title: A Bayesian decision-support system for diagnosing ventilator-associated pneumonia.
abstract: OBJECTIVE
  To determine the diagnostic performance of a Bayesian Decision-Support System (BDSS) for ventilator-associated pneumonia (VAP).
  
  
  METHODS
  A previously developed BDSS, automatically obtaining patient data from patient information systems, provides likelihood predictions of VAP. In a prospectively studied cohort of 872 ICU patients, VAP was diagnosed by two infectious-disease specialists using a decision tree (reference diagnosis). After internal validation daily BDSS predictions were compared with the reference diagnosis. For data analysis two approaches were pursued: using BDSS predictions (a) for all 9422 patient days, and (b) only for the 238 days with presumed respiratory tract infections (RTI) according to the responsible physicians.
  
  
  RESULTS
  157 (66%) of 238 days with presumed RTI fulfilled criteria for VAP. In approach (a), median daily BDSS likelihood predictions for days with and without VAP were 77% [Interquartile range (IQR) = 56-91%] and 14% [IQR 5-42%, p < 0.001, Mann-Whitney U-test (MWU)], respectively. In receiver operating characteristics (ROC) analysis, optimal BDSS cut-off point for VAP was 46%, and with this cut-off point positive predictive value (PPV) and negative predictive value (NPV) were 6.1 and 99.6%, respectively [AUC = 0.857 (95% CI 0.827-0.888)]. In approach (b), optimal cut-off for VAP was 78%, and with this cut-off point PPV and NPV were 86 and 66%, respectively [AUC = 0.846 (95% CI 0.794-0.899)].
  
  
  CONCLUSIONS
  As compared with the reference diagnosis, the BDSS had good test characteristics for diagnosing VAP, and might become a useful tool for assisting ICU physicians, both for routinely daily assessment and in patients clinically suspected of having VAP. Empirical validation of its performance is now warranted.
authors.affiliation: University Medical Center Utrecht, Division of Internal Medicine, Geriatrics and Infectious Diseases, Heidelberglaan 100, HP F.02.126, 3584 CX, Utrecht, The Netherlands.
authors.forename: Carolina A M
authors.initials: CA
authors.lastname: Schurink
authors.forename: Stefan
authors.initials: S
authors.lastname: Visscher
authors.forename: Peter J F
authors.initials: PJ
authors.lastname: Lucas
authors.forename: Henk J
authors.initials: HJ
authors.lastname: van Leeuwen
authors.forename: Erik
authors.initials: E
authors.lastname: Buskens
authors.forename: Reinier G
authors.initials: RG
authors.lastname: Hoff
authors.forename: Andy I M
authors.initials: AI
authors.lastname: Hoepelman
authors.forename: Marc J M
authors.initials: MJ
authors.lastname: Bonten
mesh_terms: D000368:Aged; D001499:Bayes Theorem; D015331:Cohort Studies; D020000:Decision Support Systems, Clinical / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D009426:Netherlands; D053717:Pneumonia, Ventilator-Associated / Q000175:diagnosis*; D011446:Prospective Studies