Search

BackgroundCOVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19. ObjectiveThis study aims to validate the PICTURE model’s ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19. MethodsThe PICTURE model was trained and validated on a cohort of hospitalized non–COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non–COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models’ ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions. ResultsIn non–COVID-19 general ward patients, PICTURE achieved an AUROC of 0.819 (95% CI 0.805-0.834) per observation, compared to the EDI’s AUROC of 0.763 (95% CI 0.746-0.781; n=21,740; P

Importance: Community-acquired pneumonia (CAP) results in approximately 1.4 million emergency department visits, 740 000 hospitalizations, and 41 000 deaths in the US annually. Observations: Community-acquired pneumonia can be diagnosed in a patient with 2 or more signs (eg, temperature >38 °C or ≤36 °C; leukocyte count <4000/μL or >10 000/μL) or symptoms (eg, new or increased cough or dyspnea) of pneumonia in conjunction with consistent radiographic findings (eg, air space density) without an alternative explanation. Up to 10% of patients with CAP are hospitalized; of those, up to 1 in 5 require intensive care. Older adults (≥65 years) and those with underlying lung disease, smoking, or immune suppression are at highest risk for CAP and complications of CAP, including sepsis, acute respiratory distress syndrome, and death. Only 38% of patients hospitalized with CAP have a pathogen identified. Of those patients, up to 40% have viruses identified as the likely cause of CAP, with Streptococcus pneumoniae identified in approximately 15% of patients with an identified etiology of the pneumonia. All patients with CAP should be tested for COVID-19 and influenza when these viruses are common in the community because their diagnosis may affect treatment (eg, antiviral therapy) and infection prevention strategies. If test results for influenza and COVID-19 are negative or when the pathogens are not likely etiologies, patients can be treated empirically to cover the most likely bacterial pathogens. When selecting empirical antibacterial therapy, clinicians should consider disease severity and evaluate the likelihood of a bacterial infection—or resistant infection—and risk of harm from overuse of antibacterial drugs. Hospitalized patients without risk factors for resistant bacteria can be treated with β-lactam/macrolide combination therapy, such as ceftriaxone combined with azithromycin, for a minimum of 3 days. Systemic corticosteroid administration within 24 hours of development of severe CAP may reduce 28-day mortality. Conclusions: Community-acquired pneumonia is common and may result in sepsis, acute respiratory distress syndrome, or death. First-line therapy varies by disease severity and etiology. Hospitalized patients with suspected bacterial CAP and without risk factors for resistant bacteria can be treated with β-lactam/macrolide combination therapy, such as ceftriaxone combined with azithromycin, for a minimum of 3 days. This Review summarizes current evidence on pathogenesis, epidemiology, diagnosis, and treatment of community-acquired pneumonia and focuses on adults without immune-compromising conditions. [ABSTRACT FROM AUTHOR]

The letter published in the American Journal of Respiratory & Critical Care Medicine discusses the potential impact of empiric antianaerobic antibiotics on sepsis-related deaths among U.S. adults. The authors conducted simulations based on observational studies, suggesting that routine use of anaerobe-sparing antibiotics could prevent thousands of deaths annually. The study highlights the importance of future research, including randomized trials, to further investigate the population-level impact of antibiotic selection in sepsis. The findings emphasize the significance of even small treatment effects on overall outcomes and the need for evidence-based guidelines in antibiotic prescribing practices for sepsis management. [Extracted from the article]

Flow cytometry holds promise to accelerate antibiotic susceptibility determinations; however, without robust multidimensional statistical analysis, general discrimination criteria have remained elusive. In this study, a new statistical method, probability binning signature quadratic form (PB-sQF), was developed and applied to analyze flow cytometric data of bacterial responses to antibiotic exposure. Both sensitive lab strains (Escherichia coli and Pseudomonas aeruginosa) and a multidrug resistant, clinically isolated strain (E. coli) were incubated with the bacteria-targeted dye, maltohexaose-conjugated IR786, and each of many bactericidal or bacteriostatic antibiotics to identify changes induced around corresponding minimum inhibition concentrations (MIC). The antibiotic-induced damages were monitored by flow cytometry after 1-h incubation through forward scatter, side scatter, and fluorescence channels. The 3-dimensional differences between the flow cytometric data of the no-antibiotic treated bacteria and the antibiotic-treated bacteria were characterized by PB-sQF into a 1-dimensional linear distance. A 99% confidence level was established by statistical bootstrapping for each antibiotic-bacteria pair. For the susceptible E. coli strain, statistically significant increments from this 99% confidence level were observed from 1/16x MIC to 1x MIC for all the antibiotics. The same increments were recorded for P. aeruginosa, which has been reported to cause difficulty in flow-based viability tests. For the multidrug resistant E. coli, significant distances from control samples were observed only when an effective antibiotic treatment was utilized. Our results suggest that a rapid and robust antimicrobial susceptibility test (AST) can be constructed by statistically characterizing the differences between sample and control flow cytometric populations, even in a label-free scheme with scattered light alone. These distances vs paired controls coupled with rigorous statistical confidence limits offer a new path toward investigating initial biological responses, screening for drugs, and shortening time to result in antimicrobial sensitivity testing.

Rationale: Chronic lung allograft dysfunction (CLAD) is the leading cause of death after lung transplant, and azithromycin has variable efficacy in CLAD. The lung microbiome is a risk factor for developing CLAD, but the relationship between lung dysbiosis, pulmonary inflammation, and allograft dysfunction remains poorly understood. Whether lung microbiota predict outcomes or modify treatment response after CLAD is unknown. Objectives: To determine whether lung microbiota predict post-CLAD outcomes and clinical response to azithromycin. Methods: Retrospective cohort study using acellular BAL fluid prospectively collected from recipients of lung transplant within 90 days of CLAD onset. Lung microbiota were characterized using 16S rRNA gene sequencing and droplet digital PCR. In two additional cohorts, causal relationships of dysbiosis and inflammation were evaluated by comparing lung microbiota with CLAD-associated cytokines and measuring ex vivo P. aeruginosa growth in sterilized BAL fluid. Measurements and Main Results: Patients with higher bacterial burden had shorter post-CLAD survival, independent of CLAD phenotype, azithromycin treatment, and relevant covariates. Azithromycin treatment improved survival in patients with high bacterial burden but had negligible impact on patients with low or moderate burden. Lung bacterial burden was positively associated with CLAD-associated cytokines, and ex vivo growth of P. aeruginosa was augmented in BAL fluid from transplant recipients with CLAD. Conclusions: In recipients of lung transplants with chronic rejection, increased lung bacterial burden is an independent risk factor for mortality and predicts clinical response to azithromycin. Lung bacterial dysbiosis is associated with alveolar inflammation and may be promoted by underlying lung allograft dysfunction. [ABSTRACT FROM AUTHOR]