Antibiotic Resistance and Prescribing in Children Hospitalized with Community-Acquired Pneumonia

Pneumonia causes more deaths in children under 5 years old worldwide than malaria, AIDS and measles combined. Community-acquired pneumonia occurs annually in about 4 million children under 5 years old in the United States and is typically caused by Streptococcus pneumoniae. Substantial variability exists in the management of this disease. The variability in the management of pediatric pneumonia is due to all aspects of the disease, including but not limited to the numerous agents that cause the disease, the lack of a gold standard diagnostic test and the lack of national guidelines regarding treatment. This variability in treatment has resulted in the use of unnecessarily broad spectrum antibiotics leading to more resistant organisms becoming more prevalent in the community. The prevalence of penicillin resistance in S. pneumoniae has increased over the past decade, but penicillin is found to be still effective clinically in treating nonsusceptible pneumococci. Accredited hospitals in the U.S. document antibiotic susceptibility patterns of S. pneumoniae and it is unclear whether the hospital-reported susceptibility patterns influence the clinician's prescribing patterns. It is also unknown if prescribing broader spectrum antibiotics to patients have similar outcomes to patients who are prescribed narrower spectrum antibiotics, for instance penicillin alone. This research examines the variability that exists in managing pediatric pneumonia by using existing data from 20,000 patients collected from over 30 tertiary care children's hospitals across the United States.Ph.D., Epidemiology -- Drexel University, 201

Emerging Biomarkers of Illness Severity: Urinary Metabolites Associated with Sepsis and Necrotizing Methicillin‐Resistant Staphylococcus aureus Pneumonia

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138419/1/phar1973.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138419/2/phar1973_am.pd

Metabolomics as a Driver in Advancing Precision Medicine in Sepsis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138226/1/phar1974.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138226/2/phar1974_am.pd

Post–COVID-19 Conditions Among Children 90 Days After SARS-CoV-2 Infection

IMPORTANCE Little is known about the risk factors for, and the risk of, developing post-COVID-19 conditions (PCCs) among children. OBJECTIVES To estimate the proportion of SARS-CoV-2-positive children with PCCs 90 days after a positive test result, to compare this proportion with SARS-CoV-2-negative children, and to assess factors associated with PCCs. DESIGN, SETTING, AND PARTICIPANTS This prospective cohort study, conducted in 36 emergency departments (EDs) in 8 countries between March 7, 2020, and January 20, 2021, included 1884 SARS-CoV-2-positive children who completed 90-day follow-up; 1686 of these children were frequency matched by hospitalization status, country, and recruitment date with 1701 SARS-CoV-2-negative controls. EXPOSURE SARS-CoV-2 detected via nucleic acid testing. MAIN OUTCOMES AND MEASURES Post-COVID-19 conditions, defined as any persistent, new, or recurrent health problems reported in the 90-day follow-up survey. RESULTS Of 8642 enrolled children, 2368 (27.4%) were SARS-CoV-2 positive, among whom 2365 (99.9%) had index ED visit disposition data available; among the 1884 children (79.7%) who completed follow-up, the median age was 3 years (IQR, 0-10 years) and 994 (52.8%) were boys. A total of 110 SARS-CoV-2-positive children (5.8%; 95% CI, 4.8%-7.0%) reported PCCs, including 44 of 447 children (9.8%; 95% CI, 7.4%-13.0%) hospitalized during the acute illness and 66 of 1437 children (4.6%; 95% CI, 3.6%-5.8%) not hospitalized during the acute illness (difference. 5.3%; 95% CI, 2.5%-8.5%). Among SARS-CoV-2-positive children, the most common symptom was fatigue or weakness (21 [1.1%]). Characteristics associated with reporting at least 1 PCC at 90 days included being hospitalized 48 hours or more compared with no hospitalization (adjusted odds ratio [aOR], 2.67 [95% CI, 1.63-4.38]); having 4 or more symptoms reported at the index ED visit compared with 1 to 3 symptoms (4-6 symptoms: aOR, 2.35 [95% CI, 1.28-4.31]; >= 7 symptoms: aOR, 4.59 [95% CI, 2.50 8.44]); and being 14 years of age or older compared with younger than 1 year (aOR, 2.67 [95% CI, 1.43-4.99]). SARS-CoV-2-positive children were more likely to report PCCs at 90 days compared with those who tested negative, both among those who were not hospitalized (55 of 1295 [4.2%; 95% CI, 3.2%-5.5%] vs 35 of 1321[2.7%; 95% CI, 1.9%-3.7%]; difference, 1.6% [95% CI, 0.2%-3.0%]) and those who were hospitalized (40 of 391[10.2%; 95% CI, 7.4%-13.7%] vs 19 of 380 [5.0%; 95% CI, 3.0%-7.7%]; difference, 5.2% [95% CI, 1.5%-9.1%]). In addition, SARS-CoV-2 positivity was associated with reporting PCCs 90 days after the index ED visit (aOR, 1.63 [95% CI, 1.14-2.35]), specifically systemic health problems (eg, fatigue, weakness, fever; aOR, 2.44 [95% CI, 1.19-5.00]). CONCLUSIONS AND RELEVANCE In this cohort study, SARS-CoV-2 infection was associated with reporting PCCs at 90 days in children. Guidance and follow-up are particularly necessary for hospitalized children who have numerous acute symptoms and are older.This studywas supported by grants from the Canadian Institutes of Health Research (operating grant: COVID-19-clinical management); the Alberta Health Services-University of Calgary-Clinical Research Fund; the Alberta Children's Hospital Research Institute; the COVID-19 Research Accelerator Funding Track (CRAFT) Program at the University of California, Davis; and the Cincinnati Children's Hospital Medical Center Division of Emergency Medicine Small Grants Program. Dr Funk is supported by the University of Calgary Eyes-High PostDoctoral Research Fund. Dr Freedman is supported by the Alberta Children's Hospital Foundation Professorship in Child Health andWellness

Accounting for misclassification bias of binary outcomes due to underscreening: a sensitivity analysis

Abstract Background Diagnostic tests are performed in a subset of the population who are at higher risk, resulting in undiagnosed cases among those who do not receive the test. This poses a challenge for estimating the prevalence of the disease in the study population, and also for studying the risk factors for the disease. Methods We formulate this problem as a missing data problem because the disease status is unknown for those who do not receive the test. We propose a Bayesian selection model which models the joint distribution of the disease outcome and whether testing was received. The sensitivity analysis allows us to assess how the association of the risk factors with the disease outcome as well as the disease prevalence change with the sensitivity parameter. Results We illustrated our model using a retrospective cohort study of children with asthma exacerbation that were evaluated for pneumonia in the emergency department. Our model found that female gender, having fever during ED or at triage, and having severe hypoxia are significantly associated with having radiographic pneumonia. In addition, simulation studies demonstrate that the Bayesian selection model works well even under circumstances when both the disease prevalence and the screening proportion is low. Conclusion The Bayesian selection model is a viable tool to consider for estimating the disease prevalence and in studying risk factors of the disease, when only a subset of the target population receive the test

Incorporation of biomarkers into a prediction model for paediatric radiographic pneumonia

Objective The aim of this study was to evaluate biomarkers to predict radiographic pneumonia among children with suspected lower respiratory tract infections (LRTI). Methods We performed a single-centre prospective cohort study of children 3 months to 18 years evaluated in the emergency department with signs and symptoms of LRTI. We evaluated the incorporation of four biomarkers (white blood cell count, absolute neutrophil count, C-reactive protein (CRP) and procalcitonin), in isolation and in combination, with a previously developed clinical model (which included focal decreased breath sounds, age and fever duration) for an outcome of radiographic pneumonia using multivariable logistic regression. We evaluated the improvement in performance of each model with the concordance (c-) index. Results Of 580 included children, 213 (36.7%) had radiographic pneumonia. In multivariable analysis, all biomarkers were statistically associated with radiographic pneumonia, with CRP having the greatest adjusted odds ratio of 1.79 (95% CI 1.47–2.18). As an isolated predictor, CRP at a cut-off of 3.72 mg·dL−1 demonstrated a sensitivity of 60% and a specificity of 75%. The model incorporating CRP demonstrated improved sensitivity (70.0% versus 57.7%) and similar specificity (85.3% versus 88.3%) compared to the clinical model when using a statistically derived cutpoint. In addition, the multivariable CRP model demonstrated the greatest improvement in concordance index (0.780 to 0.812) compared with a model including only clinical variables. Conclusion A model consisting of three clinical variables and CRP demonstrated improved performance for the identification of paediatric radiographic pneumonia compared with a model with clinical variables alone

Development and Internal Validation of a Prediction Model to Risk Stratify Children With Suspected Community-Acquired Pneumonia.

BackgroundAlthough community-acquired pneumonia (CAP) is one of the most common infections in children, no tools exist to risk stratify children with suspected CAP. We developed and validated a prediction model to risk stratify and inform hospitalization decisions in children with suspected CAP.MethodsWe performed a prospective cohort study of children aged 3 months to 18 years with suspected CAP in a pediatric emergency department. Primary outcome was disease severity, defined as mild (discharge home or hospitalization for <24 hours with no oxygen or intravenous [IV] fluids), moderate (hospitalization <24 hours with oxygen or IV fluids, or hospitalization >24 hours), or severe (intensive care unit stay for >24 hours, septic shock, vasoactive agents, positive-pressure ventilation, chest drainage, extracorporeal membrane oxygenation, or death). Ordinal logistic regression and bootstrapped backwards selection were used to derive and internally validate our model.ResultsOf 1128 children, 371 (32.9%) developed moderate disease and 48 (4.3%) severe disease. Severity models demonstrated excellent discrimination (optimism-corrected c-indices of 0.81) and outstanding calibration. Severity predictors in the final model included respiratory rate, systolic blood pressure, oxygenation, retractions, capillary refill, atelectasis or pneumonia on chest radiograph, and pleural effusion.ConclusionsWe derived and internally validated a score that accurately predicts disease severity in children with suspected CAP. Once externally validated, this score has potential to facilitate management decisions by providing individualized risk estimates that can be used in conjunction with clinical judgment to improve the care of children with suspected CAP