Three new papers published – part II

In my last post, I explained why I am at the moment not writing one post per new paper. Instead, I group them. This time with a common denominator, namely the role of cardiac troponin and stroke:

High-Sensitivity Cardiac Troponin T and Cognitive Function in Patients With Ischemic Stroke. This paper finds its origins in the PROSCIS study, in which we studied other biomarkers as well. In fact, there is a whole lot more coming. The analyses of these longitudinal data showed a – let’s say ‘medium-sized’ – relationship between cardiac troponin and cognitive function. A whole lot of caveats – a presumptive learning curve, not a big drop in cognitive function to work with anyway. After all, these are only mild to moderately affected stroke patients.

Association Between High-Sensitivity Cardiac Troponin and Risk of Stroke in 96 702 Individuals: A Meta-Analysis. This paper investigates several patient populations -the general population, increased risk population, and stroke patients. The number of patients individuals in the title might, therefore, be a little bit deceiving – I think you should really only look at the results with those separate groups in mind. Not only do I think that the biology might be different, the methodological aspects (e.g. heterogeneity) and interpretation (relative risks with high absolute risks) are also different.

Response by Siegerink et al to Letter Regarding Article, “Association Between High-Sensitivity Cardiac Troponin and Risk of Stroke in 96 702 Individuals: A Meta-Analysis”. We did the meta-analysis as much as possible “but the book”. We pre-registered our plan and published accordingly. This all to discourage ourselves (and our peer reviewers) to go and “hunt for specific results”. But then there was a letter to the editor with the following central point: Because in the subgroup of patients with material fibrillation, the cut-offs used for the cardiac troponin are so different that pooling these studies together in one analysis does not make sense. At first glance, it looks like the authors have a point: it is difficult to actually get a very strict interpretation from the results that we got. This paper described our response. Hint: upon closer inspection, we do not agree and make a good counterargument (at least, that’s what we think).

New article: Lipoprotein (a) as a risk factor for ischemic stroke: a meta-analysis

source: atherosclerosis-journal.com

Together with several co-authors, with first author AN in the lead, we did a meta analyses on the role of Lp(a) as a risk factor of stroke. Bottomline, Lp(a) seems to be a risk factor for stroke, which was most prominently seen in the young.

The results are not the only reason why I am so enthusiastic by this article. It is also about the epidemiological problem that AN encountered and we ended up discussing over coffee. The problem: the different studies use different categorisations (tertiles, quartiles, quintiles). How to use these data and pool them in a way to get a valid and precise answer to the research question? In the end we ended up using the technique proposed used by D Danesh et al. JAMA. 1998;279(18):1477-1482 that uses the normal distribution and the distances in SD. A neat technique, even though it assumes a couple of things about the uniformity of the effect over the range of the exposure. An IPD would be better, as we would be free to investigate the dose relationship and we would be able to keep adjustment for confounding uniform, but hey… this is cool in itself!

The article can be found on pubmed and on my mendeley profile.