Usefulness of Routine Laboratory Tests for Follow up of Patients Receiving Outpatient Parenteral Antimicrobial Therapy Run by Infectious Diseases Fellows.

BACKGROUND: The infectious disease society of America (IDSA) recommends routine laboratory tests for all patients receiving outpatient parenteral antimicrobial therapy (OPAT) to monitor for adverse events. There are no data to support how often patients should take monitoring laboratory tests. In addition, the relevance of different laboratory tests commonly used for OPAT follow up is not clearly known. METHODS: We conducted a retrospective observational cohort study over a 7-year study interval (1 January 2014 to 31 December 2021). Clinical data were obtained to identify the risk factors associated with abnormal laboratory tests and determine if abnormal laboratory tests led to antibiotic change or hospital readmission. RESULTS: Two hundred and forty-six patients met the inclusion criteria for this study. In our multivariate analysis, the Charlson comorbidity index (CCI) of 0-4 (aOR 0.39, 95%Cl 0.18-0.86), the use of ceftriaxone without vancomycin (aOR 0.47, 95%Cl 0.24-0.91) and an OPAT duration of 2-4 weeks (aOR 0.47, 95%Cl 0.24-0.91) were associated with a lower risk of OPAT complications. A CCI of 5 or more (aOR 2.5, 95%Cl (1.1-5.7)) and an OPAT duration of 5 or more weeks (aOR 2.7, 95% Cl 1.3-5.6) were associated with a higher risk of OPAT complications. An abnormal complete metabolic panel or vancomycin levels, but not an abnormal complete blood count, were associated with antibiotic change or readmission. CONCLUSION: Patients with fewer comorbidities, ceftriaxone and short OPAT durations are at lower risk for OPAT complications. These patients could be followed with less frequent laboratory monitoring.

Clinical Features and Treatment Outcomes of Pulmonary Mycobacterium avium-intracellulare Complex With and Without Coinfections.

Coinfections are more common in patients with cystic fibrosis and bronchiectasis. Infiltrates on imaging studies are seen more commonly in patients with coinfections, but coinfections did not affect treatment outcomes of pulmonary Mycobacterium avium complex.

Estimating pediatric cases of COVID-19 over time in the United States: Filling in a gap in public use data.

BACKGROUND: COVID-19 continues to disturb nearly all aspects of life, leaving us striving to reach herd immunity. Currently, only weekly standardized incidence rate data per age group are publicly available, limiting assessment of herd immunity. Here, we estimate the time-series case counts of COVID-19 among age groups currently ineligible for vaccination in the USA. METHODS: This was a secondary analysis of publicly available data. COVID-19 case counts by age groups were computed using incidence rate data from the CDC and population estimates from the US Census Bureau. We also created a web-based application to allow on demand analysis. RESULTS: A total of 78 weeks of data were incorporated in the analysis, suggesting the highest peak in cases within the 5-11-year age group on week ending 2021-01-09 (n = 61,095) followed by the 12-15-year age group (n = 58,093). As of July 24, 2021, case counts in the 5-11-year age group have expanded beyond other groups rapidly. DISCUSSION: This study suggests it is possible to estimate pediatric case counts of COVID-19. National agencies should report COVID-19 time series case counts for pediatric age cohorts. These data will enhance our ability to estimate the population at risk and tailor interventions accordingly.

Methodology minute: A statistical test primer for infection prevention and control.

Selecting the appropriate statistical tests for data analysis is a critical skill for the infection preventionist (IP), both for analyzing their own data as well as evaluating the scientific literature methodology. Obtaining results from data analyses has never been easier thanks to computational improvements, but the interpretation of results relies on a keen awareness that the approach was sound. The purpose of this primer is to introduce the infection preventionist to the ideas behind hypothesis testing with a focus on statistical test selection.

Excess mortality in the United States in 2020: Forecasting and anomaly detection.

All-cause mortality may be better than disease-specific data for computing excess COVID-19 mortality. We documented approximately 350,000 excess deaths using a 20-year forecast of all-cause mortality compared to provisional estimates. We must develop more granular approaches to the collection of mortality data for real-time evaluation of excess deaths.

Thresholds versus Anomaly Detection for Surveillance of Pneumonia and Influenza Mortality.

Computational surveillance of pneumonia and influenza mortality in the United States using FluView uses epidemic thresholds to identify high mortality rates but is limited by statistical issues such as seasonality and autocorrelation. We used time series anomaly detection to improve recognition of high mortality rates. Results suggest that anomaly detection can complement mortality reporting.