Over the last three weeks, we have looked at challenges that can be found at several stages of clinical practice. We started by looking at the diagnostic challenge to determine whether a patient truly has a disease and finding the best set of tests to determine the probability of disease presence. We then considered a different question. Again, the problem of prediction. But this time, focusing on predicting the future health of the patient or the course of the disease. We have seen how clinical epidemiological researches place a vital role in providing evidence to help solve these demands in daily practice, but the role of evidence in decision-making doesn't end once you know the health status of your patient. Once you reach this stage, a new therapeutics challenge begins. Let's go back to the practice. You just received some routine blood tests results for one of your patients, a 55-year old diabetic woman and you notice that her total cholesterol levels were high. As she's also a heavy smoker, it occurs to you that she might be at risk of developing cardiovascular problems in the future. A prognostic model that you recently developed and validated has gradually been adopted as a routine prediction tool, and you decide to collect some more information from your patient, and use the model to predict the ten-year risk of cardiovascular disease. Upon doing this, you find that she has a 10% chance of having a cardiovascular event within the next ten years. Putting her in an intermediate risk group. Where do you go from here? It seems that your patient's risk is sufficiently high to warrant the use of some kind of prophylactic medication in order to reduce her chance of developing cardiovascular disease. And hopefully, prevent a future event. The range of medications are currently available, but it's not clear what the therapeutic option would be the best for your patient. You remember seeing some promising results for a new polypill consisting of aspirin, lipid-lowering and antihypertensive medications. Although still in development, it is clear that if the drug where to show the same kind of results in your patients as the results seen in earlier studies, it could potentially prevent serious cardiovascular events to a regiment that is easy to comply with. However, despite the high efficacy of the drug in initial studies, there's not enough evidence that this drug will be truly effective when given to patients at large and there is limited information about the severity of any side effects of the drug. It is clear that there are treatment options for the patient in a clinic right now, but future patients might benefit from the new polypill providing its truly effective in preventing cardiovascular event and has a minimal set of side effects. We need to gather more evidence about the effect of these drug and in order to do this, we will need to conduct a therapeutic study. So here, we have an example of a clinical problem at the stage of treatment decision-making. And as with other aspects of clinical epidemiology, before we begin the process of conducting any research, we need to decide exactly which problem we want to address and start thinking about the kind of data that we'll need to collect. There are different questions that we can ask about a new therapy. But in general, the two key features that researchers are interested in In studying efficacy and safety of the treatment. Efficacy is a measure of the strengths of an effect of a therapy on a target outcome. For example, if the new polypill has a high efficacy, we would expect who receive the drug to have a lower risk of developing cardiovascular disease than if they were to never have taken the drug. A closely related measure of the performance of an intervention is its effectiveness. Effectiveness has a broader definition than efficacy and it is easier to think about at a population level. If we apply an effective intervention to a group of people. Overall, we expect to see an overall reduction in the burden of disease. While an effective intervention in likely to have high efficacy, the converse isn't necessarily true. An intervention may have a strong positive effect on treating or preventing a disease. But if the side effects are burdening or the intervention is difficult for patients to adhere to, it's overall effectiveness within a population may be limited. These two terms are often used interchangeably, but it's worth remembering that there is a subtle difference. Depending on what your overall research aims are, you may be more interested in assessing one over the other. And this will likely effect some of the choices you make when designing your study, but what good is a treatment with high efficacy if it also causes harm to a patient? For a therapy to be used in routine practice, there must be sound evidence demonstrating its safety. Safety is a broad and subjective term and it can sometimes be difficult to judge when a therapy is not safe enough for use in practice. When we consider the vast range of medicines currently used in clinical practice, it is clear that the majority are associated with different kinds of side effects. There is often a balance between the severity of a disease and the acceptable risks associated with the therapy. For example, most surgical interventions pose some kind of risk of complications. But in most cases, the risks are outweighed by the benefits of the surgery. But in order to make these kinds of judgments, clinicians need sufficient evidence concerning the kinds of unintended effects that the therapy can have and this evidence should also be collected in an intervention study. Safety and efficacy are therefore, often seen as the two central considerations in intervention research, but other measures are becoming increasingly important. For example, cost are inevitably an important fact of when deciding whether to use one interventions for another. Even if the therapy is found highly effective, if the cost are too high, it may not be feasible to provide death therapy to all of the patient who need it. Therefore, there is a drive towards finding interventions that work just as well as those used in clinical practice, but at a reduced cost and researchers might choose to conduct cost-effectiveness studies to address these kinds of research problems. Another key consideration in designing an intervention study is how we want to measure efficacy, safety and other measures such as cost-effectiveness? Efficacy, especially can be considered to be a relative measure of performance and it only really holds any meaning when one therapeutic strategy is compared with another. We're often interested in the improvement in a patients health upon receiving a treatment compared to what happens in patients not receiving any treatment or receiving the usual standard of care. There are also situations where there are several available therapies and clinicians need to know whether one therapy is safer or more efficacious than another. We need to think carefully about which comparisons we aim to make in our study as some comparisons may be more informative than others when it comes to clinical practice. It's clear that intervention research is a broad term that encompasses a whole range of research aims. And often, several aims need to be addressed in a single study to get a clear picture of whether an intervention can have any value in clinical practice.
