As expected, the probability of experiencing chest pain was high in all four classes (Class 1 = 94.9%, Class 2 = 91.2%, Class 3 = 74.3%, and Class 4 = 84.2%), although it was highest in the smallest group (Class 1). This is reassuring for the majority of patients experiencing ACS, although 44 (17%) patients in this study did not experience chest pain. Extrapolating to the population expected to experience a new or repeat episode of ACS this year (1.255 million; American Heart Association, 2010b
) means that over 213,000 Americans are at risk for delayed treatment or no treatment at all if signs or symptoms go unrecognized. The fact that Class 2, the Chest Pain Only group, contained only 58 patients is a concern. This represents only 22.6% of the sample. One may expect that these patients would be more likely to seek emergency care quickly because their decision-making is not complicated by multiple symptoms; however, the literature does not support this notion (Dracup et al., 2006
; Eagle et al., 2002
). Further study is required to determine if classes are predictive of time to treatment or short and long-term patient outcomes.
Subgroups of Individuals by Clinical Characteristics
It was hypothesized that patients would cluster on symptoms according to sex, age, race, diabetes status, diagnosis, and time to presentation in the ED following symptom onset. This hypothesis was not supported but it was consistent with a study of symptom clusters in patients with breast cancer in which Gwede, Small, Munster, Andrykowski, and Jacobsen (2008)
found that no demographic variables including age, race, education, marital status, employment, or household income classified high and low symptom burden groups. Our findings varied from the findings of Ryan et al. (2007)
in which cluster membership was predicted by sex, age, and race. The finding that the youngest patients clustered in the Heavy Symptom Burden group is concerning because the high probability of a very large number of symptoms may make it harder for patients to determine the significance of symptoms and may contribute to decision and treatment delay. However, this potential threat is mitigated by the fact that only 14.5% of patients comprised Class 1. A related concern is that older patients do not experience a heavy burden of symptoms, which may delay their decision to seek immediate care.
Class 2, the Chest Pain Only group, comprised the oldest patients (M
= 67.53 years). This is regarded as a clinically positive finding because findings from prior research indicate that older persons are likely to experience less pain during ACS (DeVon et al., 2008
). Ryan et al. (2007)
also found that patients who clustered in a group that did not have a high probability for any symptom were significantly older. Prior studies have shown that symptoms of ACS change with age and may present an obstacle to symptom recognition for elders (Canto et al., 2000
; Ĉulić, Eterović, Mirić, & Silić, 2002
Future research is warranted to determine if age is related to time to presentation and outcomes following treatment as has been reported in prior research on individual symptoms (Ryan & Zerwic, 2003
). Recently, Riegel et al. (2010)
found that elders ( ≥73 years) were less likely to recognize symptoms of heart failure compared to patients who were <73 years. The authors concluded that failure to recognize symptoms may be attributable to poor interoception, the manner by which sensory nerves process stimuli originating within the body.
Finally, groups 3 and 4 were differentiated by only two symptoms; sweating and shortness of breath. The presence of sweating may be of particular importance because shortness of breath accompanies other conditions that may mimic symptoms of ACS such as heart failure, pulmonary embolism, or anxiety. Ryan et al. (2007)
also noted that sweating may identify a subgroup of vulnerable patients.
There are limitations associated with exploratory research. We were unable to formulate hypotheses for grouping patients based on symptoms or clinical characteristics from the literature, which would have guided the design of the study and the choice of variables to measure. We examined a number of possible demographic and clinical confounders including age, sex, race, diabetes status, diagnosis, and time to presentation in the ED for symptoms identified from the ACS symptom literature. Additionally, possible patient or clinical characteristics that may aid in classifying subgroups of patients with symptom clusters during ACS remain unknown because of the paucity of ACS symptom cluster literature.
Use of a convenience sample could have led to bias because only those patients who presented to the ED were eligible for the study. Consequently, patients with ACS who experienced silent ischemia or did not seek care were not represented in this study. However, strategies such as recruiting 7 days a week over a 12-hour period (8 a.m. to 8 p.m.) may have contributed to a more representative sample of the population than would otherwise occur. Because of HIPAA guidelines, the participants had to be identified by nursing personnel or attending physicians. In most cases, patients were approached by their primary care nurse who asked permission for their names to be released to the researchers. Because all potential participants were referred by hospital staff, there is no way of knowing if selection bias occurred. It is possible that the investigators did not receive names of patients who met inclusion criteria.
Building knowledge in the science of symptom clusters is important for several reasons. Basic scientists can work to identify mechanisms underlying symptom clusters. Clinical investigators can study physiological and behavioral explanations for clusters, design, and test interventions to improve knowledge and symptom management, and examine outcome measures such as disease progression or major cardiovascular events. Healthcare providers can implement interventions and provide ongoing support to patients experiencing anginal symptoms who are at risk for ACS. Knowledge of symptoms and symptom clusters is important for patients experiencing ACS because the symptoms serve as a cue to action. Finally, knowledge and understanding of symptom clusters are also important for bystanders, first responders, and triage nurses because they are all key players in the path to appropriate and expeditious diagnostic testing.
Gaps in knowledge of symptom clusters in acute illness, including ACS, remain. Relationships between symptom triggers and symptom clusters remain largely unknown. Future researchers should include an examination of symptom clusters in population cohorts and a comparison of symptom clusters in patients who have confirmed ACS to those in whom ACS has been ruled out.