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

Autor:	Onwubiko UN; Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA., Mehta C; Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, USA., Wiley Z; Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, USA., Jacob JT; Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.; Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, USA., Ashley Jones K; Department of Pharmacy, Emory Healthcare, Atlanta, GA, USA., Kubes J; Office of Quality, Emory Healthcare, Atlanta, GA, USA., Shabbir HF; Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, USA.; Office of Quality, Emory Healthcare, Atlanta, GA, USA., Suchindran S; Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, USA., Fridkin SK; Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.; Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, USA.
Jazyk:	angličtina
Zdroj:	Antimicrobial stewardship & healthcare epidemiology : ASHE [Antimicrob Steward Healthc Epidemiol] 2024 Oct 07; Vol. 4 (1), pp. e163. Date of Electronic Publication: 2024 Oct 07 (Print Publication: 2024).
DOI:	10.1017/ash.2024.422
Abstrakt:	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. Competing Interests: None. (© The Author(s) 2024.)
Databáze:	MEDLINE
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