A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU

Abstract

The medical community is presently in a state of transition from a situation dominated by the paper medical record to a future situation where all patient data will be available on-line by an electronic clinical information system. In data-intensive clinical environments, such as intensive care units (ICUs), clinical patient data are already fully managed by such systems in a number of hospitals. However, providing facilities for storing and retrieving patient data to clinicians is not enough; clinical information systems should also offer facilities to assist clinicians in dealing with hard clinical problems. Extending an information system's capabilities by integrating it with a decision-support system may be a solution. In this paper, we describe the development of a probabilistic and decision-theoretic system that aims to assist clinicians in diagnosing and treating patients with pneumonia in the intensive-care unit. Its underlying probabilistic-network model includes temporal knowledge to diagnose pneumonia on the basis of the likelihood of laryngotracheobronchial-tree colonisation by pathogens, and symptoms and signs actually present in the patient. Optimal antimicrobial therapy is selected by balancing the expected efficacy of treatment, which is related to the likelihood of particular pathogens causing the infection, against the spectrum of antimicrobial treatment. The models were built on the basis of expert knowledge. The patient data that were available were of limited value in the initial construction of the models because of problems of incompleteness. In particular, detailed temporal information was missing. By means of a number of different techniques, among others from the theory of linear programming, these data have been used to check the probabilistic information elicited from infectious-disease experts. The results of an evaluation of a number of slightly different models using retrospective patient data are discussed as well.