title: Automated detection of healthcare associated infections: external validation and updating of a model for surveillance of drain-related meningitis.
abstract: OBJECTIVE
  Automated surveillance of healthcare-associated infections can improve efficiency and reliability of surveillance. The aim was to validate and update a previously developed multivariable prediction model for the detection of drain-related meningitis (DRM).
  
  
  METHODS
  Retrospective cohort study using traditional surveillance by infection control professionals as reference standard.
  
  
  METHODS
  Patients receiving an external cerebrospinal fluid drain, either ventricular (EVD) or lumbar (ELD) in a tertiary medical care center. Children, patients with simultaneous drains, <1 day of follow-up or pre-existing meningitis were excluded leaving 105 patients in validation set (2010-2011) and 653 in updating set (2004-2011).
  
  
  METHODS
  For validation, the original model was applied. Discrimination, classification and calibration were assessed. For updating, data from all available years was used to optimally re-estimate coefficients and determine whether extension with new predictors is necessary. The updated model was validated and adjusted for optimism (overfitting) using bootstrapping techniques.
  
  
  RESULTS
  In model validation, the rate of DRM was 17.4/1000 days at risk. All cases were detected by the model. The area under the ROC curve was 0.951. The positive predictive value was 58.8% (95% CI 40.7-75.4) and calibration was good. The revised model also includes Gram stain results. Area under the ROC curve after correction for optimism was 0.963 (95% CI 0.953- 0.974). Group-level prediction was adequate.
  
  
  CONCLUSIONS
  The previously developed multivariable prediction model maintains discriminatory power and calibration in an independent patient population. The updated model incorporates all available data and performs well, also after elaborate adjustment for optimism.
authors.affiliation: Department of Medical Microbiology, University Medical Utrecht, Utrecht, The Netherlands. M.s.m.vanmourik-2@umcutrecht.nl
authors.forename: Maaike S M
authors.initials: MS
authors.lastname: van Mourik
authors.forename: Karel G M
authors.initials: KG
authors.lastname: Moons
authors.forename: Wouter W
authors.initials: WW
authors.lastname: van Solinge
authors.forename: Jan-Willem
authors.initials: JW
authors.lastname: Berkelbach-van der Sprenkel
authors.forename: Luca
authors.initials: L
authors.lastname: Regli
authors.forename: Annet
authors.initials: A
authors.lastname: Troelstra
authors.forename: Marc J M
authors.initials: MJ
authors.lastname: Bonten
mesh_terms: D019540:Area Under Curve; D002557:Cerebrospinal Fluid Shunts / Q000009:adverse effects*; D015331:Cohort Studies; D003428:Cross Infection / Q000453:epidemiology*; D001330:Electronic Data Processing / Q000379:methods*; D062665:Epidemiological Monitoring; D006801:Humans; D008581:Meningitis / Q000453:epidemiology* / Q000209:etiology; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D012372:ROC Curve; D012189:Retrospective Studies