Biological Effects of Corticosteroids on Pneumococcal Pneumonia in Mice and Humans (preprint)

Background: Streptococcus pneumoniae is the most common bacterial cause of community acquired pneumonia and the acute respiratory distress syndrome (ARDS). Some clinical trials have demonstrated a beneficial effect of corticosteroid therapy in community acquired pneumonia, COVID-19, and ARDS, but the mechanisms of this benefit remain unclear. The objective of this study was to investigate the effects of corticosteroids on the pulmonary biology of pneumococcal pneumonia in an observational cohort of mechanically ventilated patients and in a mouse model of bacterial pneumonia with Streptococcus pneumoniae. Methods: We studied gene expression with lower respiratory tract transcriptomes from a cohort of mechanically ventilated patients and in mice. We also carried out comprehensive physiologic, biochemical, and histological analyses in mice to identify the mechanisms of lung injury in Streptococcus pneumoniae with and without adjunctive steroid therapy. Results: Transcriptomic analysis identified pleiotropic effects of steroid therapy on the lower respiratory tract in critically ill patients with pneumococcal pneumonia, findings that were reproducible in mice. In mice with pneumonia, dexamethasone in combination with ceftriaxone reduced (1) pulmonary edema formation, (2) alveolar protein permeability, (3) proinflammatory cytokine release, (4) histopathologic lung injury score, and (5) hypoxemia but did not increase bacterial burden. Conclusions: The gene expression studies in patients and in the mice support the clinical relevance of the mouse studies, which replicate several features of pneumococcal pneumonia and steroid therapy in humans. In combination with appropriate antibiotic therapy in mice, treatment of pneumococcal pneumonia with steroid therapy reduced hypoxemia, pulmonary edema, lung permeability, and histologic criteria of lung injury, and also altered inflammatory responses at the protein and gene expression level. The results from these studies provide evidence for the mechanisms that may explain the beneficial effects of glucocorticoid therapy in patients with community acquired pneumonia from Streptococcus Pneumoniae.

Microbial Dynamics and Pulmonary Immune Responses in COVID-19 Secondary Bacterial Pneumonia (preprint)

Secondary bacterial pneumonia (2°BP) is associated with significant morbidity following respiratory viral infection, yet mechanistically remains incompletely understood. In a prospective cohort of 112 critically ill adults intubated for COVID-19, we comparatively assessed longitudinal airway microbiome dynamics and studied the pulmonary transcriptome of patients who developed 2°BP versus controls who did not. We found that 2°BP was significantly associated with both mortality and corticosteroid treatment. The pulmonary microbiome in 2°BP was characterized by increased bacterial RNA load, dominance of culture-confirmed pathogens, and lower alpha diversity. Bacterial pathogens were detectable days prior to 2°BP clinical diagnosis, and in most cases were also present in nasal swabs. Pathogen antimicrobial resistance genes were also detectable in both the lower airway and nasal samples, and in some cases were identified prior to 2°BP clinical diagnosis. Assessment of the pulmonary transcriptome revealed suppressed TNFa signaling via NF-kB in patients who developed 2°BP, and a sub-analysis suggested that this finding was mediated by corticosteroid treatment. Within the 2°BP group, we observed a striking inverse correlation between innate and adaptive immune gene expression and bacterial RNA load. Together, our findings provide fresh insights into the microbial dynamics and host immune features of COVID-19-associated 2°BP.

Delayed diagnosis of Lemierre's syndrome in a patient with severe coronavirus disease-2019: Importance of comprehensive oral and neck examination – A case report (preprint)

Background: Given the widespread prevalence of the coronavirus disease-2019 (COVID-19), oral and neck examinations tend to be avoided in patients with suspected or confirmed COVID-19. This might delay the diagnosis of conditions, such as Lemierre's syndrome, which involves symptoms resembling COVID-19-related throat manifestations. Case presentation: A 24-year-old man without any underlying conditions was diagnosed with COVID-19 7 days before presentation. He was admitted to another hospital 1 day before presentation with severe COVID-19 and suspected bacterial pneumonia; accordingly, he was started on treatment with remdesivir and meropenem. Owing to bacteremic complications, the patient was transferred to our hospital for intensive care. On the sixth day, the patient experienced hemoptysis; further, a computed tomography (CT) scan revealed new pulmonary artery pseudoaneurysms. Successful embolization was performed to achieve hemostasis. In blood cultures conducted at the previous hospital, Fusobacterium nucleatum was isolated, suggesting cervical origin of the infection. A neck CT scan confirmed a peritonsillar abscess and left internal jugular vein thrombus; accordingly, he was diagnosed with Lemierre's syndrome. The treatment was switched to ampicillin/sulbactam, based on the drug susceptibility results. After 6 weeks of treatment, the patient completely recovered without complications. Conclusion: This case highlights the significance of thorough oral and neck examinations in patients with suspected or diagnosed COVID-19 for the detection of throat and neck symptoms caused by other conditions.

Identification of priority pathogens for aetiological diagnosis in adults with community-acquired pneumonia in China: a multicentre prospective study.

BACKGROUND: Community-acquired pneumonia (CAP) is a major public health challenge worldwide. However, the aetiological and disease severity-related pathogens associated with CAP in adults in China are not well established based on the detection of both viral and bacterial agents. METHODS: A multicentre, prospective study was conducted involving 10 hospitals located in nine geographical regions in China from 2014 to 2019. Sputum or bronchoalveolar lavage fluid (BALF) samples were collected from each recruited CAP patient. Multiplex real-time PCR and bacteria culture methods were used to detect respiratory pathogens. The association between detected pathogens and CAP severity was evaluated. RESULTS: Among the 3,403 recruited eligible patients, 462 (13.58%) had severe CAP, and the in-hospital mortality rate was 1.94% (66/3,403). At least one pathogen was detected in 2,054 (60.36%) patients, with two or more pathogens were co-detected in 725 patients. The ten major pathogens detected were Mycoplasma pneumoniae (11.05%), Haemophilus influenzae (10.67%), Klebsiella pneumoniae (10.43%), influenza A virus (9.49%), human rhinovirus (9.02%), Streptococcus pneumoniae (7.43%), Staphylococcus aureus (4.50%), adenovirus (2.94%), respiratory syncytial viruses (2.35%), and Legionella pneumophila (1.03%), which accounted for 76.06-92.52% of all positive detection results across sampling sites. Klebsiella pneumoniae (p < 0.001) and influenza viruses (p = 0.005) were more frequently detected in older patients, whereas Mycoplasma pneumoniae was more frequently detected in younger patients (p < 0.001). Infections with Klebsiella pneumoniae, Staphylococcus aureus, influenza viruses and respiratory syncytial viruses were risk factors for severe CAP. CONCLUSIONS: The major respiratory pathogens causing CAP in adults in China were different from those in USA and European countries, which were consistent across different geographical regions over study years. Given the detection rate of pathogens and their association with severe CAP, we propose to include the ten major pathogens as priorities for clinical pathogen screening in China.

Advances in the study of myeloid-derived suppressor cells in infectious lung diseases.

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature cells capable of inhibiting T-cell responses. MDSCs have a crucial role in the regulation of the immune response of the body to pathogens, especially in inflammatory response and pathogenesis during anti-infection. Pathogens such as bacteria and viruses use MDSCs as their infectious targets, and even some pathogens may exploit the inhibitory activity of MDSCs to enhance pathogen persistence and chronic infection of the host. Recent researches have revealed the pathogenic significance of MDSCs in pathogens such as bacteria and viruses, despite the fact that the majority of studies on MDSCs have focused on tumor immune evasion. With the increased prevalence of viral respiratory infections, the resurgence of classical tuberculosis, and the advent of medication resistance in common bacterial pneumonia, research on MDSCs in these illnesses is intensifying. The purpose of this work is to provide new avenues for treatment approaches to pulmonary infectious disorders by outlining the mechanism of action of MDSCs as a biomarker and therapeutic target in pulmonary infectious diseases.

[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.

One year after ICU admission for severe community-acquired pneumonia of bacterial, viral or unidentified etiology. What are the outcomes?

INTRODUCTION: Multiplex polymerase chain reaction (mPCR) for respiratory virus testing is increasingly used in community-acquired pneumonia (CAP), however data on one-year outcome in intensive care unit (ICU) patients with reference to the causative pathogen are scarce. MATERIALS AND METHODS: We performed a single-center retrospective study in 123 ICU patients who had undergone respiratory virus testing for CAP by mPCR and with known one-year survival status. Functional status including dyspnea (mMRC score), autonomy (ADL Katz score) and need for new home-care ventilatory support was assessed at a one-year post-ICU follow-up. Mortality rates and functional status were compared in patients with CAP of a bacterial, viral or unidentified etiology one year after ICU admission. RESULTS: The bacterial, viral and unidentified groups included 19 (15.4%), 37 (30.1%), and 67 (54.5%) patients, respectively. In multivariate analysis, one-year mortality in the bacterial group was higher compared to the viral group (HR 2.92, 95% CI 1.71-7.28, p = 0.02) and tended to be higher compared to the unidentified etiology group (p = 0.06); but no difference was found between the viral and the unidentified etiology group (p = 0.43). In 64/83 one-year survivors with a post-ICU follow-up consultation, there were no differences in mMRC score, ADL Katz score and new home-care ventilatory support between the groups (p = 0.52, p = 0.37, p = 0.24, respectively). Severe dyspnea (mMRC score = 4 or death), severe autonomy deficiencies (ADL Katz score ≤ 2 or death), and major adverse respiratory events (new home-care ventilatory support or death) were observed in 52/104 (50.0%), 47/104 (45.2%), and 65/104 (62.5%) patients, respectively; with no difference between the bacterial, viral and unidentified group: p = 0.58, p = 0.06, p = 0.61, respectively. CONCLUSIONS: CAP of bacterial origin had a poorer outcome than CAP of viral or unidentified origin. At one-year, impairment of functional status was frequently observed, with no difference according to the etiology.

Methodological rigor and reporting quality of clinical practice guidelines for adults hospitalized with bacterial pneumonia: a scoping review protocol.

OBJECTIVE: This scoping review will present a profile of methodological rigor and reporting quality of clinical practice guidelines for adults hospitalized with bacterial pneumonia. INTRODUCTION: An ideal clinical practice guideline is evidence-based and the product of a rigorous and robust literature-vetted process, yet reports show that rigor is not being achieved. Moreover, a new vulnerable population has been identified due to COVID-19, increasing the need for high quality clinical practice guidelines. Preliminary searches yielded no scoping or systematic reviews on methodological rigor and reporting quality of clinical practice guidelines used for managing bacterial pneumonia in hospitalized adults. INCLUSION CRITERIA: This review will consider current national and international clinical practice guidelines for management of hospitalized adult patients with either suspected or confirmed primary bacterial pneumonia. The review will include adult patients with multiple diagnoses if there is a clearly delineated clinical practice guideline for pneumonia. METHODS: A 3-step search strategy will be conducted using JBI methodology for scoping reviews. After an initial MEDLINE search for keywords, a broad search of 7 databases, 1 simultaneous platform, gray literature, specialty organizations, and international guideline groups will be conducted from 2017 to the present, in any language. Reference lists will be screened for additional sources. A 2-step screening process will be used to identify eligible clinical practice guidelines. Three reviewers will independently extract data using a standardized form. Domain scores will be analyzed and presented as percentages, and the results will be interpreted as map trends. DETAILS OF THIS REVIEW PROJECT ARE AVAILABLE AT: Open Science Framework https://osf.io/eucqy/.

Clinical Features of COVID-19 and Differentiation from Other Causes of CAP.

Community-acquired pneumonia (CAP) is a significant cause of morbidity and mortality, one of the most common reasons for infection-related death worldwide. Causes of CAP include numerous viral, bacterial, and fungal pathogens, though frequently no specific organism is found. Beginning in 2019, the COVID-19 pandemic has caused incredible morbidity and mortality. COVID-19 has many features typical of CAP such as fever, respiratory distress, and cough, and can be difficult to distinguish from other types of CAP. Here, we highlight unique clinical features of COVID-19 pneumonia such as olfactory and gustatory dysfunction, lymphopenia, and distinct imaging appearance.

Chest computed tomography analysis of lung sparing morphology: differentiation of COVID-19 pneumonia from influenza pneumonia and bacterial pneumonia using the arched bridge and vacuole signs.

INTRODUCTION: This study evaluated the arched bridge and vacuole signs, which constitute morphological patterns of lung sparing in coronavirus disease 2019 (COVID-19), then examined whether these signs could be used to differentiate COVID-19 pneumonia from influenza pneumonia or bacterial pneumonia. METHODS: In total, 187 patients were included: 66 patients with COVID-19 pneumonia, 50 patients with influenza pneumonia and positive computed tomography findings, and 71 patients with bacterial pneumonia and positive computed tomography findings. Images were independently reviewed by two radiologists. The incidences of the arched bridge sign and/or vacuole sign were compared among the COVID-19 pneumonia, influenza pneumonia, and bacterial pneumonia groups. RESULTS: The arched bridge sign was much more common among patients with COVID-19 pneumonia (42/66, 63.6%) than among patients with influenza pneumonia (4/50, 8.0%; P<0.001) or bacterial pneumonia (4/71, 5.6%; P<0.001). The vacuole sign was also much more common among patients with COVID-19 pneumonia (14/66, 21.2%) than among patients with influenza pneumonia (1/50, 2.0%; P=0.005) or bacterial pneumonia (1/71, 1.4%; P<0.001). The signs occurred together in 11 (16.7%) patients with COVID-19 pneumonia, but they did not occur together in patients with influenza pneumonia or bacterial pneumonia. The arched bridge and vacuole signs predicted COVID-19 pneumonia with respective specificities of 93.4% and 98.4%. CONCLUSION: The arched bridge and vacuole signs are much more common in patients with COVID-19 pneumonia and can help differentiate COVID-19 pneumonia from influenza and bacterial pneumonia.

Hematological Parameters and Procalcitonin as Discriminants between Bacterial Pneumonia-Induced Sepsis and Viral Sepsis Secondary to COVID-19: A Retrospective Single-Center Analysis.

Bacterial and viral sepsis induce alterations of all hematological parameters and procalcitonin is used as a biomarker of infection and disease severity. Our aim was to study the hematological patterns associated with pulmonary sepsis triggered by bacteria and Severe Acute Respiratory Syndrome-Coronavirus-type-2 (SARS-CoV-2) and to identify the discriminants between them. We performed a retrospective, observational study including 124 patients with bacterial sepsis and 138 patients with viral sepsis. Discriminative ability of hematological parameters and procalcitonin between sepsis types was tested using receiver operating characteristic (ROC) analysis. Sensitivity (Sn%), specificity (Sp%), positive and negative likelihood ratios were calculated for the identified cut-off values. Patients with bacterial sepsis were older than patients with viral sepsis (p < 0.001), with no differences regarding gender. Subsequently to ROC analysis, procalcitonin had excellent discriminative ability for bacterial sepsis diagnosis with an area under the curve (AUC) of 0.92 (cut-off value of >1.49 ng/mL; Sn = 76.6%, Sp = 94.2%), followed by RDW% with an AUC = 0.87 (cut-off value >14.8%; Sn = 80.7%, Sp = 85.5%). Leukocytes, monocytes and neutrophils had good discriminative ability with AUCs between 0.76-0.78 (p < 0.001), while other hematological parameters had fair or no discriminative ability. Lastly, procalcitonin value was strongly correlated with disease severity in both types of sepsis (p < 0.001). Procalcitonin and RDW% had the best discriminative ability between bacterial and viral sepsis, followed by leukocytes, monocytes and neutrophils. Procalcitonin is a marker of disease severity regardless of sepsis type.

COVision: Convolutional Neural Network for the Differentiation of COVID-19 from Common Pulmonary Conditions Using CT Scans (preprint)

With the growing amount of COVID-19 cases, especially in developing countries with limited medical resources, it is essential to accurately diagnose COVID-19 with high specificity. Due to characteristic ground-glass opacities (GGOs), present in both COVID-19 and other acute lung diseases, misdiagnosis occurs often — 26.6% of the time in manual interpretations of CT scans. Current deep learning models can identify COVID-19 but cannot distinguish it from other common lung diseases like bacterial pneumonia. COVision is a multiclassification convolutional neural network (CNN) that can differentiate COVID-19 from other common lung diseases, with a low false-positivity rate. This CNN achieved an accuracy of 95.8%, AUROC of 0.970, and specificity of 98%. We found statistical significance that our CNN performs better than three independent radiologists with at least 10 years of experience, especially at differentiating COVID-19 from pneumonia. After training our CNN with 105,000 CT slices, we analyzed our CNN’s activation maps and found that lesions in COVID-19 presented peripherally, closer to the pleura, whereas pneumonia lesions presented centrally. Finally, using a federated averaging model, we ensemble our CNN with a pretrained clinical factors neural network (CFNN) to create a comprehensive diagnostic tool.

COVID-19 in Patients with Cancer: Insights from a Tertiary Care Intensive Care Unit (preprint)

Background & Aim: Since The emergence of the COVID-19, patients with cancer have been among the most vulnerable patients, as this infection can be severe and mostly requires intensive care therapy. Literature discussing the risk factors and the outcome of these patients in intensive care units (ICU) is accumulating. Our study aims to search for the incidence of COVID-19 infection in cancer patients and analyses their associated comorbidities, possible risk factor for infections, and their outcomes. Methods: Patients with active cancer under treatment and those recently diagnosed with cancer and had confirmed COVID-19 infection requiring ICU admission were included in our study over 8 months, from March to October 2022. Patient demographic data, comorbidities, ICU stay, duration of hospital stay, oxygenation/ventilatory requirements, treatment, secondary bacterial infection, and outcome were collected from the COVID-19 patients' registry in the ICU. Data were entered into the SPSS program version 23, and results were considered statistically significant at p ≤ 0.05. Results:  A total of 24 patients with cancer and COVID-19 infection required intensive care therapy. The most common type of malignancy in those patients was solid organ tumor (13 vs. 11 patients), and most of the study sample were males (20/ 83.3%). Seventy-five percent (18 patients) required intubation and invasive ventilation. Twenty-nine percent (7 patients) had secondary bacterial pneumonia and bacteremia. In addition, 70% had septic shock and required vasopressors. Acute kidney injury (AKI) due to rhabdomyolysis (P<0.001), secondary bacterial infection (P<0.006), bacteremia and pneumonia (P<0.02), invasive ventilation (P<0.02) and requiring muscle relaxant (P<0.02), the requirement for High flow nasal cannula and prone position (P<0.03 and 0.01) respectively, shock (P<0.004) were significantly associated with increased mortality. Patients with cancer and COVID-19 had higher severity scores (P<0.003), longer ventilation duration (P<0.002), and ICU stay (P<0.002). Overall mortality was 45%.8, there was no significant difference in mortality rate between patients with solid organ tumors and hematological malignancy with COVID-19 infection requiring intensive care therapy (P<0.68). Conclusion: Cancer patients requiring ICU were more prone to develop AKI, rhabdomyolysis, secondary infection, requiring ventilation and prone position, and septic shock. These patients had a significantly high mortality rate and were severely ill, requiring prolonged ventilation and ICU stays.

Increased mortality in ICU patients ≥70 years old with COVID-19 compared to patients with other pneumonias.

BACKGROUND: Patients over 70 years old represent a substantial proportion of the COVID-19 ICU population and their mortality rates are high. The aim of this study is to describe the outcomes of patients ≥70 years old admitted to Dutch ICUs with COVID-19, compared to patients ≥70 years old admitted to the ICU for bacterial and other viral pneumonias, with adjustments for age, comorbidities, severity of illness, and ICU occupancy rate. METHODS: Retrospective cohort study including patients ≥70 years old admitted to Dutch ICUs, comparing patients admitted with COVID-19 from March 1st 2020 to January 1st 2022 with patients ≥70 years old admitted because of a bacterial and other viral pneumonia, both divided in a historical (i.e., January 1st 2017 to January 1st 2020) and current cohort (i.e., March 1st 2020 to January 1st 2022). Primary outcome is hospital mortality. RESULTS: 11,525 unique patients ≥70 years old admitted to Dutch ICUs were included; 5094 with COVID-19, 5334 with a bacterial pneumonia, and 1312 with another viral pneumonia. ICU-mortality and in-hospital mortality rates of the patients ≥70 years old admitted with COVID-19 were 39.7% and 47.6% respectively. ICU- and hospital mortality rates of the patients who were admitted in the same or in an historical time period with a bacterial pneumonia or other viral pneumonias were considerably lower (19.5% and 28.6% for patients with a bacterial pneumonia in the historical cohort and 19.1% and 28.8% in the same period, for the patients with other viral pneumonias 20.7% and 28.9%, and 22.7% and 31.8% respectively, all p < 0.001). Differences persisted after correction for several clinical characteristics and ICU occupancy rate. CONCLUSIONS: In ICU-patients ≥70 years old, COVID-19 is more severe compared to bacterial or viral pneumonia.

Fall of viral and bacterial pneumonia hospitalizations following COVID-19 pandemic mitigation strategies: a central Italian Region retrospective study.

Community-Acquired Pneumonia (CAP) represents one of the first causes of hospitalization and death in the elderly all over the world and weighs heavily on public health system. Since the beginning of the COVID-19 (CoronaVirus Disease-19) pandemic, everybody's behavior was forced to change, as the result of a global lockdown strategy and the obligation of using personal protection equipment (PPE). We aimed to evaluate how the mitigation strategies adopted to fight SARS-CoV-2 (Severe Acute Respiratory Coronavirus Syndrome 2) infection have influenced hospitalizations due to CAP in two different Local Health Boards (LHBs) of central Italy. We considered two main periods of observation: before and after the national start of lockdown, in two Abruzzo's LHBs. We analyzed 19,558 hospital discharge records of bacterial and viral CAP. Excluding SARS-CoV2 infection, a significant decrease in CAP hospitalizations was observed. Through the analysis of Diagnosis Related Group (DRG) values, we highlighted a significant saving of founds for the Regional Health Service. The enactment of social distancing measures to contain COVID-19 spread, brought down admissions for bacterial and viral pneumonia. Our study emphasizes that costs for hospitalizations due to CAP could be drastically reduced by mask wearing and social distancing.

COVision: Convolutional Neural Network for the Differentiation of COVID-19 from Common Pulmonary Conditions using CT Scans (preprint)

With the growing amount of COVID-19 cases, especially in developing countries with limited medical resources, it is essential to accurately diagnose COVID-19 with high specificity. Due to characteristic ground-glass opacities (GGOs), present in both COVID-19 and other acute lung diseases, misdiagnosis occurs often: 26.6% of the time in manual interpretations of CT scans. Current deep-learning models can identify COVID-19 but cannot distinguish it from other common lung diseases like bacterial pneumonia. COVision is a multi-classification convolutional neural network (CNN) that can differentiate COVID-19 from other common lung diseases, with a low false-positivity rate. This CNN achieved an accuracy of 95.8%, AUROC of 0.970, and specificity of 98%. We found a statistical significance that our CNN performs better than three independent radiologists with at least 10 years of experience. especially in differentiating COVID-19 from pneumonia. After training our CNN with 105,000 CT slices, we analyzed the activation maps of our CNN and found that lesions in COVID-19 presented peripherally, closer to the pleura, whereas pneumonia lesions presented centrally. Finally, using federated averaging, we ensemble our CNN with a pretrained clinical factors neural network (CFNN) to create a comprehensive diagnostic tool.

Seroprevalence of community-acquired atypical bacterial pneumonia among adult COVID-19 patients from a single center in Al Madinah Al Munawarah, Saudi Arabia: A retrospective cohort study.

OBJECTIVES: To investigate the seroprevalence of the community-acquired bacterial that causes atypical pneumonia among confirmed severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) patients. METHODS: In this cohort study, we retrospectively investigated the seroprevalence of Chlamydia pneumoniae, Mycoplasma pneumoniae, and Legionella pneumophila among randomly selected 189 confirmed COVID-19 patients at their time of hospital presentation via commercial immunoglobulin M (IgM) antibodies against these bacteria. We also carried out quantitative measurements of procalcitonin in patients' serum. RESULTS: The seropositivity for L. pneumophila was 12.6%, with significant distribution among patientsolder than 50 years (χ2 test, p=0.009), while those of M. pneumoniae was 6.3% and C. pneumoniae was 2.1%, indicating an overall co-infection rate of 21% among COVID-19 patients. No significant difference (χ2 test, p=0.628) in the distribution of bacterial co-infections existed between male and female patients. Procalcitonin positivity was confirmed amongst 5% of co-infected patients. CONCLUSION: Our study documented the seroprevalence of community-acquired bacteria co-infection among COVID-19 patients. In this study, procalcitonin was an inconclusive biomarker for non-severe bacterial co-infections among COVID-19 patients. Consideration and proper detection of community-acquired bacterial co-infection may minimize misdiagnosis during the current pandemic and positively reflect disease management and prognosis.

Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning.

COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively.