1198 infection control & hospital epidemiology october 2017, vol. 38, no. 10


high risk (eg, ciprofloxacin, clindamycin, and amoxicillin).25–27 Afulllistofthe predictors examined is provided in the Appendix. Based on statistical performance, the 3 best-performing


models are described here: basic, enhanced, and automated. The basic model is a parsimonious model with components that could be easily populated in most medical settings. The enhanced model is a variant of the basic model to which a limited set of variables, which could be extracted from an EMR, were added. These variables, which are part of the LAPS2 severity of illness score,22 were based on their statistical


table 1. Predictors Used Within Each Model Modela Predictor


Age (continuous) Age (splines)


Admitted from a skilled nursing facility ≥ 2 hospitalizations within 60 d prior to T0


New gastric acid suppression (PPI) at the onset of iCDI High-risk antibiotic at the onset of iCDIe Fluoroquinolone at the onset of iCDI Patient in the ICU at the onset of iCDI Blood urea nitrogen Creatinine


Blood urea nitrogen: creatinine Total bilirubin Arterial pH Lactate


Total white blood cell count


Lowest temperature within T0+4 d Highest temperature within T0+4 d LAPS2+splinesf COPS2+splinesg


Elapsed hospital length of stay at T0


Basic x


Zilberbergb


Enhanced Automated x


x


Gastrointestinal surgery within 30 d prior to T0 xx Immunosuppression statusc xx Locus of iCDI onsetd


x x x x x


x x x x x x x x x


xx x x


x


contribution using methods described below. Finally, the automated model is based on variables that could be generated in real time given existing systems in place in KPNC.16 We elected to compare these final 3 models against the Zilberberg model, a previously published model by Zilberberg et al,25 because it was based on a large cohort and the authors provided substantive detail on its statistical performance. For the


Zilberberg model, we structured predictors to match the speci- fications of Zilberberg et al exactly.However, we did not employ their original coefficients, instead allowing these to emerge given


x x x x x


NOTE. LAPS2, laboratory-based acute physiology score, version 2; COPS, comorbidity point score, version 2; T0, Time zero (T0) is the date-time stamp of the physician order for the index Clostridium difficile infection test; iCDI, incident


Clostridium difficile infection (see text for how iCDI is defined); ICU, intensive care unit. aSee text for more detail on model selection. bWe replicated the model developed by Zilberberg et al.25 cA patient’s immunosuppression status was defined using algorithmic rules using International Classification of Disease, Ninth Revision (ICD-9) diagnosis codes and immunocompromising medications and treatments used in the


6 mo prior to iCDI. dLocus of iCDI onset is categorized as (1) community-onset, healthcare-facility–associated (iCDI diagnosed by a positive toxin test within 72 h of admission or iCDI diagnosed in any outpatient setting and a hospitalization in the prior 90 d); (2) community-onset, community-associated (reference group in model: iCDI diagnosed by a positive toxin test within 72 h of admission or in any outpatient setting and no hospitalization in the previous 90 d); or (3) hospital-onset, healthcare-facility–associated (CDI diagnosed >72 h after hospital admission). These definitions


were also used by Zilberberg et al.25 eWe employed the same definitions as Zilberberg et al.25 fLAPS2 is a composite severity of illness score and employs 16 laboratory tests, vital signs, pulse oximetry, and


neurological status checks.22 gCOPS2 is a 12-month longitudinal comorbidity burden score that includes history elements (eg, recent surgery involving the gastrointestinal track).22


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