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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). DESIGN: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. MEASUREMENTS AND 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.

journal_name

Intensive Care Med

journal_title

Intensive care medicine

authors

Schurink CA,Visscher S,Lucas PJ,van Leeuwen HJ,Buskens E,Hoff RG,Hoepelman AI,Bonten MJ

doi

10.1007/s00134-007-0728-6

subject

Has Abstract

pub_date

2007-08-01 00:00:00

pages

1379-86

issue

8

eissn

0342-4642

issn

1432-1238

journal_volume

33

pub_type

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