The TREAT project: decision support and prediction using causal probabilistic networks

Leibovici, Leonard; Paul, Mical; Nielsen, Anders Dencker; Tacconelli, Evelina; Andreassen, Steen

doi:10.1016/j.ijantimicag.2007.06.035

Cited by 52 publications

(24 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Future studies should attempt to quantify the negative ecological impact of unnecessary and superfluous antibiotic treatment using the same outcome measures by which appropriate empirical treatment is measured, loss-of-life years. The loss to both the individual treated and society should be accounted for (49).…”

Section: Discussionmentioning

confidence: 99%

Systematic Review and Meta-Analysis of the Efficacy of Appropriate Empiric Antibiotic Therapy for Sepsis

Paul

Shani

Muchtar

et al. 2010

Antimicrob Agents Chemother

Self Cite

628

432

View full text Add to dashboard Cite

Quantifying the benefit of early antibiotic treatment is crucial for decision making and can be assessed only in observational studies. We performed a systematic review of prospective studies reporting the effect of appropriate empirical antibiotic treatment on all-cause mortality among adult inpatients with sepsis. Two reviewers independently extracted data. Risk of bias was assessed using the Newcastle-Ottawa score. We calculated unadjusted odds ratios (ORs) with 95% confidence intervals for each study and extracted adjusted ORs, with variance, methods, and covariates being used for adjustment. ORs were pooled using random-effects meta-analysis. We examined the effects of methodological and clinical confounders on results through subgroup analysis or mixed-effect meta-regression. Seventy studies were included, of which 48 provided an adjusted OR for inappropriate empirical antibiotic treatment. Inappropriate empirical antibiotic treatment was associated with significantly higher mortality in the unadjusted and adjusted comparisons, with considerable heterogeneity occurring in both analyses (I 2 > 70%). Study design, time of mortality assessment, the reporting methods of the multivariable models, and the covariates used for adjustment were significantly associated with effect size. Septic shock was the only clinical variable significantly affecting results (it was associated with higher ORs). Studies adjusting for background conditions and sepsis severity reported a pooled adjusted OR of 1.60 (95% confidence interval ‫؍‬ 1.37 to 1.86; 26 studies; number needed to treat to prevent one fatal outcome, 10 patients [95% confidence interval ‫؍‬ 8 to 15]; I 2 ‫؍‬ 46.3%) given 34% mortality with inappropriate empirical treatment. Appropriate empirical antibiotic treatment is associated with a significant reduction in all-cause mortality. However, the methods used in the observational studies significantly affect the effect size reported. Methods of observational studies assessing the effects of antibiotic treatment should be improved and standardized.Sepsis affects 1.1 to 2.4 per 1,000 people per year and 20 to 42% of these patients die in hospital, with these rates probably underestimating the contribution of hospital-acquired infections (3, 16, 61). Septicemia and pneumonia combined are the sixth most common causes of death in the United States (36). Antibiotic treatment for the first 24 to 48 h is largely empirical (i.e., provided without evidence on the causative pathogen or its susceptibilities), and it is common wisdom that appropriate empirical antibiotic treatment (i.e., matching the in vitro susceptibilities of the isolated pathogens) reduces mortality. Physicians thus strive to achieve appropriate empirical antibiotic treatment for inpatients with suspected infections, and many times this is at the cost of administering superfluous and unnecessary antibiotics. Such treatment is associated with resistance development (83, 97) and side effects with no benefit.Estimates of the potential benefit of appropriate em...

show abstract

Section: Discussionmentioning

confidence: 99%

Systematic Review and Meta-Analysis of the Efficacy of Appropriate Empiric Antibiotic Therapy for Sepsis

Paul

Shani

Muchtar

et al. 2010

Antimicrob Agents Chemother

Self Cite

628

432

View full text Add to dashboard Cite

show abstract

“…Computerized protocols have thus become more widespread as they can be applied to complex clinical problems to create patient-specific therapy instructions [5,6]. A cardiovascular (CVS) model could be integrated into the clinical decision making process by offering clinicians the possibility of not only assisting in diagnosing, but also providing a model-based means to test different therapeutic procedures and their likely effect on the patient.…”

mentioning

confidence: 99%

Prediction of hemodynamic changes towards PEEP titrations at different volemic levels using a minimal cardiovascular model

Starfinger

Chase

Hann

et al. 2008

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

(stroke volume). These results validate the general applicability of such a rule, which is not pig-specific, but holds over for all analyzed pigs. This rule enables physiological simulation and prediction of patient response. Overall, the prediction accuracy achieved represents a further clinical validation of these models, methods and overall approach to cardiovascular diagnosis and therapy guidance.

show abstract

“…the local distribution of pathogens, the susceptibility patterns of these pathogens, and the clinical presentation of the patient. 6 When using the TREAT program, physicians are asked questions about the patient's background, clinical status, travel history, and exposure to resistant microorganisms. Therefore the program can ensure a thorough examination of the patient and it can produce a resume page providing a comprehensive view of the patient case.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the decision support system for antimicrobial treatment, TREAT, in an acute medical ward of a university hospital

Arboe

Laub

Kronborg

et al. 2014

International Journal of Infectious Diseases

View full text Add to dashboard Cite

show abstract

The TREAT project: decision support and prediction using causal probabilistic networks

Cited by 52 publications

References 33 publications

Systematic Review and Meta-Analysis of the Efficacy of Appropriate Empiric Antibiotic Therapy for Sepsis

Systematic Review and Meta-Analysis of the Efficacy of Appropriate Empiric Antibiotic Therapy for Sepsis

Prediction of hemodynamic changes towards PEEP titrations at different volemic levels using a minimal cardiovascular model

Evaluation of the decision support system for antimicrobial treatment, TREAT, in an acute medical ward of a university hospital

Contact Info

Product

Resources

About