title: Validation of a Sampling Method to Collect Exposure Data for Central-Line-Associated Bloodstream Infections.
abstract: OBJECTIVE
  Surveillance of central-line-associated bloodstream infections requires the labor-intensive counting of central-line days (CLDs). This workload could be reduced by sampling. Our objective was to evaluate the accuracy of various sampling strategies in the estimation of CLDs in intensive care units (ICUs) and to establish a set of rules to identify optimal sampling strategies depending on ICU characteristics.
  
  
  METHODS
  Analyses of existing data collected according to the European protocol for patient-based surveillance of ICU-acquired infections in Belgium between 2004 and 2012.
  
  
  METHODS
  CLD data were reported by 56 ICUs in 39 hospitals during 364 trimesters.
  
  
  METHODS
  We compared estimated CLD data obtained from weekly and monthly sampling schemes with the observed exhaustive CLD data over the trimester by assessing the CLD percentage error (ie, observed CLDs - estimated CLDs/observed CLDs). We identified predictors of improved accuracy using linear mixed models.
  
  
  RESULTS
  When sampling once per week or 3 times per month, 80% of ICU trimesters had a CLD percentage error within 10%. When sampling twice per week, this was >90% of ICU trimesters. Sampling on Tuesdays provided the best estimations. In the linear mixed model, the observed CLD count was the best predictor for a smaller percentage error. The following sampling strategies provided an estimate within 10% of the actual CLD for 97% of the ICU trimesters with 90% confidence: 3 times per month in an ICU with >650 CLDs per trimester or each Tuesday in an ICU with >480 CLDs per trimester.
  
  
  CONCLUSIONS
  Sampling of CLDs provides an acceptable alternative to daily collection of CLD data.
authors.affiliation: 1Public Health and Surveillance Department,Scientific Institute of Public Health,Brussels,Belgium.
authors.forename: Naïma
authors.initials: N
authors.lastname: Hammami
authors.affiliation: 1Public Health and Surveillance Department,Scientific Institute of Public Health,Brussels,Belgium.
authors.forename: Karl
authors.initials: K
authors.lastname: Mertens
authors.affiliation: 2Department of Applied Mathematics,Computer Science and Statistics,Ghent University,9000 Ghent,Belgium.
authors.forename: Rosanna
authors.initials: R
authors.lastname: Overholser
authors.affiliation: 2Department of Applied Mathematics,Computer Science and Statistics,Ghent University,9000 Ghent,Belgium.
authors.forename: Els
authors.initials: E
authors.lastname: Goetghebeur
authors.affiliation: 1Public Health and Surveillance Department,Scientific Institute of Public Health,Brussels,Belgium.
authors.forename: Boudewijn
authors.initials: B
authors.lastname: Catry
authors.affiliation: 1Public Health and Surveillance Department,Scientific Institute of Public Health,Brussels,Belgium.
authors.forename: Marie-Laurence
authors.initials: ML
authors.lastname: Lambert
mesh_terms: D016470:Bacteremia / Q000453:epidemiology*; D001530:Belgium / Q000453:epidemiology; D002405:Catheterization, Central Venous / Q000009:adverse effects*; D003428:Cross Infection / Q000453:epidemiology*; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D016014:Linear Models; D015983:Selection Bias; D013997:Time Factors