Related Experiment Video
Updated: Jan 8, 2026
06:55
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
15.0K
Using Encounter-Level Data for Risk-Adjustment of Antimicrobial Use Comparisons: Feasibility and Variable Selection
Rebekah W Moehring1, Michael E Yarrington1, Elizabeth Dodds Ashley1
1Department of Medicine, Division of Infectious Diseases, Duke Center for Antimicrobial Stewardship and Infection Prevention, Duke University, Durham, North Carolina, USA.
Summary
External comparisons of hospital antimicrobial use (AU) can inform stewardship strategies. Encounter-level data and machine learning models proved feasible and meaningful for risk-adjustment, with agnostic approaches performing comparably to expert-adjudicated ones.
Area of Science:
- Healthcare Analytics
- Health Informatics
- Antimicrobial Stewardship
Background:
- External comparisons of hospital antimicrobial use (AU) require risk-adjustment using encounter characteristics to inform antimicrobial stewardship program strategy.
- Barriers to encounter-level modeling include data collection feasibility and optimal variable selection for risk adjustment.
Purpose of the Study:
- To measure achievements in sharing validated, encounter-level AU data among a multisystem hospital collaborative.
- To compare variable selection strategies for AU risk-adjustment models using retrospective analyses.
Main Methods:
- Utilized electronic health record data from 50 US hospitals (2020-2021) for model training and testing.
- Compared four input variable strategies: diagnosis-related groups, Elixhauser comorbidities, agnostic Clinical Classification Software Refined (CCSR), and adjudicated CCSR.
- Employed gradient-boosted machine tree-based models to estimate antibacterial days of therapy (DOT), measuring accuracy with mean absolute error (MAE).
Main Results:
- Fifty of 76 hospitals successfully shared validated datasets.
- Modeling strategies with more CCSR inputs yielded the lowest MAE.
- Agnostic and adjudicated strategies showed highly correlated estimates and similar influential variables.
Conclusions:
- Expert adjudication was resource-intensive and did not yield superior results compared to an agnostic approach.
- Risk-adjustment using extensive encounter-level data and machine learning is feasible and valuable for future hospital AU assessments.
Related Concept Videos
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
380
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
380
01:20Comparing the Survival Analysis of Two or More Groups
533
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
533
01:25Statistical Methods for Analyzing Epidemiological Data
864
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
864
01:25Strategies for Assessing and Addressing Confounding
336
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
336
01:12Analysis of Population Pharmacokinetic Data
653
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
653
01:26Pharmacokinetic Models: Comparison and Selection Criterion
309
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
309