Contrasting methods to operationalize antibiotic exposure in clinical research: a real-world application on health care–associated Clostridioides difficile infection - Report - MDSpire
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Contrasting methods to operationalize antibiotic exposure in clinical research: a real-world application on health care–associated Clostridioides difficile infection
Defining Antibiotic Exposure in Clinical Studies on C. difficile Infection
Overview
This study compares three analytic methods—factor analysis, logistic regression, and LASSO regression—to operationalize antibiotic exposure in hospitalized patients with health care–associated Clostridioides difficile infection. Findings highlight multiple antibiotic exposure characteristics that influence infection risk and model prediction.
Background
Antibiotic exposure assessment is critical in clinical research evaluating outcomes such as health care–associated infections, including Clostridioides difficile. Operationalizing antibiotic use is complex due to variations in medication type, dose, duration, and timing. Simplistic dichotomous measures may overlook important exposure nuances. This study aims to summarize common operationalization methods and demonstrate analytic approaches to better capture antibiotic exposure in clinical epidemiology.
Data Highlights
Method
Key Variables Selected
Model Purpose
Factor Analysis
8 variables: any antibiotic exposure; number of antibiotic classes; number of antibiotic courses; dose; monobactam; β-lactam–β-lactamase inhibitors; rifamycin; cephalosporin
Exploratory dimensionality reduction
Logistic Regression
Any antibiotic exposure; proportion of hospitalization on antibiotics
Best model fit for association with C. difficile infection
LASSO Regression
22 variables including 10 antibiotic exposures: any exposure; β-lactam–β-lactamase inhibitors; carbapenem; cephalosporin; fluoroquinolone; monobactam; rifamycin; sulfonamides; miscellaneous; proportion of hospitalization on antibiotics
Predictive modeling of infection risk
Key Findings
Factor analysis identified eight key antibiotic exposure variables contributing most to data variation.
Logistic regression models with predictors for any antibiotic exposure and proportion of hospitalization on antibiotics yielded best fit for infection association.
LASSO regression selected a broader set of 22 variables, including multiple antibiotic classes and exposure timing, for predictive modeling.
Antibiotic exposure operationalization should consider multiple dimensions such as type, dose, duration, and timing.
Different analytic approaches provide complementary insights for exposure variable selection in clinical research.
Clinical Implications
Clinicians and researchers should recognize that antibiotic exposure is multifaceted and that simplistic binary measures may inadequately capture risk factors for health care–associated infections like C. difficile. Incorporating detailed exposure characteristics into study designs and predictive models can improve understanding and stewardship efforts. Tailoring operational definitions to specific research questions enhances validity and comparability across studies.
Conclusion
Multiple operational definitions of antibiotic exposure exist, each with strengths for different analytic goals. Employing diverse modeling approaches enables more nuanced assessment of antibiotic-related infection risk and supports improved clinical research and antimicrobial stewardship.
References
Study Authors/2024 -- Diverse Approaches for Defining Antibiotic Exposure in Clinical Studies
