Sudden Cardiac Death: Epidemiology, Transient Risk, and Intervention Assessment

Despite recent progress in the management of cardiovascular disorders generally, and cardiac arrhythmias in particular, sudden cardiac death remains both a problem for the practicing clinician and a major public health issue [1-3]. Although the absolute number of sudden cardiac deaths has decreased in parallel with a reduction in overall cardiovascular mortality [1, 2], the proportion of all cardiovascular deaths that are sudden and unexpected remains constant at approximately 50% [2-4]. Information about the causes and mechanisms of the sudden cardiac death syndrome suggests that a specific reduction will require the development of new and multifaceted approaches. We have integrated data from several disciplines to generate a broad view of three components of this problem. These include the clinical epidemiology of sudden cardiac death and its application to preventive medicine; identification and control of transient risk factors that have proximate responsibility for the initiation of fatal arrhythmias; and analysis of therapeutic outcome data by means that provide appropriate interpretations of the potential benefits of interventions. Sources of data exist that address each of these issues, and we merged these disparate sets of data and concepts into a statement on current perspectives of and future approaches to the problem of sudden cardiac death. Applied Clinical Epidemiology Epidemiologic information that is clinically applicable to the problem of sudden cardiac death extends beyond the collection and analysis of mortality data across various demographic subgroups. The problem is dynamic and so is its epidemiology. Three areas of particular importance include the relation between absolute numbers of events and incidence of sudden cardiac death within defined population pools; the time dependence of risk; and the efficiency of therapeutic interventions. Risk in Population Subgroups When sudden cardiac death is measured as the absolute number of events per year in a defined population, it becomes clear that the highest-risk subgroups commonly cited in clinical studies (for instance, patients with low ejection fractions, patients with a history of heart failure, and survivors of out-of-hospital cardiac arrest) do not account for the majority of the events. This statement does not mean that commonly held perceptions about high-risk patients are wrong but rather that the predictive power of these high-risk characteristics applies only to small subgroups. The greater part of the risk for sudden cardiac death is hidden within the larger, more general population pools. These populations have smaller excesses in incidence but generate more events because of the larger size of the population bases from which they emerge. In Figure 1, the magnitude of the risk, expressed as incidence, is compared with the total number of events per year under six different conditions. The estimates are based on published epidemiologic and clinical data [4, 5]. When the more than 300 000 sudden cardiac deaths that occur annually among the unselected adult population in the United States [2, 3] are expressed as a fraction of the total adult population, the overall incidence is 0.1% to 0.2% per year. This calculation includes the 20% to 25% of sudden cardiac death victims whose cardiac arrest is the first clinical manifestation of previously silent or unrecognized heart disease [6-8] plus those with various degrees of increased risk identified by established clinical characteristics or risk factor profiles. When the more easily recognized, very-high-risk subgroups are removed from this population base, the calculated incidence for the remaining patients decreases, further amplifying the problem of identifying specific persons at risk among the general population. On the basis of these estimates, a preventive intervention designed for the general adult population must be applied to the 999 of 1000 people who will not have an event in order to reach and potentially influence the unidentified 1 of 1000 who will. Such limited efficiency impedes application of many active interventions and highlights the need for identifying specific markers of major increases in risk, even among groups with identified risk factors, in order to achieve more effective preventive efforts. Although escalation from a subgroup with several coronary risk factors in the absence of previous clinical events (risk, 1% to 2%/y) to higher-risk subgroups provides the ability to identify high-risk persons with progressively increasing power (Figure 1), the absolute number of persons who can be identified decreases with each escalation of risk. Thus, the challenge is not to focus more attention on the highest-risk clinical subgroups that are already clearly profiled but rather to develop new methods that will allow the identification of high-risk clusters within larger subgroups that have lesser degrees of increased risk. The first step toward this goal requires knowledge of the total number of sudden cardiac deaths within a specified population, the fraction of deaths that are sudden within that population, and the total mortality. For example, Kjekshus [9] analyzed a group of studies of heart-failure-related deaths, contrasting the probability of death and the ratio of sudden to total deaths as they relate to functional classification. In studies in which the mean functional class was between class I and class II, the overall death rate was relatively low, but 67% of the deaths were sudden [9]. In contrast, among studies with mean functional classifications close to class IV, the overall death rate was high, but the fraction of sudden deaths was only 29%. In the former circumstance, a therapeutic intervention targeted to all-cause mortality would be less efficient (that is, only a small fraction of the total population exposed to the intervention would have the potential to benefit from it), whereas, for the latter, efficiency would be high. For an intervention specific to the problem of sudden cardiac death, however, efficiency in the former case would be relatively high (67% of all deaths are sudden), whereas for the latter, efficiency for sudden cardiac death would be low because of the dominance of nonsudden deaths. Figure 1. Sudden cardiac deaths among population subgroups. Time Dependence of Risk The risk for death after surviving a major change in cardiovascular status is not linear over time for most conditions [5]. Survival curves for both sudden and total cardiac deaths show that the most rapid rate of attrition occurs during the first 6 to 18 months after an index event. By 24 months, the slope of the survival curve begins to approach the configuration of one describing a similar population that has remained free of the interposed major cardiovascular event (Figure 2). Further, the shapes of the curves are likely to be influenced by the magnitude of the increased risk after the index cardiovascular event. For instance, data from the Cardiac Arrhythmia Suppression Trial (CAST) [10, 11] show a linear attrition among the relatively low-risk, randomized placebo group during long-term follow-up of these patients with myocardial infarctions complicated by postinfarction arrhythmias but having relatively good mean ejection fractions. Data from the Multicenter Post-Infarction Program [12, 13], however, showed that subgrouping patients according to interactions between frequency of premature ventricular complexes (PVCs) and ejection fractions after they have survived acute myocardial infarction resulted in progressively increasing risk as the number and cumulative power of risk factors increased. The low-risk subgroups generated linear survival curves, whereas the added mortality in higher-risk subgroups tended to be expressed early (Figure 2). The time dependence of risk within the higher-risk groups limits the opportunity for effective intervention strategies to the early periods after the conditioning cardiovascular event. Curves showing these characteristics have been generated from among survivors of out-of-hospital cardiac arrest [14] Figure 2, from patients with new-onset heart failure, and from patients who have had high-risk markers after myocardial infarction [12, 15]. This property of risk must be integrated into strategies designed to intervene in such patients. Controlled intervention trials that allow enrollment of patients more than 12 to 18 months after the cardiovascular event to which the study is indexed might be confounded by a lower-than-anticipated event rate if such entrants are heavily represented in the study group. When the temporal property of risk is applied to individual patients, moreover, the probability of benefit from a secondary preventive intervention is also a function of time of implementation. Figure 2. Time-dependence of risk after cardiovascular events. Top. Middle. Bottom. Identification and Control of Transient Risk Factors The PVC hypothesis assumes that PVCs serve as triggers for the initiation of ventricular tachycardia or fibrillation (VT/VF) and that suppression of PVCs protects against sudden cardiac death by eliminating this triggering function. This concept gained popularity from early observations in coronary care units among patients with acute ischemic events [16, 17], and the view of PVCs as primary triggering events for fatal arrhythmias expanded and became ingrained into clinical dogma. However, the clinical application of the PVC hypothesis remained uncertain in clinical settings of chronic risk. Despite the existence of consistent data supporting chronic PVCs as a risk factor in long-term studies [12, 18-21], special circumstances are required to show the initiation of spontaneous life-threatening clinical arrhythmias by PVCs. Incidental ambulatory recordings have shown that the spontaneous onset of ventricular fibrillation may be preceded by a tendency toward increases in sinus rate and PVC frequency [22-2