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

Mamoon A Aldeyab, Stuart E Bond, Ian Gould, Jade Lee-Milner, Joseph J. Spencer-Jones, Achyut Guleri, Adel Sadeq, Feras Jirjees, William J Lattyak

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1125-1134
Number of pages10
JournalExpert Review of Anti-Infective Therapy
Issue number10
Early online date27 Oct 2023
Publication statusPublished - 1 Nov 2023

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