Stites, S. The Journal of Hospital Infection. Published online: 5 September 2016
Background: The incidence and severity of Clostridium difficile infections (CDIs) have increased in recent years. Predictive models may help to identify at-risk patients before the onset of infection. Early identification of high-risk patients could help antimicrobial stewardship (AMS) programmes and other initiatives to better prevent C. difficile in these patients.
Aim: The purpose of this study was to develop a predictive model that identified patients at high-risk for CDI at the time of hospitalization. This approach to early identification was evaluated to determine if it could improve upon a preexisting AMS programme.
Methods: Generalized linear regression and receiver operand characteristic (ROC) curve analyses were used to develop an analytic model to predict CDI risk at the time of hospitalization in a retrospective cohort of inpatients. The model was then validated in a prospective cohort. Concurrence between the model’s risk predictions and a preexisting ABS programme was assessed.
The model identified 55% of patients as high-risk for CDI at the time of admission and who later tested positive. One in every 32 high-risk patients with potentially modifiable antibiotic risk factors tested positive for CDI. Half (53%) tested positive before meeting the risk criteria for the hospital’s AMS programme.
Conclusion: Analytic models can prospectively identify most patients at the time of admission who later test positive for C. difficile. This approach to early identification may help AMS programmes pursue susceptibility testing and modifications to antibiotic therapies sooner in order to better prevent CDI.
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