Evidence map›Paper›PMID 39411663›Full record

ArticleAntimicrobial stewardship & healthcare epidemiology : ASHE2024

Derivation of a risk-adjusted model to predict antibiotic prescribing among hospitalists in an academic healthcare network.

Udodirim N Onwubiko, Christina Mehta, Zanthia Wiley, Jesse T Jacob, K Ashley Jones, Julianne Kubes, Hasan F Shabbir, Sujit Suchindran, Scott K Fridkin

Abstract read
In one paragraph

Article in Antimicrobial stewardship & healthcare epidemiology : ASHE, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Estimating the likelihood of hospitalists to repeatedly prescribe high rates of antibiotics.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Udodirim N OnwubikoDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Christina MehtaDepartment of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, USA.
Zanthia WileyDepartment of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, USA.
Jesse T JacobDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.ORCID https://orcid.org/0000-0001-9811-6260
K Ashley JonesDepartment of Pharmacy, Emory Healthcare, Atlanta, GA, USA.
Julianne KubesOffice of Quality, Emory Healthcare, Atlanta, GA, USA.ORCID https://orcid.org/0000-0002-6717-3104
Hasan F ShabbirDepartment of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, USA.
Sujit SuchindranDepartment of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, USA.
Scott K FridkinDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.ORCID https://orcid.org/0000-0003-4892-693X

Funding

NCEZID CDC HHS U54 CK000601
6 · The paper itself

Abstract

Background: Among inpatients, peer-comparison of prescribing metrics is challenging due to variation in patient-mix and prescribing by multiple providers daily. We established risk-adjusted provider-specific antibiotic prescribing metrics to allow peer-comparisons among hospitalists. Methods: Using clinical and billing data from inpatient encounters discharged from the Hospital Medicine Service between January 2020 through June 2021 at four acute care hospitals, we calculated bimonthly (every two months) days of therapy (DOT) for antibiotics attributed to specific providers based on patient billing dates. Ten patient-mix characteristics, including demographics, infectious disease diagnoses, and noninfectious comorbidities were considered as potential predictors of antibiotic prescribing. Using linear mixed models, we identified risk-adjusted models predicting the prescribing of three antibiotic groups: broad spectrum hospital-onset (BSHO), broad-spectrum community-acquired (BSCA), and anti-methicillin-resistant Staphylococcus aureus (Anti-MRSA) antibiotics. Provider-specific observed-to-expected ratios (OERs) were calculated to describe provider-level antibiotic prescribing trends over time. Results: Predictors of antibiotic prescribing varied for the three antibiotic groups across the four hospitals, commonly selected predictors included sepsis, COVID-19, pneumonia, urinary tract infection, malignancy, and age >65 years. OERs varied within each hospital, with medians of approximately 1 and a 75th percentile of approximately 1.25. The median OER demonstrated a downward trend for the Anti-MRSA group at two hospitals but remained relatively stable elsewhere. Instances of heightened antibiotic prescribing (OER >1.25) were identified in approximately 25% of the observed time-points across all four hospitals. Conclusion: Our findings indicate provider-specific benchmarking among inpatient providers is achievable and has potential utility as a valuable tool for inpatient stewardship efforts.

Identifiers

PMID39411663
PMCPMC11474874

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.