“Research highlights” is a weekly round-up of research papers appearing in the print BMJ. We start off with this week’s research questions, before providing more detail on some individual research papers and accompanying articles.
- Does routine monitoring of viral load and CD4 cell count benefit people receiving antiretroviral therapy in sub-Saharan Africa?
- Is this routine monitoring cost effective?
- What are the benefits and harms of targeting intensive glycaemic control versus conventional glycaemic control in patients with type 2 diabetes?
- Do the reported predictive powers of cardiovascular biomarkers differ between observational studies and randomised controlled trials?
Should we monitor antiretroviral therapy in resource poor settings?
Getting life prolonging antiretroviral drugs into resource poor settings has been the priority; whether to and how to monitor the people taking them are questions that have received less attention. So far only one trial has investigated monitoring options, write Carlos de Rio and Wendy Armstrong.
Their commentary accompanies a randomised controlled trial and cost effectiveness analysis of three monitoring strategies for 1096 people receiving ART in Uganda. Mermin and colleagues and Kahn and colleagues compare three strategies: clinical monitoring alone; clinical monitoring and quarterly CD4 count; and clinical monitoring, CD4 count, and viral load.
For UK readers, the contrast with monitoring regimes in the UK will be stark. WHO previously decided against monitoring treatment in resource poor settings, based on the results of the previous trial. But these new studies show that CD4 monitoring reduced adverse outcomes, and that the strategy was cost effective. The addition of viral load testing did not provide a statistically significant benefit over and above CD4 count; and at five times the cost of CD4 testing, the cost effectiveness study does not support its use.
Now policy makers are left to ponder whether antiretroviral therapy should be monitored in a resource poor setting, such as Uganda, or whether the money would be better spent on simply widening access to the drugs.
Evidence for cardiovascular biomarkers: does effect size vary with study design?
“It is wrong to assume that a biomarker that is (causally) related to incidence of disease (aetiology) is necessarily (causally) related to progression (prognosis). Risk prediction models are easy to produce, hard to validate, and harder still to implement in clinical practice. And, thus far, evidence of impact on decision making or prognosis is nearly always lacking.” So said Harry Hemingway and colleagues two years ago in the BMJ (2009;339:b4184), in an article that called for, among other things, “clarity over the strength of evidence required for prognostic biomarkers to be considered ‘established’ or ‘useful’.”
It’s not just the strength of evidence that’s up for scrutiny, so is the production of that evidence. Is it better to test the predictive power of biomarkers using observational (cohort and case-control) studies or data from randomised controlled trials? This isn’t an esoteric question only for methodologists. It matters to clinicians and patients, because the wrong conclusions about biomarkers could lead to the wrong tests and even the wrong disease management.
Ioanna Tzoulaki and colleagues comprehensively reviewed meta-analyses of biomarkers that might predict cardiovascular disease, coronary heart disease, or cardiovascular mortality. Eligible meta-analyses had to include at least one observational study and at least one randomised controlled trial. They found 31 such meta-analyses, and concluded that, on average, cardiovascular biomarkers have less promising results in the evidence derived from randomised controlled trials than from observational studies. In the full version of the paper on bmj.com the authors discuss why this matters; for instance,“If one considered only data from randomised controlled trials, probably neither Lp(a) lipoprotein nor C reactive protein would be considered good biomarkers.”
It’s a cliche, but clearly, more research is needed. Tzoulaki and colleagues suggest that study registration and biobanking could be used to ensure that all datasets are assessed for each emerging biomarker, and not just those that are “in the fridge” of certain investigators. Editorialist Jan Vandenbroucke isn’t convinced, however.
Comparative assessment of implantable hip devices with different bearing surfaces
Art Sedrakyan and colleagues systematically appraised evidence about clinical outcomes after hip replacement with various bearing surfaces, including data from the US Food and Drug Administration.
Effectiveness of strategies incorporating training and support of traditional birth attendants on perinatal and maternal mortality
Confirming the findings of randomised controlled trials, this meta-analysis by Amie Wilson and colleagues shows that in developing countries, perinatal and neonatal deaths are significantly reduced with strategies incorporating training and support of traditional birth attendants.
Statins and prevention of infections
Findings of a meta-analysis by Hester van den Hoek and colleagues do not support the hypothesis that statins reduce the risk of infections, as suggested in observational studies.