Prediction of Bacteremia Using TREAT, a Computerized Decision-Support System

Paul, Mical; Andreassen, Steen; Nielsen, Anders Dencker; Tacconelli, Evelina; Almanasreh, N.; Fraser, Abigail; Yahav, Dafna; Ram, R; Leibovici, Leonard

doi:10.1086/503034

Cited by 69 publications

(40 citation statements)

References 26 publications

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“…Previously described BDSSs for infectious diseases other than VAP considered choosing empirical therapy for bacterial infections [21], predicting the pathogens of bacteraemia originating from the urinary tract and from other sites [22][23][24], and diagnosing community-acquired pneumonia (CAP) [25]. This BDSS for diagnosing CAP was retrospectively evaluated in 41,371 patients that had visited an emergency department and for which a discharge diagnosis was known.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2007

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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 cutoff 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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2007

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“…The present analysis included all CAP patients treated empirically with a combination of a b-lactam and a macrolide or with a b-lactam antibiotic alone, participating in the TREAT study [19,20], a system for balancing antibiotic treatment against development of drug resistance. Patients were enrolled as part of a two-phase study (observational and interventional) designed to evaluate the effectiveness of TREAT, a computerised decision support system for antibiotic treatment of common bacterial infections among in-patients (Clinical-Trials.gov Identifier: NCT00233376).…”

Section: Methodsmentioning

confidence: 99%

The need for macrolides in hospitalised community-acquired pneumonia: propensity analysis

Paul

Nielsen²,

Gafter‐Gvili³

et al. 2007

Eur Respir J

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on behalf of the TREAT Study Group ABSTRACT: The present study compared b-lactam macrolide (''combination'') therapy versus b-lactam alone (''monotherapy'') for hospitalised community-acquired pneumonia, using propensity scores to adjust for the differences between patients.A prospective multinational observational study was carried out. Baseline patient and infection characteristics were used to develop a propensity score for combination therapy. Patients were matched by the propensity score (three decimal point precision) and compared with 30-day mortality and hospital stay. The propensity score was used as a covariate in a logistic model for mortality.Patients treated with monotherapy (n5169) were older (mean¡SD age 70.6¡17.3 versus 65.0¡19.6 yrs), had a higher chronic diseases score and a different clinical presentation compared with patients treated with combination therapy (n5282). Unadjusted mortality was significantly higher with monotherapy (37 (22%) out of 169 versus 21 (7%) out of 282). Only 27 patients in the monotherapy group could be matched to 27 patients in the combination group using the propensity score. The mortality in these groups was identical, with three (11%) demises each. The multivariable odds ratio for mortality associated with combination therapy, adjusted for the propensity score and the Pneumonia Severity Index, was 0.69 (95% confidence interval 0.32-1.48).The benefit of combination therapy versus monotherapy cannot be reliably assessed in observational studies, since the propensity to prescribe these regimens differs markedly.

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“…The use of dynamic Bayesian networks as the basis for the construction of prognostic models has been explored in [34] with the particular application to prognosis of low-grade carcinoid tumours; evaluation results regarding three patients were also presented. Another Bayesian-network-based decision-support system, called TREAT, aiming at predicting bacteremia in patients is described in [28]. A randomised clinical trial showed improvement in the percentage of appropriate empirical antibiotic treatments when using the system [29].…”

Section: Related Workmentioning

confidence: 99%

Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare

Velikova

Scheltinga

Lucas

et al. 2014

International Journal of Approximate Reasoning

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Bridging the gap between the theory of Bayesian networks and solving an actual problem is still a big challenge and this is in particular true for medical problems, where such a gap is clearly evident. We argue that Bayesian networks offer appropriate technology for the successful modelling of medical problems, including the personalisation of healthcare. Personalisation is an important aspect of remote disease management systems. It involves the forecasting of progression of a disease based on the interpretation of patient data by a disease model. A natural foundation for disease models is physiological knowledge, as such knowledge facilitates building clinically understandable models. This paper proposes ways to represent such knowledge as part of engineering principles employed in building clinically practical probabilistic models. The methodology has been used to construct a temporal Bayesian network model for preeclampsia -a pregnancy-related disorder. The model is the first of its kind and an integral part of a mobile home-monitoring system intended for use in daily pregnancy care. We conducted an evaluation study with actual patient data to obtain insight into the model's performance and suitability. The results obtained are encouraging and show the potential of exploiting physiological knowledge for personalised decisionsupport systems.

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Prediction of Bacteremia Using TREAT, a Computerized Decision-Support System

Cited by 69 publications

References 26 publications

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

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

The need for macrolides in hospitalised community-acquired pneumonia: propensity analysis

Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare

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