# Improving Empiric Antimicrobial Therapy for Gram-Negative Infections through a Personalized Smart Antibiogram

> **NIH AHRQ K08** · UNIVERSITY OF IOWA · 2022 · $147,917

## Abstract

Project Summary/Abstract
Increasing antimicrobial resistance (AMR) is one of the most urgent public health threats. In 2019, the Center
for Diseases Control and Prevention (CDC) estimated that infections with AMR affect at least 2.8 million people
and are associated with at least 35,900 excessive deaths annually in the US. This threat is particularly
problematic among Gram-negative rod (GNR) pathogens in which high-rates of resistance to last-line
antimicrobials have emerged globally, while our efforts to develop new antimicrobials have stumbled.
To decelerate the emergence of AMR among GNR pathogens, it is essential to guide clinicians away from
choosing unnecessarily broad-spectrum antimicrobials. An antibiogram, a facility-level summary of
antimicrobial susceptibility data, is a common local reference tool which clinicians use when choosing empiric
therapy. However, antibiograms have major limitations. First, little is known about how clinicians are currently
using them when making empiric therapy decision. Second, antibiogram data is aggregated at the facility-level,
and data may be skewed based on the type of practice or geographic area. Lastly, but most importantly, an
antibiogram does not consider any patient-level factors. Therefore, there are strong, and pressing needs to
understand 1) how an antibiogram is used by the clinician, 2) how much an antibiogram reflects the risk of
AMR for individual patients and 3) how we can overcome limitations of antibiogram to optimize empiric therapy
and reduce AMR. The overall goal is to create a novel, real-time personalized antibiogram (“Smart
Antibiogram”) to overcome current limitations of antibiogram and to optimize clinician choice of empiric therapy
for GNRs by providing a “predicted risk of AMR” based on a machine learning model incorporating patient- and
facility-level data. This goal will be accomplished through (a) Master of Science in Health Informatics
coursework, (b) a Mentorship Advisory Committee, (c) carefully selected conferences and workshops, and (d)
a mentored research study. Our specific aims are to (1) Characterize the current use of antibiograms in clinical
practice and measure the acceptable risk of resistance when clinicians make empiric therapy decisions for
Gram-negative bloodstream infections and urinary tract infections within diverse clinical settings; (2) Assess
the accuracy of currently-used antibiograms to predict the risk of resistance for individual patients in a large
retrospective microbiology cohort for GNR infections; (3) Develop a machine learning model to predict the
individualized risk of AMR for patients infected with GNR pathogens and validate prospectively and externally.
This will lead to the future development of a personalized decision support tool (“Smart Antibiogram”). The
expected outcomes of this AHRQ K08 Award will be the comprehensive understanding of the effectiveness
and limitations of antibiogram, and the informatics toolkits to develop Smart An...

## Key facts

- **NIH application ID:** 10489348
- **Project number:** 5K08HS027472-03
- **Recipient organization:** UNIVERSITY OF IOWA
- **Principal Investigator:** Michihiko Goto
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2022
- **Award amount:** $147,917
- **Award type:** 5
- **Project period:** 2020-09-30 → 2025-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10489348

## Citation

> US National Institutes of Health, RePORTER application 10489348, Improving Empiric Antimicrobial Therapy for Gram-Negative Infections through a Personalized Smart Antibiogram (5K08HS027472-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10489348. Licensed CC0.

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