title: Using electronic health information to risk-stratify rates of Clostridium difficile infection in US hospitals.
abstract: BACKGROUND
  Expanding hospitalized patients' risk stratification for Clostridium difficile infection (CDI) is important for improving patient safety. We applied definitions for hospital-onset (HO) and community-onset (CO) CDI to electronic data from 85 hospitals between January 2007 and June 2008 to identify factors associated with higher HO CDI rates.
  
  
  METHODS
  Nonrecurrent CDI cases were identified among adult (≥ 18-year-old) inpatients by a positive C. difficile toxin assay result more than 8 weeks after any previous positive result. Case categories included HO, CO-hospital associated (CO-HA), CO-indeterminate hospital association (CO-IN), and CO-non-hospital associated (CO-NHA). C. difficile testing intensity (CDTI) was defined as the total number of C. difficile tests performed, normalized to the number of patients with at least 1 C. difficile toxin test recorded. We calculated both the incidence density and the prevalence of CDI where appropriate. We fitted a multivariable Poisson model to identify factors associated with higher HO CDI rates.
  
  
  RESULTS
  Among 1,351,156 unique patients with 2,022,213 admissions, 9,803 cases of CDI were identified; of these, 50.6% were HO, 17.4% were CO-HA, 9.0% were CO-IN, and 23.0% were CO-NHA. The incidence density of HO was 6.3 per 10,000 patient-days. The prevalence of CO CDI on admission was, per 10,000 admissions, 8.4 for CO-HA, 4.4 for CO-IN, and 11.1 for CO-NHA. Factors associated (P < .0001) with higher HO CDI rates included older age, higher CO-NHA prevalence on admission, and increased CDTI.
  
  
  CONCLUSIONS
  Electronic health information can be leveraged to risk-stratify HO CDI rates by patient age and CO-NHA prevalence on admission. Hospitals should optimize diagnostic testing to improve patient care and measured CDI rates.
authors.affiliation: Division of Healthcare Quality Promotion, University of Massachusetts and EviMed Research Group, Amherst, Massachusetts 30333, USA. cmcdonald1@cdc.gov
authors.forename: Marya D
authors.initials: MD
authors.lastname: Zilberberg
authors.forename: Ying P
authors.initials: YP
authors.lastname: Tabak
authors.forename: Dawn M
authors.initials: DM
authors.lastname: Sievert
authors.forename: Karen G
authors.initials: KG
authors.lastname: Derby
authors.forename: Richard S
authors.initials: RS
authors.lastname: Johannes
authors.forename: Xiaowu
authors.initials: X
authors.lastname: Sun
authors.forename: L Clifford
authors.initials: LC
authors.lastname: McDonald
mesh_terms: D000328:Adult; D000368:Aged; D016360:Clostridioides difficile; D003015:Clostridium Infections / Q000453:epidemiology*; D017714:Community-Acquired Infections / Q000453:epidemiology; D003428:Cross Infection / Q000453:epidemiology*; D018563:Disease Notification; D057286:Electronic Health Records; D006761:Hospitals; D006801:Humans; D008875:Middle Aged; D015999:Multivariate Analysis; D016012:Poisson Distribution; D011159:Population Surveillance / Q000379:methods*; D012307:Risk Factors; D014481:United States / Q000453:epidemiology