More biomarkers for risk stratification of chest pain: Bigger, faster, better?

See also pp. 403–9 Patients attending the Emergency Department (ED) with chest pain present a challenging paradox to those responsible for their care. Those who are shown to have an acute coronary syndrome (ACS) have an increased risk of serious short-term adverse outcomes, such as death. However, the majority of patients presenting to the emergency room with chest pain do not have an ACS and have an uneventful prognosis.1 Overburdened EDs need to maintain the efficient throughput of their department. Most cardiology departments are ill-equipped to assess every patient presenting with chest pain and expect a selected cohort of patients for more intensive investigation and treatment. Clinicians wish to minimize the risk of adverse outcomes in their patients. Hospital administrators ask that all of these things happen, but with minimal cost. Although these challenges are by no means unique to patients presenting with chest pain, the size of the cohort and the potential for serious adverse outcomes have served to focus a significant amount of research in this area. Current guidelines recommend the approach of clinical assessment, observation, serial electrocardiography and biomarker testing and early provocative testing for ischaemia as a means of risk stratification of patients presenting acutely with chest pain.2 This approach, with a variety of minor modifications, has repeatedly been shown to accurately identify a cohort of patients presenting with chest pain, who are at a very low risk of short- and medium-term adverse outcomes.3 It should be recognized that this is a process of risk stratification and not a comprehensive diagnostic process. Nevertheless, it accurately identifies a group of patients in whom early discharge from the ED might be safely undertaken, with deferral of further assessment to the ambulatory setting. Two aspects of such an approach remain a challenge to many institutions: prolonged observation in the ED and the provision of early provocative testing. In this issue of the Journal, Macdonald and Nagree present a study that seeks to circumvent these problems by using an earlier and extended panel of biomarkers to identify patients at low risk of ACS.4 This approach is not novel, but well-conducted studies of this type, which include medium-term census of adverse outcomes, remain a welcome addition to the literature. Inclusion of myoglobin to the biomarker panel measured only 2 h following admission identified all patients who were subsequently found to have elevated troponin levels at 12 h. One patient, who was negative for both myoglobin at 2 h and troponin at 12 h, returned with a fatal ACS within 24 h. Regardless of the specific details of that case, it graphically illustrates the limitation of biomarkers alone to identify patients at high risk of ACS. Furthermore, the addition of early testing for myoglobin appeared to have a significant adverse effect on the positive predictive value of the biomarker panel, with over 60% of positive tests proving to be ‘false positive’, when compared with more prolonged assessment of the troponin level. This is not surprising. Putting the benefits of the time profile of myoglobin aside, these results indicate that we might have reached a point of diminishing returns from the addition of more biomarkers to the current risk stratification model. Given that the sensitivity and negative predictive value for short-term events of the current troponin plus electrocardiogram-based models are so close to 100%, any further improvement will have an inevitable detrimental effect on specificity. Although the absolute number of false positive tests might seem modest in the context of Macdonald and Nagree's study, given the size of the cohort that such an approach could potentially be applied to, it represents a significant problem that would make demonstration of cost-effectiveness a challenge. This problem is already exacerbated by the widespread use of biomarkers in relatively unselected cohorts of patients, some of whom will have a vanishingly small pretest probability of having coronary artery disease. Future approaches might include better use of simple predictive models based upon demographic data and our extensive knowledge of the epidemiology of coronary artery disease. Such an approach might be able to accurately and safely identify a cohort of patients at very low risk, who might be able to be discharged early and without further testing, and in whom further biomarker testing would be unlikely to perform with acceptable accuracy.5 At present, such models await validation in an Australasian population. For these reasons, it is too early to advocate the use of novel biomarkers in more accelerated protocols of risk stratification, until larger controlled studies with meaningful end-points confirm the safety and accuracy of such strategies. It would have been preferable if Macdonald and Nagree's study had integrated information from an approach that included early provocative testing for coronary artery disease, for example, exercise stress testing. Stress testing has been shown to provide incremental and cost-effective benefits beyond the use of electrocardiogram and troponin.6,7 It is arguable that this is the minimal ‘gold standard’ to which biomarker testing should be compared with, given that many studies have already used this approach to risk stratification. It might well be that the argument should be for more widespread access to such a simple, safe and relatively cheap procedure as exercise stress testing, rather than investing in additional biomarker testing.