title: Developing a Clinical Prediction Rule for First Hospital-Onset Clostridium difficile Infections: A Retrospective Observational Study.
abstract: BACKGROUND The healthcare burden of hospital-acquired Clostridium difficile infection (CDI) demands attention and calls for a solution. Identifying patients' risk of developing a primary nosocomial CDI is a critical first step in reducing the development of new cases of CDI. OBJECTIVE To derive a clinical prediction rule that can predict a patient's risk of acquiring a primary CDI. DESIGN Retrospective cohort study. SETTING Large tertiary healthcare center. PATIENTS Total of 61,482 subjects aged at least 18 admitted over a 1-year period (2013). INTERVENTION None. METHODS Patient demographic characteristics, evidence of CDI, and other risk factors were retrospectively collected. To derive the CDI clinical prediction rule the patient population was divided into a derivation and validation cohort. A multivariable analysis was performed in the derivation cohort to identify risk factors individually associated with nosocomial CDI and was validated on the validation sample. RESULTS Among 61,482 subjects, CDI occurred in 0.46%. CDI outcome was significantly associated with age, admission in the past 60 days, mechanical ventilation, dialysis, history of congestive heart failure, and use of antibiotic medications. The sensitivity and specificity of the score, in the validation set, were 82.0% and 75.7%, respectively. The area under the receiver operating characteristic curve was 0.85. CONCLUSION This study successfully derived a clinical prediction rule that will help identify patients at high risk for primary CDI. This tool will allow physicians to systematically recognize those at risk for CDI and will allow for early interventional strategies. Infect Control Hosp Epidemiol 2016;37:896-900.
authors.affiliation: Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York.
authors.forename: Anne
authors.initials: A
authors.lastname: Press
authors.affiliation: Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York.
authors.forename: Benson S
authors.initials: BS
authors.lastname: Ku
authors.affiliation: Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York.
authors.forename: Lauren
authors.initials: L
authors.lastname: McCullagh
authors.affiliation: Feinstein Institute for Medical Research, Manhasset, New York.
authors.forename: Lisa
authors.initials: L
authors.lastname: Rosen
authors.affiliation: Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York.
authors.forename: Safiya
authors.initials: S
authors.lastname: Richardson
authors.affiliation: Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York.
authors.forename: Thomas
authors.initials: T
authors.lastname: McGinn
mesh_terms: D000368:Aged; D016360:Clostridioides difficile / Q000302:isolation & purification*; D003015:Clostridium Infections / Q000209:etiology* / Q000517:prevention & control; D003428:Cross Infection / Q000209:etiology* / Q000517:prevention & control; D057286:Electronic Health Records; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies; D018570:Risk Assessment