Blood sugar, cholesterol levels, or bone density: Is it possible to find out in trials whether a treatment has a benefit based solely on measured data?

Physiological or biochemical readings such as blood pressure or cholesterol levels can be determined quickly and simply. They are important in the field of medicine to see, for instance, whether a particular treatment is effective. Yet in researching treatments they are only of limited use: they usually cannot predict how a treatment will affect events that are critical to patients, like heart attacks or broken bones.

Laboratory data or physiological readings are often used in medical research to determine whether a particular treatment has a benefit. This may involve measuring, for example, whether a particular treatment lowers blood pressure, instead of measuring whether it lowers the number of heart attacks. The advantage of these readings is that they can usually be measured quickly and simply. It is already apparent after a short time whether a particular drug lowers blood pressure, for instance. Trials where only these data are taken will therefore need less time to do. It can otherwise take years to determine whether events like heart attacks can also be prevented through treatment.

Heart attacks are also quite rare, which means that a large number of people must be observed to be able to see clear differences between the treatment groups. In a trial on blood pressure, however, the changes in blood pressure can be measured for each individual participant, so that only few participants are required to make out an effect. This makes it easy to understand why it is so desirable to test whether a treatment helps based on simple laboratory data: a statement about the benefit of certain treatments can be made much earlier and with less difficulty.

When laboratory data or physiological readings are used in trials as substitute criteria for heart attacks, for example, they are called surrogate endpoints or surrogate markers (from the Latin surrogatum, meaning substitute). These are values that have been measured between the beginning of treatment and the occurrence of the disease or event.

Markers important to patients such as mortality, heart attacks, quality of life or the length of hospital stays are called patient-relevant endpoints. The term “patient-relevant” reflects the fact that it concerns issues that are important to patients, for instance how they feel, and whether they are able to plan their daily life as usual or live longer. The following table shows some examples of surrogate markers and the corresponding patient-relevant endpoints:

Surrogate marker	Patient-relevant endpoint
High cholesterol levels	Heart attack
Low bone density	Broken bone
Cardiac arrhythmia	Sudden cardiac death
High blood pressure	Stroke, heart attack

 

Surrogate parameters are often misleading

Abnormal laboratory data often correspond with a certain disease or condition. But sometimes a particular value will deviate from the norm even in a healthy person. This fact alone is enough to show that surrogate markers are not very reliable criteria for a treatment’s benefit. It may happen, for example, that one treatment influences the patient-relevant endpoint, but the surrogate endpoint does not change. On the other hand, a treatment can affect a surrogate parameter without influencing the patient-relevant endpoint. This is why most surrogate endpoints are not reliable and can be misleading when evaluating benefits of treatments.

The extra work and additional costs needed to do a trial are not good arguments for replacing patient-relevant endpoints with surrogate endpoints. But sometimes there are other reasons for doing so. Surrogate endpoints may be considered when dealing with the treatment of a serious disease for which there is no therapy so far. This requires that there be compelling evidence that the effect of treatment on the surrogate endpoint predicts how the treatment influences the patient-relevant end point. If this is the case it may be a good idea to initiate treatment, even though it is not absolutely certain how the patient-relevant end point will be affected. This was true of the first HIV drugs, for instance: trials showed that these drugs can significantly reduce the number of human immunodeficiency viruses detectable in the body. But there were no trials showing that as a result, fewer people develop AIDS or that mortality is reduced. Because there were no alternatives for treatment and an HIV infection progresses rapidly without treatment, the drug regulators approved these drugs anyway. Today it is recognized that this meant thousands of people with HIV were saved from an early death.

There are many examples in medicine that show how quickly a false conclusion can be arrived at by relying only on surrogate endpoints. A prime example of why it is necessary to study patient-relevant endpoints in trials is demonstrated by the story of a group of drugs for treating cardiac arrhythmia: Some people develop a specific form of cardiac dysrhythmia after a heart attack and have an increased risk of sudden cardiac death as a result. In order to normalize the heartbeat, different types of drugs were developed in the 1970s – they are called class 1c antiarrhythmic agents. By using electrocardiographs (ECGs, also sometimes called EKGs), trials showed that these drugs did successfully normalize the heartbeat. The ECG images were, however, just a substitute criterion for what was actually important, namely the risk of dying.

In the late 1980s, a group of researchers started a trial on class 1c antiarrhythmic agents. This so-called CAST trial studied not only the effect of the drugs on the heartbeat, but also how they affected mortality from sudden cardiac death. The results were sobering: Amongst the group of the participants who had used one of the three antiarrhythmic agents, the rate of sudden cardiac death was twice as high as in those who had taken a dummy drug (placebo) – although these agents were able to improve the rhythm of the heart in the ECG imaging. The CAST trial is now considered a prime example of why laboratory data and other substitute readings alone cannot be relied upon.

Another example of a misleading surrogate end point is bone density as an indicator for the risk of broken bones in post-menopause women: In the 1980s a trial was conducted to test whether sodium fluoride in addition to calcium can lower the risk of broken bones in women with osteoporosis. Examining the bones revealed that the bone density of women who took sodium fluoride increased. Yet they had more broken bones than did women who had taken only a placebo in addition to calcium. Sodium fluoride did increase bone density, but at the same time it changed the composition and quality of the bone material, which made the bones weaker.

Laboratory data have their place in medicine

Laboratory data and biochemical readings are anything but useless in medicine, though. They are needed to make a diagnosis, predict how a particular condition will progress or to check whether a treatment is working or is using the correct dose. A person who has type 1 diabetes, for example, will regularly monitor blood sugar levels in order to adjust his or her insulin dose. Laboratory exams and an ECG are required in order to diagnose a heart attack.

Laboratory data and biochemical readings become problematic when they are used as surrogates in trials to measure the benefit of treatment for patients. Caution must be exercised here: just because a drug lowers blood pressure, it does not automatically protect against heart attacks or strokes. This has to be tested in trials that examine not only blood pressure levels, but also the effect of the drug on cardiovascular disease.

This is also true of complementary medical interventions and dietary supplements. Trials of surgical and psychological treatments can also be misleading if they are testing the wrong questions.

Our information is based on trials done using patient-relevant endpoints

The publisher of this website, the Institute for Quality and Efficiency in Health Care (IQWiG), has a legislative mandate to assess the benefits and harms of medical interventions. In fulfilling this mandate, IQWiG analyzes trials with patient-relevant endpoints and thoroughly examines whether patients can actually benefit from a particular treatment. For this reason IQWiG does not refer to effectiveness in its assessments, but to the benefit and/or harm of a particular medical intervention. That treatment is effective does not automatically mean that it also has a benefit for patients.

IQWiG also provides consumers with unbiased and independent information about health issues on the website Informedhealthonline.org. The research that this information is based on must fulfill certain criteria and, for example, examine patient-relevant endpoints. You can find more information about the fundamental principles of evidence-based medicine here.

Author: German Institute for Quality and Efficiency in Health Care (IQWiG)

© IQWiG (Institute for Quality and Efficiency in Health Care) Last update of this website: 05/11/2012