Do depressive symptoms predict the incidence of myocardial infarction independent of hopelessness?

With negative treatment trials, the role of depression as an aetiological or prognostic factor in coronary heart disease (CHD) remains controversial. We quantified the effect of depression on CHD, assessing the extent of confounding by coronary risk factors and disease severity. Meta-analysis of cohort studies measuring depression with follow-up for fatal CHD/incident myocardial infarction (aetiological) or all-cause mortality/fatal CHD (prognostic). We searched MEDLINE and Science Citation Index until December 2003. In 21 aetiological studies, the pooled relative risk of future CHD associated with depression was 1.81 (95% CI 1.53-2.15). Adjusted results were included for 11 studies, with adjustment reducing the crude effect marginally from 2.08 (1.69-2.55) to 1.90 (1.49-2.42). In 34 prognostic studies, the pooled relative risk was 1.80 (1.50-2.15). Results adjusted for left ventricular function result were available in only eight studies; and this attenuated the relative risk from 2.18 to 1.53 (1.11-2.10), a 48% reduction. Both aetiological and prognostic studies without adjusted results had lower unadjusted effect sizes than studies from which adjusted results were included (P<0.01). Depression has yet to be established as an independent risk factor for CHD because of incomplete and biased availability of adjustment for conventional risk factors and severity of coronary disease.

Motivation: Survival prediction of breast cancer (BC) patients independently of treatment, also known as prognostication, is a complex task since clinically similar breast tumors, in addition to be molecularly heterogeneous, may exhibit different clinical outcomes. In recent years, the analysis of gene expression profiles by means of sophisticated data mining tools emerged as a promising technology to bring additional insights into BC biology and to improve the quality of prognostication. The aim of this work is to assess quantitatively the accuracy of prediction obtained with state-of-the-art data analysis techniques for BC microarray data through an independent and thorough framework. Results: Due to the large number of variables, the reduced amount of samples and the high degree of noise, complex prediction methods are highly exposed to performance degradation despite the use of cross-validation techniques. Our analysis shows that the most complex methods are not significantly better than the simplest one, a univariate model relying on a single proliferation gene. This result suggests that proliferation might be the most relevant biological process for BC prognostication and that the loss of interpretability deriving from the use of overcomplex methods may be not sufficiently counterbalanced by an improvement of the quality of prediction. Availability: The comparison study is implemented in an R package called survcomp and is available from http://www.ulb.ac.be/di/map/bhaibeka/software/survcomp/. Contact: bhaibeka@ulb.ac.be Supplementary information: Supplementary data are available at Bioinformatics online.