The puzzle of long-term morbidity after critical illness

Data continue to emerge demonstrating the poor quality of life of ICU survivors in the months and years following critical illness. In this issue of Critical Care, Cuthbertson and colleagues present new data on quality of life from a cohort of ICU survivors who were followed for 5 years. They found that survivors had poor physical quality of life and low quality adjusted life-years in comparison to age-adjusted norms, describing the long-term impact of critical illness as similar to a co-morbidity. Studies are now needed that seek to identify potentially modifiable factors both during and following an ICU admission to allow for eventual improvement in long-term morbidity. Such studies will likely need to incorporate extensive planning for data collection, as well as coordinated linkage with other available datasets that include substantial amounts of patient information from outside of the ICU

Visualisation of BioPAX Networks using BioLayout Express (3D).

BioLayout Express (3D) is a network analysis tool designed for the visualisation and analysis of graphs derived from biological data. It has proved to be powerful in the analysis of gene expression data, biological pathways and in a range of other applications. In version 3.2 of the tool we have introduced the ability to import, merge and display pathways and protein interaction networks available in the BioPAX Level 3 standard exchange format. A graphical interface allows users to search for pathways or interaction data stored in the Pathway Commons database. Queries using either gene/protein or pathway names are made via the cPath2 client and users can also define the source and/or species of information that they wish to examine. Data matching a query are listed and individual records may be viewed in isolation or merged using an 'Advanced' query tab. A visualisation scheme has been defined by mapping BioPAX entity types to a range of glyphs. Graphs of these data can be viewed and explored within BioLayout as 2D or 3D graph layouts, where they can be edited and/or exported for visualisation and editing within other tools

Predicting late anemia in critical illness

INTRODUCTION: Identifying critically ill patients most likely to benefit from pre-emptive therapies will become increasingly important if therapies are to be used safely and cost-effectively. We sought to determine whether a predictive model could be constructed that would serve as a useful decision support tool for the pre-emptive management of intensive care unit (ICU)-related anemia. METHODS: Our cohort consisted of all ICU patients (n = 5,170) admitted to a large tertiary-care academic medical center during the period from 1 July 2000 to 30 June 2001. We divided the cohort into development (n = 3,619) and validation (n = 1,551) sets. Using a set of demographic and physiologic variables available within six hours of ICU admission, we developed models to predict patients who either received late transfusion or developed late anemia. We then constructed a point system to quantify, within six hours of ICU admission, the likelihood of developing late anemia. RESULTS: Models showed good discrimination with receiver operating characteristic curve areas ranging from 0.72 to 0.77, although predicting late transfusion was consistently less accurate than predicting late anemia. A five-item point system predicted likelihood of late anemia as well as existing clinical trial inclusion criteria but resulted in pre-emptive intervention more than two days earlier. CONCLUSION: A rule-based decision support tool using information available within six hours of ICU admission may lead to earlier and more appropriate use of blood-sparing strategies