# Data-Driven Interventions for Reducing C. difficile Incidence

> **NIH AHRQ R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $500,000

## Abstract

Considered one of the most urgent microbial threats by the Centers for Disease Control and Prevention (CDC),
estimates of the excess costs of C. difficile infection (CDI) to the healthcare system range from $897 million to
over $4 billion. Our long-term goal is to develop tools to identify patients at risk for CDI that could reduce its
incidence, decrease transmission, improve patient outcomes, and reduce healthcare expenditures. We have
developed and validated an algorithm using the electronic health record (EHR) to identify patients at high risk
for CDI several days in advance of their diagnosis. However, there is a gap in knowledge as to whether real-
world data-driven risk models can improve outcomes by guiding interventions in a clinical setting.
To fill this gap in knowledge and improve CDI prevention efforts in hospitals, we propose the following specific
aims: 1) to prospectively deploy an institution-specific daily risk prediction model for CDI and
assess how elevated risk relates to colonization with C. difficile; 2a) to conduct a quality improvement study
assessing a hospital-wide intervention bundle that incorporates patient risk for CDI; and 2b) to identify
heterogeneous intervention effects across different subgroups (e.g., colonized versus not colonized; specific
ribotypes) and secondary outcomes (e.g., reduced severity/complications). We will apply our model to daily
extracts of EHR data, collect discarded rectal swabs and stool after standard clinical testing is completed to
determine colonization status / ribotypes, and assess our model with respect to colonization status, potentially
incorporating it to further improve the model. Using rates of hospital-acquired CDI, we will also assess the
impact of a hospital-wide, risk-based prevention bundle rolled out for each ward in stepped-wedge, cluster-
randomized fashion. The bundle will include both infection prevention and antimicrobial stewardship
components. This project’s successful completion would provide a model for improving the prevention of CDI
and other healthcare associated infections in hospitals and health centers.

## Key facts

- **NIH application ID:** 9943775
- **Project number:** 1R01HS027431-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Krishna Rao
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $500,000
- **Award type:** 1
- **Project period:** 2020-03-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9943775

## Citation

> US National Institutes of Health, RePORTER application 9943775, Data-Driven Interventions for Reducing C. difficile Incidence (1R01HS027431-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9943775. Licensed CC0.

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