Effect of β-Lactam Plus Macrolide Versus Fluoroquinolone on 30-Day Readmissions for Community-Acquired Pneumonia

The Global Burden of Community-Acquired Pneumonia in Adults, Encompassing Invasive Pneumococcal Disease and the Prevalence of Its Associated Cardiovascular Events, with a Focus on Pneumolysin and Macrolide Antibiotics in Pathogenesis and Therapy

Despite innovative advances in anti-infective therapies and vaccine development technologies, community-acquired pneumonia (CAP) remains the most persistent cause of infection-related mortality globally. Confronting the ongoing threat posed by Streptococcus pneumoniae (the pneumococcus), the most common bacterial cause of CAP, particularly to the non-immune elderly, remains challenging due to the propensity of the elderly to develop invasive pneumococcal disease (IPD), together with the predilection of the pathogen for the heart. The resultant development of often fatal cardiovascular events (CVEs), particularly during the first seven days of acute infection, is now recognized as a relatively common complication of IPD. The current review represents an update on the prevalence and types of CVEs associated with acute bacterial CAP, particularly IPD. In addition, it is focused on recent insights into the involvement of the pneumococcal pore-forming toxin, pneumolysin (Ply), in subverting host immune defenses, particularly the protective functions of the alveolar macrophage during early-stage disease. This, in turn, enables extra-pulmonary dissemination of the pathogen, leading to cardiac invasion, cardiotoxicity and myocardial dysfunction. The review concludes with an overview of the current status of macrolide antibiotics in the treatment of bacterial CAP in general, as well as severe pneumococcal CAP, including a consideration of the mechanisms by which these agents inhibit the production of Ply by macrolide-resistant strains of the pathogen.

Clinical Efficacy Protocol of Yinhuapinggan Granules: A Randomized, Double-Blind, Parallel, and Controlled Clinical Trial Program for the Intervention of Community-Acquired Drug-Resistant Bacterial Pneumonia as a Complementary Therapy

Background: Community-acquired bacterial pneumonia (CABP) is an important health care concern in the worldwide, and is associated with significant morbidity, mortality, and health care expenditure. Streptococcus pneumoniae is the most frequent causative pathogen of CABP. Common treatment for hospitalized patients with CABP is empiric antibiotic therapy using β-lactams in combination with macrolides, respiratory fluoroquinolones, or tetracyclines. However, overuse of antibiotics has led to an increased incidence of drug-resistant S. pneumoniae, exacerbating the development of community-acquired drug-resistant bacterial pneumonia (CDBP) and providing a challenge for physicians to choose empirical antimicrobial therapy. Methods: Traditional Chinese medicine (TCM) is widely used as a complementary treatment for CDBP. Yinhuapinggan granules (YHPG) is widely used in the adjuvant treatment of CDBP. Experimental studies and small sample clinical trials have shown that YHPG can effectively reduce the symptoms of CDBP. However, there is a lack of high-quality clinical evidence for the role of YHPG as a complementary drug in the treatment of CDBP. Here, we designed a randomized, double-blind, placebo-controlled clinical trial to explore the efficacy and safety of YHPG. A total of 240 participants will be randomly assigned to the YHPG or placebo group in a 1:1 ratio. YHPG and placebo will be added to standard treatment for 10 days, followed by 56 days of follow-up. The primary outcome is the cure rate of pneumonia, and the secondary outcomes includes conversion rate of severe pneumonia, lower respiratory tract bacterial clearance, lactic acid (LC) clearance rate, temperature, C-reactive protein (CRP), criticality score (SMART-COP score), acute physiological and chronic health assessment system (APACHEII score) and clinical endpoint events. Adverse events will be monitored throughout the trial. Data will be analyzed according to a pre-defined statistical analysis plan. This research will disclose the efficacy of YHPG in acquired drug-resistant pneumonia. Clinical Trial Registration: https://clinicaltrials.gov, identifier ChiCTR2100047501.

Impact on in-hospital mortality of ceftaroline versus standard of care in community-acquired pneumonia: a propensity-matched analysis

The purpose of this study is to evaluate the in-hospital mortality of community-acquired pneumonia (CAP) treated with ceftaroline in comparison with standard therapy. This was a retrospective observational study in two centers. Hospitalized patients with CAP were grouped according to the empiric regimen (ceftaroline versus standard therapy) and analyzed using a propensity score matching (PSM) method to reduce confounding factors. Out of the 6981 patients enrolled, 5640 met the inclusion criteria, and 89 of these received ceftaroline. After PSM, 78 patients were considered in the ceftaroline group (cases) and 78 in the standard group (controls). Ceftaroline was mainly prescribed in cases with severe pneumonia (67% vs. 56%, p = 0.215) with high suspicion of Staphylococcus aureus infection (9% vs. 0%, p = 0.026). Cases had a longer length of hospital stay (13 days vs. 10 days, p = 0.007), while an increased risk of in-hospital mortality was observed in the control group compared to the case group (13% vs. 21%, HR 0.41; 95% CI 0.18 to 0.62, p = 0.003). The empiric use of ceftaroline in hospitalized patients with severe CAP was associated with a decreased risk of in-hospital mortality.

Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia

Background

Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality worldwide. Although there are many predictors of death for CAP, there are still some limitations. This study aimed to build a simple and accurate model based on available and common clinical-related feature variables for predicting CAP mortality by adopting machine learning techniques.

Methods

This was a single-center retrospective study. The data used in this study were collected from all patients (≥18 years) with CAP admitted to research hospitals between January 2012 and April 2020. Each patient had 62 clinical-related features, including clinical diagnostic and treatment features. Patients were divided into two endpoints, and by using Tensorflow2.4.1 as the modeling framework, a three-layer fully connected neural network (FCNN) was built as a base model for classification. For a comprehensive comparison, seven classical machine learning methods and their integrated stacking patterns were introduced to model and compare the same training and test data.

Results

A total of 3997 patients with CAP were included; 205 (5.12%) died in the hospital. After performing deep learning methods, this study established an ensemble FCNN model based on 12 FCNNs. By comparing with seven classical machine learning methods, the area under the curve of the ensemble FCNN was 0.975 when using deep learning algorithms to classify poor from good prognosis based on available and common clinical-related feature variables. The predicted outcome was poor prognosis if the ControlNet's poor prognosis score was greater than the cutoff value of 0.50. To confirm the scientificity of the ensemble FCNN model, this study analyzed the weight of random forest features and found that mainstream prognostic features still held weight, although the model is perfect after integrating other factors considered less important by previous studies.

Conclusion

This study used deep learning algorithms to classify prognosis based on available and common clinical-related feature variables in patients with CAP with high accuracy and good generalizability. Every clinical-related feature is important to the model.