Identification of antibiotic consumption targets for the management of Clostridioides difficile infection in hospitals- a threshold logistic modelling approach

ABSTRACT

Background

This study aims to demonstrate the utility of a threshold logistic approach to identifying thresholds for specific antibiotic use associated with Clostridioides difficile infection (CDI) in an English teaching hospital.

Methods

A combined approach of nonlinear modeling and logistic regression, named threshold logistic, was used to identify thresholds and risk scores in hospital-level antibiotic use associated with hospital-onset, healthcare-associated (HOHA) CDI cases.

Results

Using a threshold logistic regression approach, an incidence greater than 0.2645 cases/1000 occupied bed-days (OBD; 85th percentile) was determined as the cutoff rate to define a critical (high) incidence rate of HOHA CDI. Fluoroquinolones and piperacillin-tazobactam were found to have thresholds at 84.8 and 54 defined daily doses (DDD)/1000 OBD, respectively. Analysis of data allowed calculating risk scores for HOHA CDI incidence rates exceeding the 85th percentile, i.e. entering critical incidence level. The threshold-logistic model also facilitated performing ‘what-if scenarios’ on future values of fluoroquinolones and piperacillin-tazobactam use to understand how HOHA CDI incidence rates may be affected.

Conclusion

Using threshold logistic analysis, critical incidence levels and antibiotic use targets to control HOHA CDI were determined. Threshold logistic models can be used to inform and enhance the effective design and implementation of antimicrobial stewardship programs.

KEYWORDS:

• Antibiotic consumption
• antimicrobial resistance
• threshold logistic
• Clostridioides difficile infection
• predictive models
• antimicrobial stewardship

Article highlights

• Balancing access to effective antimicrobials with the prevention of the emergence of antibiotic resistance is challenging.

• The non-linear relationship between antibiotic use and resistance creates a threshold of antibiotic use beyond which resistance would be triggered.

• Threshold logistic analysis provided the critical pathogen incidence level and antibiotic use targets to control resistance.

• Threshold logistic analysis provided near-real time performance feedback and future projections to control resistance.

• Threshold logistic models can be used to inform and enhance the effective design and implementation of antimicrobial stewardship programs.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14787210.2023.2263642.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

Conceptualization and design, M Aldeyab and W Lattyak; methodology, all authors; software, W Lattyak; data acquisition and investigation: all authors; formal analysis, M Aldeyab and W Lattyak; W Lattyak was the principal analyst; writing – original draft preparation, M Aldeyab; writing – review and editing, all authors. All authors have read and agreed to the published version of the manuscript.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This paper was not funded.