Current opinion in critical care
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Community-acquired pneumonia remains a prevalent and potentially life-threatening infection. In general, the disease is considered severe when inpatient care including ICU admission is required, and this often suggests a poorer prognosis. Severe community-acquired pneumonia continues to be an important subject of research from different perspectives, including assessment of illness severity, etiology, diagnostic tests, and treatment options. The aim of this descriptive review is to comment on the results of the relevant original articles in this area published since April 1, 2003. ⋯ The usefulness of inflammatory markers to assess the outcome of the disease is unclear. Data on severity scores are conclusive and different validated and simple predictive rules are available for the classification of patients into risk classes. Therapeutic strategies that have been investigated confirm the impact of adequate empiric antibiotic treatment on clinical outcome and the equivalence between short and long courses in the duration of therapy. A definitive beneficial effect of early administration of antimicrobials or the knowledge of the etiology of pneumonia on the clinical course of the disease has not been demonstrated.
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Decisions made in critical care are often complicated, requiring an in-depth understanding of the relations between complex diseases, available interventions, and patients with a wide range of characteristics. Standard modeling techniques such as decision trees and statistical modeling have difficulty in capturing these interactions as the complexity of the problem increases. ⋯ Simulation models provide useful tools for organizing and analyzing the interactions between therapies, tradeoffs, and outcomes.
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Curr Opin Crit Care · Oct 2004
ReviewBayesian analysis, pattern analysis, and data mining in health care.
To discuss the current role of data mining and Bayesian methods in biomedicine and heath care, in particular critical care. ⋯ With the increasing availability of biomedical and health-care data with a wide range of characteristics there is an increasing need to use methods which allow modeling the uncertainties that come with the problem, are capable of dealing with missing data, allow integrating data from various sources, explicitly indicate statistical dependence and independence, and allow integrating biomedical and clinical background knowledge. These requirements have given rise to an influx of new methods into the field of data analysis in health care, in particular from the fields of machine learning and probabilistic graphical models.
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Infections remain an important threat for critically ill patients, and the emergence of antibiotic resistance is increasingly hampering successful treatment. In this review, new aspects of the diagnosis and prevention of ventilator-associated pneumonia and of strategies of antibiotic use to limit the development and spread of resistance are described. ⋯ Recent developments in diagnosis, treatment, and prevention of ventilator-associated pneumonia and strategies to reduce emergence of antibiotic resistance have been reviewed. Whether changes in antibiotic policy will reduce the emergence of antibiotic resistance remains to be determined. In this area, methodologic problems that have been overlooked in many studies have been addressed recently. These issues must be clarified to provide reliable data on the effects of interventions in hospital settings.