Anna Kauppi awarded Fellowship to join Lab
At the beginning of February Anna Kauppi will join the lab as a visting fellow, thanks to a fellowship from the Wenner-Gren Foundation in Sweden. Anna gained her PhD in the Department of Chemistry, University of Umea, in northern Sweden, using multivariate statistical modelling to predict biological and chemical properties. She brings this experience to our team, where she will be instrumental in launching the cadUK project.
Our laboratory has collaborated for almost ten years with our colleagues at Umea (including Prof. Henrik Antti and Prof. Svante Wold) who are world-leaders in the development and application of projection-based multivariate modelling tools. Anna's arrival takes that collaboration ones step further, and expands our capabilities in this important area which underpins our efforts in profiling diagnostics, and is an important component of the cadUK project.
cadUK is an ambitious ten-year programme designed to use multivariate statistical modelling to identify the best diagnostic paradigm for coronary heart disease. At present, the majority of heart attacks in the UK occur among individuals not presently diagnosed with coronary heart disease. Furthermore, the rate of heart attack among those attending a tertiary referral centre for extensive (and expensive) work-up is only 10-fold higher than in the general population. This represents a relatively poor enrichment achieved by the present diagnostic paradigm (in this context, a 'diagnostic paradigm' is a series of one or more tests performed in a particular order, with pre-defined cut-offs for positive and negative outcomes). cadUK aims to determine the optimum sequence and cut-offs for the tests currently in clinical use to achieve the best diagnostic performance. cadUK also has a health economic component, and aims to address the question of the best resource utilisation in heart disease diagnosis and treatment.
The first phase of cadUK involves analysis of the MaGiCAD and PUMA datasets which we have collected previously. These studies have collected data from more than 6,000 people and represent one of the most comprehensive data resources for analysis of diagnostic performance ever collected.
Related links:
Details of the MaGiCAD data resource can be found at the study website.
The PUMA study also has its own website.
One of the first tasks on initiating the cadUK study will be to establish the project website. Details of the site will be posted here.
Further details of research at the University of Umea can be found here.