JOURNAL ARTICLE

Assessment of risk-adjusted mortality ratio (RAMR) in bloodstream infections using all-patient refined diagnosis-related groups (APR-DRGs).

  • Published In: Journal of Antimicrobial Chemotherapy (JAC), 2024, v. 79, n. 5. P. 1019 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Calle, Guillermo Maestro De La; Vélez, Jorge; Flores, Javier Mateo; Barrio, Noelia García; Orellana, María Ángeles; Quirós-González, Víctor; Bermejo, Carlos Lumbreras; Bernal, José Luis 3 of 3

Abstract

This article focuses on calculating a risk-adjusted mortality ratio (RAMR) for bloodstream infections (BSIs) using All-Patient Refined Diagnosis-Related Groups (APR-DRGs) and comparing it with the crude mortality rate (CMR). Conducted as a retrospective observational study from 2019 to 2022 in a large tertiary hospital, the study analyzed 2,541 BSIs caused by Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida species. Results showed that RAMR was higher than expected for all pathogens except E. coli, with the greatest excess mortality observed in P. aeruginosa, Candida, and S. aureus infections. The study suggests that RAMR, which adjusts for patient demographics and clinical severity via APR-DRGs, provides a more nuanced and clinically relevant mortality indicator than CMR alone, supporting its use as a complementary metric in antibiotic stewardship programs.

Additional Information

  • Source:Journal of Antimicrobial Chemotherapy (JAC). 2024/05, Vol. 79, Issue 5, p1019
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:0305-7453
  • DOI:10.1093/jac/dkae065
  • Accession Number:177017199
  • Copyright Statement:Copyright of Journal of Antimicrobial Chemotherapy (JAC) is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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