at a rate that is not very rare.

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.

The potential clinical value of pairing procalcitonin and lung ultrasonography to guide antibiotic therapy in patients with community-acquired pneumonia: a narrative review.

INTRODUCTION: Lower respiratory tract infections (LRTIs) are among the most frequent infections and are prone to inappropriate antibiotic treatments. This results from a limited accuracy of diagnostic tools in identifying bacterial pneumonia. Lung ultrasound (LUS) has excellent sensitivity and specificity in diagnosing pneumonia. Additionally, elevated procalcitonin (PCT) levels correlate with an increased likelihood of bacterial infection. LUS and PCT appear to be complementary in identifying patients with bacterial pneumonia who are likely to benefit from antibiotics. AREAS COVERED: This narrative review aims to summarize the current evidence for LUS to diagnose pneumonia, for PCT to guide antibiotic therapy and the clinical value of pairing both tools. EXPERT OPINION: LUS has excellent diagnostic accuracy for pneumonia in different settings, regardless of the examiner's experience. PCT guidance safely reduces antibiotic prescription in LRTIs. The combination of both tools has demonstrated an enhanced accuracy in the diagnosis of pneumonia, including CAP in the ED and VAP in the ICU, but randomized controlled studies need to validate the clinical impact of a combined approach.

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.

[Analysis of chest imaging features of novel coronavirus pneumonia, bacterial pneumonia and viral pneumonia].

OBJECTIVE: To investigate and summarize the chest CT imaging features of patients with novel coronavirus pneumonia (COVID-19), bacterial pneumonia and other viral pneumonia. METHODS: Chest CT data of 102 patients with pulmonary infection due to different etiologies were retrospectively analyzed, including 36 patients with COVID-19 admitted to Hainan Provincial People's Hospital and the Second Affiliated Hospital of Hainan Medical University from December 2019 to March 2020, 16 patients with other viral pneumonia admitted to Hainan Provincial People's Hospital from January 2018 to February 2020, and 50 patients with bacterial pneumonia admitted to Haikou Affiliated Hospital of Central South University Xiangya School of Medicine from April 2018 to May 2020. Two senior radiologists and two senior intensive care physicians were participated to evaluated the extent of lesions involvement and imaging features of the first chest CT after the onset of the disease. RESULTS: Bilateral pulmonary lesions were more common in patients with COVID-19 and other viral pneumonia, and the incidence was significantly higher than that of bacterial pneumonia (91.6%, 75.0% vs. 26.0%, P < 0.05). Compared with other viral pneumonia and COVID-19, bacterial pneumonia was mainly characterized by single-lung and multi-lobed lesion (62.0% vs. 18.8%, 5.6%, P < 0.05), accompanied by pleural effusion and lymph node enlargement. The proportion of ground-glass opacity in the lung tissues of patients with COVID-19 was 97.2%, that of patients with other viral pneumonia was 56.2%, and that of patients with bacterial pneumonia was only 2.0% (P < 0.05). The incidence rate of lung tissue consolidation (25.0%, 12.5%), air bronchial sign (13.9%, 6.2%) and pleural effusion (16.7%, 37.5%) in patients with COVID-19 and other viral pneumonia were significantly lower than those in patients with bacterial pneumonia (62.0%, 32.0%, 60.0%, all P < 0.05), paving stone sign (22.2%, 37.5%), fine mesh sign (38.9%, 31.2%), halo sign (11.1%, 25.0%), ground-glass opacity with interlobular septal thickening (30.6%, 37.5%), bilateral patchy pattern/rope shadow (80.6%, 50.0%) etc. were significantly higher than those of bacterial pneumonia (2.0%, 4.0%, 2.0%, 0%, 22.0%, all P < 0.05). The incidence of local patchy shadow in patients with COVID-19 was only 8.3%, significantly lower than that in patients with other viral pneumonia and bacterial pneumonia (8.3% vs. 68.8%, 50.0%, P < 0.05). There was no significant difference in the incidence of peripheral vascular shadow thickening in patients with COVID-19, other viral pneumonia and bacterial pneumonia (27.8%, 12.5%, 30.0%, P > 0.05). CONCLUSIONS: The probability of ground-glass opacity, paving stone and grid shadow in chest CT of patients with COVID-19 was significantly higher than those of bacterial pneumonia, and it was more common in the lower lungs and lateral dorsal segment. In other patients with viral pneumonia, ground-glass opacity was distributed in both upper and lower lungs. Bacterial pneumonia is usually characterized by single lung consolidation, distributed in lobules or large lobes and accompanied by pleural effusion.

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.

Impact of lung ultrasound during the SARS-CoV-2 pandemic: Distinction between viral and bacterial pneumonia.

INTRODUCTION: Lung Ultrasound is an accessible, low-cost technique that has demonstrated its usefulness in the prognostic stratification of COVID-19 patients. In addition, according to previous studies, it can guide us towards the potential aetiology, especially in epidemic situations such as the current one. PATIENTS AND METHODS: 40 patients were prospectively recruited, 30 with confirmed SARS-CoV-2 pneumonia and 10 with community-acquired pneumonia (CAP). The patients included underwent both a chest X-ray and ultrasound. RESULTS: There were no differences in the 2 groups in terms of clinical and laboratory characteristics. The main ultrasound findings in the SARS-CoV-2 group were the presence of confluent B lines and subpleural consolidations and hepatinization in the CAP group. Pleural effusion was more frequent in the CAP group. There were no normal lung ultrasound exams. Analysis of the area under the curve (AUC) curves showed an area under the curve for Lung Ultrasound of 89.2% (95% CI: 75%.0-100%, p < .001) in the identification of SARS-CoV-2 pneumonia. The cut-off value for the lung score of 10 had a sensitivity of 93.3% and a specificity of 80.0% (p < .001). DISCUSSION: The combination of the findings of the Lung Ultrasound, with a Lung Score greater than 10, added to the rest of the additional tests, can be an excellent tool to predict the aetiology of the pneumonia.

Outcome After Lung Transplantation From a Donor With Bacterial Pneumonia Under the Japanese Donor Evaluation System.

BACKGROUND: In Japan, a unique medical consultant system for donor evaluation and management has been developed in an effort to maximize the use of extended criteria donor lungs. The aim of this study was to investigate the impact of donor pneumonia (DP) on the outcome after lung transplantation under this system. MATERIALS AND METHODS: Clinical data of 85 patients who underwent deceased donor lung transplantation (41 single and 44 bilateral lung transplants) between August 2012 and March 2018 were reviewed. DP was defined as the recognition of pneumonia on imaging with positive bacterial culture in the airway at the time of transplantation. RESULTS: Twenty-three transplanted lung grafts were recognized as having DP (27.1%). Serial chest x-rays at the donor hospital did not show deteriorating infiltration or consolidation. The PaO2/FiO2 ratio at brain death evaluations were similar between the donor pneumonia (DP) negative (-) and donor pneumonia (DP) positive (+) groups. Perioperative antibiotics were effective against 94% of isolated bacteria. The duration of postoperative antibiotics therapy was longer in the DP (+) group (P = .02). The incidence of primary graft dysfunction and acute rejection, intensive care unit stay, chronic lung allograft dysfunction-free survival, and overall survival were similar between the DP (+) and DP (-) groups. CONCLUSIONS: Transplantation of donor lung grafts harboring pneumonia but having a similar oxygenation level to those without pneumonia was safely performed and did not affect long-term outcome. Appropriate evaluation of serial imaging at donor hospital and suitable perioperative antibiotic management may be reasons for this outcome.

Automated detection of COVID-19 through convolutional neural network using chest x-ray images.

The COVID-19 epidemic has a catastrophic impact on global well-being and public health. More than 27 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, and challenges to the variations of the COVID-19, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. We aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal/COVID-19) and 87.50% accuracy for tertiary classification (Normal/COVID-19/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. In the case of transfer learning, the classification accuracy drops with the increased number of classes. The results are demonstrated by comprehensive receiver operating characteristics (ROC) and confusion metric analysis with 10-fold cross-validation.

A Tool to Distinguish Viral From Bacterial Pneumonia.

BACKGROUND: Establishing the etiology of community-acquired pneumonia (CAP) in children at admission is challenging. Most of the admitted children with CAP receive antibiotics. We aimed to build and validate a diagnostic tool combining clinical, analytical and radiographic features to differentiate viral from bacterial CAP, and among bacterial CAP, typical from atypical bacteria. METHODS: Design-observational, multi-center, prospective cohort study was conducted in 2 phases. Settings: 24 secondary and tertiary hospitals in Spain. Patients-A total of 495 consecutive hospitalized children between 1 month and 16 years of age with CAP were enrolled. Interventions-A score with 2 sequential steps was built (training set, 70% patients, and validation set 30%). Step 1 differentiates between viral and bacterial CAP and step 2 between typical and atypical bacterial CAP. Optimal cutoff points were selected to maximize specificity setting a high sensitivity (80%). Weights of each variable were calculated with a multivariable logistic regression. Main outcome measures-Viral or bacterial etiology. RESULTS: In total, 262 (53%) children (median age: 2 years, 52.3% male) had an etiologic diagnosis. In step 1, bacterial CAPs were classified with a sensitivity = 97%, a specificity = 48%, and a ROC's area under the curve = 0.81. If a patient with CAP was classified as bacterial, he/she was assessed with step 2. Typical bacteria were classified with a sensitivity = 100%, a specificity = 64% and area under the curve = 0.90. We implemented the score into a mobile app named Pneumonia Etiology Predictor, freely available at usual app stores, that provides the probability of each etiology. CONCLUSIONS: This 2-steps tool can facilitate the physician's decision to prescribe antibiotics without compromising patient safety.

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.

Neumonías graves bacterianas en niños menores de 1 año / Severe bacterial pneumonia in children under 1 year of age

La neumonía es una infección común y potencialmente grave que tiene una prevalencia importante en la infancia y causa más muerte que cualquier otra enfermedad en el mundo en niños menores de 5 años. Con el objetivo de caracterizar el comportamiento de neumonía grave bacteriana en los menores de 1 año, ingresados en la unidad de cuidados intensivos pediátricos, en el 2do semestre del año 2019. Se realizó un estudio descriptivo, observacional y retrospectivo en este año. El universo estuvo constituido por 37 pacientes a los que se les diagnosticó neumonía que requirió ingreso hospitalario y la muestra quedó representada por 32 pacientes que cumplieron con los criterios de inclusión y exclusión. El grupo de 0-4 meses (50 por ciento), el sexo masculino (68.8 por ciento), la zona rural (71.9 por ciento), la vía de ingreso por cuerpo de guardia (56.3 por ciento), la estadía hospitalaria menor de 72 horas en UTIP (68.8 por ciento) y las acciones de enfermería independientes (46.8 por ciento), fueron los hallazgos más significativos encontrados. El grupo de edad entre 0-4 meses, del sexo masculino y de procedencia rural predominó en el estudio. El cuerpo de guardia fue la vía de ingreso que más se utilizó. Los pacientes tuvieron una estadía hospitalaria menor de 3 días y las acciones de enfermería independientes en la neumonía grave bacteriana fueron las que más se utilizaron(AU)

Pneumonia is a common and potentially serious infection that has a significant prevalence in childhood and causes more death than any other disease in the world in children under the age of 5. With the aim of characterizing the behavior of severe bacterial pneumonia in children under 1 year, admitted to the pediatric intensive care unit, in the 2nd semester of 2019. A descriptive, observational and retrospective study was conducted this year. The universe consisted of 37 patients who were diagnosed with pneumonia that required hospital admission and the sample was represented by 32 patients who met the inclusion and exclusion criteria. The group of 0-4 months (50 percent), the male sex (68.8 percent), the rural area (71.9 percent), the route of admission by guard corps (56.3 percent), the hospital stay less than 72 hours in PICU (68.8 percent) and the independent nursing actions (46.8 percent), were the most significant findings found. The age group between 0-4 months, male and rural origin predominated in the study. The guard corps was the most widely used route of entry. Patients had a hospital stay of ess than 3 days and independent nursing actions in severe bacterial pneumonia were the most widely used(EU)

Radiographic severity and treatment outcome of Mycobacterium abscessus complex pulmonary disease.

INTRODUCTION: The lack of reliable predictors for the treatment response complicates decisions to initiate treatment in patients with Mycobacterium abscessus complex pulmonary disease (MABC-PD). We aimed to investigate whether baseline radiographic disease severity is associated with treatment outcome in MABC-PD. METHOD: We retrospectively analyzed 101 patients with MABC-PD (54 with M. abscessus-PD and 47 with M. massiliense-PD) treated in a tertiary referral hospital between January 2006 and December 2019. Using chest computed tomography images, baseline radiographic disease severity was quantitatively scored according to five categories of radiographic lesions (bronchiectasis, bronchiolitis, cavities, nodules, and consolidation). RESULTS: Treatment success was achieved in 53.7% of patients with M. abscessus-PD and 85.1% of patients with M. massiliense-PD. Higher overall scores for baseline radiographic disease severity were associated with treatment failure in patients with M. massiliense-PD (aOR 1.35, 95% CI 1.02-1.79 for each 1-point increase in severity score), as well as in patients with M. abscessus-PD (aOR 1.15, 95% CI 1.00-1.33). This was particularly prominent in patients with overall severity score of ≥14 (aOR 31.16, 95% CI 1.12-868.95 for M. massiliense-PD and aOR 3.55, 95% CI 1.01-12.45 for M. abscessus-PD). Among variable radiographic abnormalities, the score for cavitary lesion severity was associated with treatment failure in patients with M. abscessus-PD (aOR 1.26, 95% CI 1.01-1.56), but not in patients with M. massiliense-PD. CONCLUSIONS: Given the association between baseline radiographic disease severity and treatment outcome, initiating treatment should be actively considered before significant progression of radiographic lesions in patients with MABC-PD.

Analysis of SARS-CoV-2 PCR and Chest CT Findings at a Single Facility in Tokyo (Machida Municipal Hospital).

INTRODUCTION: The SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) test is useful for diagnosing COVID-19, and the RT-PCR positive rate is an important indicator for estimating the incidence rate and number of infections. COVID-19 pneumonia is also associated with characteristic findings on chest CT, which can aid in diagnosis. METHODS: We retrospectively evaluated patient background characteristics, the number of cases, the positivity rate, and chest CT findings for positive and negative cases in 672 patients who underwent RT-PCR for suspected COVID-19 at our hospital between April 3 and August 28, 2020. In addition, we compared trends in the positive rates at approximately weekly intervals with trends in the number of new infections in Machida City, Tokyo. RESULTS: The study included 323 men and 349 women, with a median age of 46 years (range: 1 month-100 years). RT-PCR findings were positive in 37 cases, and the positive rate was 5.51%. Trends in the positive rate at our hospital and the number of new COVID-19 cases in the city were similar during the study period. Among patients with positive results, 15 (40.5%) had chest CT findings, and 14 had bilateral homogeneous GGOs. Among patients with negative results, 190 had chest CT findings at the time of examination, and 150 were diagnosed with bacterial pneumonia or bronchitis, with main findings consisting of consolidations and centrilobular opacities. Only 11 of these patients exhibited bilateral homogeneous GGOs. CONCLUSION: Bilateral homogeneous GGOs are characteristic of COVID-19 pneumonia and may aid in the diagnosis of COVID-19.

An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images.

PURPOSE: To construct an auxiliary empirical antibiotic therapy (EAT) multi-class classification model for children with bacterial pneumonia using radiomics features based on artificial intelligence and low-dose chest CT images. MATERIALS AND METHODS: Data were retrospectively collected from children with pathogen-confirmed bacterial pneumonia including Gram-positive bacterial pneumonia (122/389, 31%), Gram-negative bacterial pneumonia (159/389, 41%) and atypical bacterial pneumonia (108/389, 28%) from January 1 to June 30, 2019. Nine machine-learning models were separately evaluated based on radiomics features extracted from CT images; three optimal submodels were constructed and integrated to form a multi-class classification model. RESULTS: We selected five features to develop three radiomics submodels: a Gram-positive model, a Gram-negative model and an atypical model. The comprehensive radiomics model using support vector machine method yielded an average area under the curve (AUC) of 0.75 [95% confidence interval (CI), 0.65-0.83] and accuracy (ACC) of 0.58 [sensitivity (SEN), 0.57; specificity (SPE), 0.78] in the training set, and an average AUC of 0.73 (95% CI 0.61-0.79) and ACC of 0.54 (SEN, 0.52; SPE, 0.75) in the test set. CONCLUSION: This auxiliary EAT radiomics multi-class classification model was deserved to be researched in differential diagnosing bacterial pneumonias in children.

Pulmonary and thoracic infection by Fusobacterium nucleatum. / Infección pulmonar y torácica por Fusobacterium nucleatum.

INTRODUCTION: Fusobacterium nucleatum is an anaerobic bacillus that is part of the oral microbiota and dental pla que. This can cause local and potentially remote infections, which are exceptional in pediatrics. Ob jective: To present the case of a patient with lung injury with chest wall invasion by Fusobacterium nucleatum. CLINICAL CASE: An 11-year-old female immunocompetent patient who consulted due to a two-week history of cough, night sweats, without fever or weight loss, and increased volume at the left spleen thoracic level. There was no history of chest wall trauma or travel outside the country. Two weeks before the onset of symptoms, she was treated for dental caries. Imaging studies and CT scan showed left spleen pneumonia, which invades the pleura and the chest wall. A minimal thoracotomy was performed, releasing a thick, foul-smelling liquid. The studies for common germs and tubercu losis were negative. Hematology ruled out tumor lesions. The anaerobic study reported the develo pment of Fusobacterium nucleatum. The patient was treated with penicillin followed by amoxicillin presenting good clinical and radiological responses. The dental procedure was suspected as the cause of infection. CONCLUSIONS: Fusobacterium nucleatum can occasionally cause remote or extra-oral in fections in immunocompetent patients, such as pneumonia with chest wall invasion, therefore it is necessary to bear it in mind.

Convolutional Sparse Support Estimator-Based COVID-19 Recognition From X-Ray Images.

Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large data sets. However, data scarcity can be a crucial obstacle when using them for COVID-19 detection. Alternative approaches such as representation-based classification [collaborative or sparse representation (SR)] might provide satisfactory performance with limited size data sets, but they generally fall short in performance or speed compared to the neural network (NN)-based methods. To address this deficiency, convolution support estimation network (CSEN) has recently been proposed as a bridge between representation-based and NN approaches by providing a noniterative real-time mapping from query sample to ideally SR coefficient support, which is critical information for class decision in representation-based techniques. The main premises of this study can be summarized as follows: 1) A benchmark X-ray data set, namely QaTa-Cov19, containing over 6200 X-ray images is created. The data set covering 462 X-ray images from COVID-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. 2) The proposed CSEN-based classification scheme equipped with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images when the average performance of 5-fold cross validation over QaTa-Cov19 data set is calculated. 3) Having such an elegant COVID-19 assistive diagnosis performance, this study further provides evidence that COVID-19 induces a unique pattern in X-rays that can be discriminated with high accuracy.