A Bayesian decision-support system for diagnosing ventilator-associated pneumonia

• Published in: Intensive care medicine 2007-08, Vol.33 (8), p.1379-1386
• Main Authors: SCHURINK, Carolina A. M, VISSCHER, Stefan, LUCAS, Peter J. F, VAN LEEUWEN, Henk J, BUSKENS, Erik, HOFF, Reinier G, HOEPELMAN, Andy I. M, BONTEN, Marc J. M
• Format: Article
• Language: English
• Subjects: Aged; Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy; Bayes Theorem; Biological and medical sciences; Cohort Studies; Decision making; Decision Support Systems, Clinical - statistics & numerical data; Emergency and intensive cardiocirculatory care. Cardiogenic shock. Coronary intensive care; Emergency and intensive respiratory care; Female; Humans; Information systems; Intensive care; Intensive care medicine; Intensive Care Units; Male; Medical sciences; Middle Aged; Netherlands; Nosocomial infections; Patients; Physicians; Pneumonia; Pneumonia, Ventilator-Associated - diagnosis; Probability; Prospective Studies; Variables; Ventilators
• ISSN: 0342-4642; 1432-1238
• DOI: 10.1007/s00134-007-0728-6

To determine the diagnostic performance of a Bayesian Decision-Support System (BDSS) for ventilator-associated pneumonia (VAP). 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. 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)]. 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.