Stroke severity and incidence might be stabilizing, or even decreasing over time in western countries, but this sure is not true for other parts of the world. But here is something to think about: with increasing survival, people will suffer longer from the consequences of stroke. This is of course especially true if the stroke occured at a young age.
To understand the true impact of stroke, we need to look beyond increased risk of secondaryevents. We need to understand how the disease affects day-to-day life, especially long term in young stroke patients. The team in Helsinki (HSYR) took a look at the pattern of young stroke patients returning to work. The results:
We included a total of 769 patients, of whom 289 (37.6%) were not working at 1 year, 323 (42.0%) at 2 years, and 361 (46.9%) at 5 years from IS.
That is quite shocking! But how about the pattern? For that we used lasagna plots, something like heatmaps for longitudinal epidemiological data. The results are above: the top panel is just the data like in our database, while the lower data has some sorting to help interpret the results a bit better.
The paper can be found here, and I am proud to say that it is open access, but you can as always just check my Mendeley profile.
Aarnio K, Rodríguez-Pardo J, Siegerink B, Hardt J, Broman J, Tulkki L, Haapaniemi E, Kaste M, Tatlisumak T, Putaala J. Return to work after ischemic stroke in young adults. Neurology 2018; 0: 1.
A new paper, this time venturing into the field of the so-called heart-brain interaction. We often see stroke patients with cardiac problems, and vice versa. And to make it even more complex, there is also a link to dementia! What to make of this? Is it a case of chicken and the egg, or just confounding by a third variable? How do these diseases influence each other?
This paper tries to get a grip on this matter by zooming in on a marker of cardiac damage, i.e. cardiac troponin T. We looked at this marker in our stroke patients. Logically, stroke patients do not have increased levels of troponin T, yet, they do. More interestingly, the patients that exhibit high levels of this biomarker also have high level of structural changes in the brain, so called cerebral white matter lesions.
But the problem is that patients with high levels of troponin T are different from those who have no marker of cardiac damage. They are older and have more comorbidities, so a classic case for adjustment for confounding, right? But then we realize that both troponin as well as white matter lesions are a left skewed data. Log transformation of the variables before you run linear regression, but then the interpretation of the results get a bit complex if you want clear point estimates as answers to your research question.
So we decided to go with a quantile regression, which models the quantile cut offs with all the multivariable regression benefits. The results remain interpretable and we don’t force our data into distribution where it doesn’t fit. From our paper:
In contrast to linear regression analysis, quantile regression can compare medians rather than means, which makes the results more robust to outliers . This approach also allows to model different quantiles of the dependent variable, e.g. 80th percentile. That way, it is possible to investigate the association between hs-cTnT in relation to both the lower and upper parts of the WML distribution. For this study, we chose to perform a median quantile regression analysis, as well as quantile regression analysis for quintiles of WML (i.e. 20th, 40th, 60th and 80th percentile). Other than that, the regression coefficients indicate the effects of the covariate on the cut-offs of the respective quantiles of the dependent variable, adjusted for potential covariates, just like in any other regression model.
Interestingly, the result show that association between high troponin T and white matter lesions is the strongest in the higher quantiles. If you want to stretch to a causal statement that means that high troponin T has a more pronounced effect on white matter lesions in stroke patients who are already at the high end of the distribution of white matter lesions.
But we should’t stretch it that far. This is a relative simple study, and the clinical relevance of our insights still needs to be established. For example, our unadjusted results might indicate that the association in itself might be strong enough to help predict post stroke cognitive decline. The adjusted numbers are less pronounced, but still, it might be enough to help prediction models.
The paper, led by RvR, is now published in J of Neurol, and can be found here, as well as on my mendeley profile.
von Rennenberg R, Siegerink B, Ganeshan R, Villringer K, Doehner W, Audebert HJ, Endres M, Nolte CH, Scheitz JF. High-sensitivity cardiac troponin T and severity of cerebral white matter lesions in patients with acute ischemic stroke. J Neurol Springer Berlin Heidelberg; 2018; 0: 0.
I wrote about this in an earlier topic: JLR and I published a paper in which we explain that a single relative risk, irrespective of its form, is jus5t not enough. Some crucial elements go missing in this dimensionless ratio. The RR could allow us to forget about the size of the denominator, the clinical context, the crude binary nature of the outcome.
So we have provided some methods and ways of thinking to go beyond the RR in an tutorial published in RPTH (now in early view). The content and message are nothing new for those trained in clinical research (one would hope). Even for those without a formal training most concepts will have heard the concepts discussed in a talk or poster . But with all these concepts in one place, with an explanation why they provide a tad more insight than the RR alone, we hope that we will trigger young (and older) researchers to think whether one of these measures would be useful. Not for them, but for the readers of their papers.
The paper is open access CC BY-NC-ND 4.0, and can be downloaded from the website of RPTH, or from my mendeley profile.
This is not about altmetrics. Nor is about the emails you get from colleagues or patients. It is about the impact of a certain risk factor. A single relative risk is meaningless. As it is a ratio, it is dimensionless, and without the context hidden in the numerator and denominator, it can be tricky to interpret results.
Together with JLR I have a paper coming up in which we plead to use one of the many ways one could interpret the impact of your results, and just simply go beyond the simple relative risk. This will be published in RPTH, the relatively new journal of the ISTH, where I also happen to be on the editorial board.
One of those ways it to report the population attributable risk: the percent of cases which can be attributed to the risk factor in question. It is often said that if we had a magic wand and would use it to make the risk factor disappear X% of the patient will not develop the disease. Some interpret this as the causal fraction, which is not completely correct if you dive really deep into epidemiological theory, but still, you get the idea.
In a paper based on PROSCIS data, with first author CM at the helm, we have tested several ways to calculate the PAR of five well known and established risk factors for bad outcome after stroke. Understanding what lies behind which patient gets has a bad outcome and which doesn’t is one the things we really struggle with, as many patient with well established risk factors just don’t develop a poor outcome. Quantifying the impact of risk factors, and arguably more importantly, ranking the risk factors is a good tool to help MDs, patients, researchers and public health officials to know where to focus on.
However, when we compared the PARs calculated by different methods, we came to the conclusion there is quite some variation. The details are in the table below, but the bottom line is this. It is not a good sign when your results depend on the method. Similar methods should get similar results. But upon closer inspection (and somewhat reassuring) the order of magnitude as well as the rank of the 5 risk factors stays almost similar.
So, yes, it is possible to measure the impact of your results. These measures do depend on the type of method you have used, which in itself is somewhat worrying, but given that we don’t have magic wand of which we expect to remove a fraction of the disease of up to 2 decimals precise, the PAR is a great tool to get some more grip on the context of RR.
The paper was published in PLOS One and can be found on their website or on my mendeley profile
PS This paper is one of the first papers with patient data in which we provided the data together with the manuscript. From the paper: “The data that support the findings of this study are available to all interested researchers at Harvard Dataverse (https://doi.org/10.7910/DVN/REBNRX).”
The German society for epidemiology has an annual teaching award, i.e. the “Preis für exzellente Lehre in der Epidemiologie”. From their website:
Mit der Auszeichnung sollen herausragende Leistungen oder überdurchschnittliches Engagement in der Lehre der Epidemiologie gewürdigt werden. (…) Preiswürdig sind innovative, originelle oder nachhaltige Angebote, ebenso wie ein besonders hoher persönlicher Einsatz für die Lehre.”
In short, anything goes in terms of format, innovation, personal commitment etc. However, there is a trick: only students can nominate you. So what happened? My students nominated me for my “overall teaching concept”. Naturally, the DGEpi wondered what that teaching concept actually was and asked me to provide some more information. So I took that opportunity and actually described what and why I teach, to see what the actual concept behind this all is. Here is the result.
The bottom line is simple: I think you learn the best not only by reading a book, but that you learn by doing, help in the organization and help teach in various epi related activities. You need to get exposed in several formats with different people. So I have helped set up a plethora of activities for the young student to learn epidemiology in different ways on different levels: read classics, discuss in weekly journal clubs, use popular scientific books in book clubs, but also organize platforms for discussion, interaction and inspiration (yes, I am talking about BEMC). The most important thing might be that students should learn the basics for epidemiology, even though they might not need that for that own research projects. This is especially true for medical students who want to learn about clinical research.
Last week I learned that the award in the end was awarded to me. Of course I am honored on a personal level, and this honors needs to be extended to my mentors. But I also take this award as an indication that the recent and increasing Berlin based epi-activities I helped to organize together with epi enthusiast at the IPH, iBIKE and QUEST did not go unnoticed by the German epidemiological community.
I will pick up the price in Bremen at the yearly conference of the DGEPI. See you there?
ICH is not my topic, but as we were preparing for the ESO Summerschool I explored the for me as yet untouched areas of stroke research. That brought me to this paper by Shoamanesh et al in JAMA Neurology which investigates a potential interaction between CMB and the treatment at hand in relation to outcome in patients with ICH. Their conclusion: no interaction. The paper is easy to read and has some at first glance convincing data, but then I realized some elements are just not right:
the outcome is not rare, still a logistic model is used to assess relative risk
interaction is assessed based on multiplicative interaction even while adding variables could lead to other estimates of interaction due to the non-collapsibility of the OR
the underlying clinical question of interaction is arguable better answered with an analyses of additive interaction.
I decided to write a letter to the editor. Why? Well, additionally to the methodological issues mentioned above, the power of the analyses was quite low and the conclusion of “no effect” based on a p value >0.05 with low power is in itself a problem. Do I expect that there is a massive shift in how I would interpret the data when they would have analysed the data differently? I don’t think so, especially as the precision of any quantification of additive interaction will be quite low. But that is not the main issue here: the way the data were presented does not allow the reader to assess additive interaction. So my letter was focused on that: suggesting to present the data in a slight different way, and then we can discuss whether the conclusions as drawn by the authors still holds. Then, and only then we get the full picture of the value of CMB in treatment decision. The thing is that we will then realize that the full picture is actually not the full picture, as the data are quite limited and imprecise and more research is required before strong conclusions can be drawn.
But the letter was rejected by JAMA Neurology because of space limitations and priority. I didn’t appeal. The same happened when I submitted an edited version of the paper to Neuro-epidemiology. I didn’t appeal. In the meantime, I’ve contacted the corresponding author, but he did not get back to me. So now what? Pubmed commons died. Pubpeer is, to my taste, too much focused on catching image frauds, even though they do welcome other types of contributions. I know my comments are only interesting for the methodologically inclined, and in the greater scheme of things, their value is limited. I also do understand space limitation when it comes to print, but how about online?Anyway, a lot of reasons why things happened why they happened. But somebody told me that if it was important enough to write a letter, it is important enough to publish it somewhere. So here I am, posting my initial letter on my own website, which almost certainly means that no single reader of the original paper will find out about these comments.
Post publication peer review ideas anybody?
The original paper can be found here, on the website of JAMA Neurology.
Peer review is not a pissing contest. Peer reviewing is not about findings the smallest of errors and delay publication because of it. Peer review is not about being right. Peer review is not about rewriting the paper under review. Peer review is not about asking for yet another experiment.
Peer review is about making sure that the conclusions presented in the paper are justified by the data presented and peer review is about helping the authors get the best report on what they did.
At least that what I try to remind myself of when I write my peer review report. So what happened when I wrote a peer review about a paper presenting data on the two hemostatic factors protein C and FVIII in relation to arterial thrombosis. These two proteins are known to have a direct interaction with each other. But does this also translate into the situation where a combination of the two risk factors of the “have both, get extra risk for free”?
There are two approaches to test so-called interaction: statistical and biological. The authors presented one approach, while I thought the other approach was better suited to analyze and interpret the data. Did that result in an academic battle of arguments, or perhaps a peer review deadlock? No, the authors were quite civil to entertain my rambling thoughts and comments with additional analyses and results, but convinced me in the end that their approach have more merit in this particular situation. The editor of thrombosis and hemostasis saw this all going down and agreed with my suggestion that an accompanying editorial on this topic to help the readers understand what actually happened during the peer review process. The nice thing about this is that the editor asked me to that editorial, which can be found here, the paper by Zakai et al can be found here.
All this learned me a thing or two about peer review: Cordial peer review is always better (duh!) than a peer review street brawl, and that sharing aspects from the peer review process could help readers understand the paper in more detail. Open peer review, especially the parts where peer review is not anonymous and reports are open to readers after publication, is a way to foster both practices. In the meantime, this editorial will have to do.