Cardiovascular disease is the leading cause of death in women regardless of race or ethnicity.1 Coronary heart disease (CHD), including myocardial infarction (MI) and acute coronary syndrome (ACS), is associated with adverse outcomes in women. One potential reason women with CHD have such poor outcomes is that women are more likely to experience a myriad of non-chest-pain symptoms than are men, contributing to difficulty receiving a correct diagnosis and delay in receiving optimal treatment.2-5
Most studies on symptoms of CHD formulate a list of the participants' most frequently reported symptoms.6-8 This descriptive information on women's most commonly reported prodromal and acute symptoms associated with CHD is especially important because it provides vital information to assist clinicians in diagnosing this often elusive disease in women.5,7 Canto and colleagues9 conducted a thorough review of the literature on women's symptoms associated with ACS to provide additional guidance on women's symptom presentation. However, women, as men, seldom experience an individual symptom indicative of CHD but instead present with numerous symptoms.
Researchers in other specialties suggest that clusters of symptoms, as opposed to individual symptoms, are more clinically important.10-13 Leaders in symptom management research14-16 conceptualize symptom clusters as multiple symptoms that are related to each other and are experienced concurrently. Symptom clusters often share a common etiology. In addition, measurements of frequency and/or severity of symptoms within clusters typically correlate with each other.14
Symptom cluster analysis is especially prominent in the cancer literature, and several authors have performed a concept analysis related to symptom clusters.17,18 Others have reported on the usefulness of symptom clusters in developing a plan for symptom management of cancer pain and other related symptoms.11,12,17 Symptom clusters may also be useful in formulating diagnostic criteria and evaluating patterns of symptoms that typify different diseases and symptom progression.15
Unlike the cancer literature, there are few reported cluster analyses in the field of cardiovascular disease. Several researchers investigated clusters of CHD risk factors, such as in young adults in Ireland,19 patients with type 2 diabetes,20 or with metabolic syndrome.21 Another article based on cluster analysis focused on the knowledge of individuals at risk for developing MI and what symptoms they perceived might be indicative of MI.22 These researchers initially used Q methodology to develop a group of 49 potential MI symptoms. Participants then responded to these statements to identify the symptoms they believed were most and least likely associated with MI. Next, they conducted a factor analysis that resulted in 4 clusters of possible symptoms of MI. The 61 subjects in this study had not experienced an MI, so their responses were not based on actual symptoms. Although this information is useful for possible development and modification of educational interventions to promote early recognition of MI symptoms in the public, it is not useful in assisting clinicians in recognizing clusters of actual MI symptoms.
A recent study23 described characteristics of older adults (≥65 years old) who ranked 7 symptoms (chosen a priori by the research team) based on symptoms they experienced 1 week prior to hospitalization after their MI. Participants completed the questionnaire as soon as stable and while still hospitalized. The investigators analyzed symptoms using hierarchical cluster analysis to identify groups of patients who were most similar in the symptoms they experienced related to certain individual characteristics (demographics, comorbidities, cardiac risk factors, quality of life, and mood states). The 247 participants were divided into 3 groups according to reported symptoms that the authors described as clustering into discrete patterns. They labeled these clusters as the classic ACS symptom group, weary group, and diffuse symptom group. They described the classic ACS group (n = 53) as those with severe chest pain and moderate fatigue with significant differences in patient characteristics of less heart failure; the weary group (n = 72) as those reporting severe fatigue, sleep disturbance, shortness of breath, and moderate chest pain with characteristics of younger age (mean age = 74 years), diabetes, more likely to be treated with bypass surgery, lower quality of life and vitality scores, and higher fatigue, anxiety, depression, and anger scores. Lastly, the diffuse symptom group (n = 122) described low symptom intensity and mild shortness of breath and fatigue and had significantly different characteristics of older age and less hypertension. Fatigue emerged as a significant symptom in all 3 clusters with an overall prevalence of 76.1%, whereas shortness of breath was the second most prevalent symptom (61.5%). Chest pain was reported by only 56%, whereas the 4 other symptoms were reported by less than 50% of the participants (palpitations, sleep disturbance, nausea, and vomiting). They did not conduct analyses related to ethnicity or race because the sample was 92% white but reported no significant differences related to sex. Because the symptom instrument was limited to 7 symptoms, a full description of actual symptoms experienced by participants was likely restricted. In addition, participants could choose only chest pain and not other descriptors of chest discomfort, likely reducing reports of other discomfort sensations more commonly reported by women.2,9
Therefore, the state of the knowledge of women's CHD symptoms is expanding but remains in an early phase of development. Other studies have described women's symptoms by race,2,6 but no studies could be located that performed a cluster analysis of CHD symptoms reported by racially diverse women prior to and during MI.
We performed a secondary analysis to (a) generate naturally occurring symptom clusters based on women's prodromal and acute MI symptoms separately, (b) examine the association between women's characteristics and symptom clusters, and (c) describe the percentage of women who reported experiencing the same symptoms in both the prodromal and acute MI phases.
Sample and Description of Database
We performed a secondary analysis of retrospective self-reported data obtained by telephone survey from 1270 women (43% black, 42% white, 15% Hispanic) with a confirmed MI recruited from 15 geographically diverse sites located in 8 states. Human subject review committees approved the study at each of the sites. The purposes of the original studies were to describe and compare the prodromal and acute MI symptoms in white, black, and Hispanic women. A detailed description of the inclusion criteria, recruitment procedure, and sample description of women in this database is described elsewhere.2,24 Briefly, because of the retrospective nature of the study, all women in the database had to pass (achieve a score of ≤16) a 6-item cognitive screen appropriate for assessing cognition prior to telephone surveys.25 Interviews with Hispanic women were conducted in the language of their choice (Spanish or English). Because Hispanic women were fluent in either Mexican-Spanish or Caribbean-Spanish, research assistants who were fluent in both dialects administered the telephone surveys. Trained registered nurse research assistants, located at 1 site, conducted telephone surveys 4 months after the MI to allow sufficient time for women to recognize prodromal symptoms. Research assistants entered responses directly into an ACCESS database while conducting the 1-time 60-minute interview. Although telephone surveys have both advantages and disadvantages,26 this cost-effective data collection method allowed participation of a large number of racially and ethnically diverse women from a wide geographical area. This was essential for statistical and clinically meaningful comparisons of symptoms and generalizability of findings.
White women were older (67 [SD, 12] years) than minority women (63 [SD, 13] years) (P < .001), but minority women were not significantly different from each other (Hispanics, 64 [SD, 13] years; blacks, 63 [SD, 13] years) (P = .385). White women were better educated and reported higher incomes than minority women.
The original data were collected using the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey, which includes intensity and frequency measures of 33 prodromal (early warning intermittent symptoms that change after MI) and intensity of 37 acute MI symptoms (continuously present after onset until time of diagnosis). It also contains comorbidities/risk factors. A complete description of the symptom content of the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey and definitions of terms are described elsewhere.27 This instrument was developed based on qualitative interviews and incorporates symptoms and their descriptors based on women's actual accounts. It has been extensively pilot tested with white, black, and Hispanic women. Although women were offered the opportunity to add additional symptoms or descriptors during data collection, they did not identify any additional items.2
Women were queried about their prodromal symptom experience, including both frequency in days per week (less than monthly [0.167] to daily  and severity [0-3, 3 = most severe]) of symptoms, and about their acute MI symptoms and corresponding severity.2 Because acute symptoms were a 1-time event, there was no rating for frequency. Based on women's responses, we calculated a prodromal and acute symptom score for each woman by summing the individual symptom scores. Scores are calculated by multiplying the intensity by the frequency for each prodromal symptom and are based solely on the intensity for each acute symptom. Women also responded to 10 possible comorbidities/risk factor questions, such as presence of diabetes, hypertension, or hypercholesterolemia, and self-reported height and weight.
Data management and most analyses were performed using Stata version 10.0 (Stata Corp, College Station, Texas). Recursive partitioning was performed using the algorithm implemented in the Classification Trees for Multiple Binary Responses program (http://peace.med.yale.edu/pub/mcart).
Cluster analysis was used to group women with MI into similar configurations of prodromal and acute symptoms separately based on frequency and severity of the reported symptoms. Cluster analysis comprises a set of statistical methods, including K mean and K median, that seek to divide a population into naturally occurring homogeneous subgroups by minimizing the within-group differences while maximizing between-groups differences.28 Thus, our operational definition of clustering is a method of dividing objects into groups based on a measurement of similarity such that the most similar objects are grouped together while similarities between groups are minimized. In an effort to better describe cluster membership, bivariate analyses by χ2 test were used to examine the association between cluster assignment and women's characteristics and known risk factors independently, whereas multivariate analysis using multinomial (polytomous) logistic regression was used to examine the association between cluster assignment and characteristics/risk factors (age, race, body mass index [BMI], personal history of heart disease, family history of heart disease, diabetes, smoking, physical activity, hypercholesterolemia and hypertension) combined.29
Because of the possibility of complex interplay between patient characteristics, risk factors, and symptom clusters, recursive partitioning was used to explore these nonlinear interactions. Recursive partitioning is a multivariable analysis method useful in uncovering hidden patterns and groups within data. It relates an outcome (cluster membership) to a collection of independent predictors (patient characteristics/risk factors). The method partitions the data into homogeneous groups or nodes, while maximizing the differences between groups or nodes. The recursive algorithm used the Gini criterion for splitting nodes and a 10-fold cross-validation for pruning and tree validation. Results from recursive partitioning are routinely presented as a decision tree or a set of classification rules.30
Several clustering algorithms were evaluated after removing infrequently reported prodromal (<20%) and acute symptoms (<15%). Symptoms were removed if rarely reported and not significantly associated with any of the 6 risk factors. Sixteen of the 33 prodromal and 22 of the 37 acute symptoms were retained for analysis. The K-mean and K-median clustering methods yielded the most informative clusters, both producing similar clustering results. Therefore, only the results from the K-mean method are presented.
Three clusters of women were identified based on their reported prodromal symptoms and characteristics. Each cluster contained women with increasing frequency and severity of reported prodromal symptoms. Women in the first cluster reported the fewest and least severe prodromal symptoms, whereas women in the third cluster reported having the most prodromal symptoms that were of the greatest severity. The average prodromal score of every prodromal symptom was lowest in cluster 1 and highest in cluster 3. For example, the average score (intensity times frequency) of the most frequently reported prodromal symptom, very tired/unusual fatigue, was 4.78 for cluster 1, 10.26 for cluster 2, and 12.22 for cluster 3. The most frequently reported prodromal symptoms by cluster are listed in Table 1.
The first cluster (cluster 1) was composed of 552 women. None of the prodromal symptoms were reported by more than 70% of the women in cluster 1, and only 1 symptom, very tired/unusual fatigue, was reported by 48.6% of the women. No other symptom was reported by more than 40% of the women. Only 2 symptoms, very tired/unusual fatigue and sleep disturbance, were reported by more than 70% of the 435 women in cluster 2. In contrast, more than 70% of the 283 women in cluster 3 reported having the same 6 prodromal symptoms, and another 10 prodromal symptoms were reported by 40% to 69.9 % of them. Unlike women in the other 2 clusters, more than 40% of women in cluster 3 reported having chest pain/discomfort (59.4%). In summary, women in cluster 3 reported having the most prodromal symptoms, whereas a lower proportion of women in clusters 1 and 2 reported having prodromal symptoms.
Next, we assessed the association between women's characteristics and prodromal cluster membership to determine if demographic characteristics or known cardiovascular risk factors could be used to inform cluster membership. Six of 10 characteristics were strongly associated with cluster membership: race, age, BMI, personal history of heart disease, smoking status, and diabetes (Table 2).
Because of the possibility of complex interactions, we used multinomial (polytomous) logistic regression to examine patient characteristics simultaneously (Table 3). The women in cluster 3, who had the most number and greatest severity of prodromal symptoms, were 50% less likely to be white than black and 33% less likely to be older, after adjusting for the other factors in the model, than women in the other 2 clusters. Women in cluster 3 were also 92% more likely to be obese, 73% more likely to report having a personal history of cardiovascular disease, and 71% more likely to be smokers. Diabetes was no longer a significant predictor of cluster membership after accounting for the effect of the other factors in the model. Therefore, based on prodromal symptoms and their descriptors and the characteristics of the women reporting the symptoms in each cluster, we named the clusters as follows: cluster 1 as older, silent asymptomatic group; cluster 2 as diverse, mildly symptomatic group; and cluster 3 as young, minority, multiple distressing symptom group.
Recursive partitioning identified BMI as the most important factor in classifying prodromal symptoms, followed by race, smoking, age, and personal history of CHD (Figure 1). Approximately 41% of the young (aged <50 years), obese (BMI >29 kg/m2) black/Hispanic women were classified in cluster 3: young, minority, multiple distressing symptom group, whereas 37% were in cluster 2: diverse, mildly symptomatic group; and 22% of them were classified in cluster 1, older, silent asymptomatic group. Only 9.9% of normal-weight (BMI <29 kg/m2), nonsmoking, minority women without personal history of CHD were in cluster 3: young, minority, multiple distressing symptom group; in comparison, 63% of these women were classified in cluster 1, the older, silent asymptomatic group.
Three clusters of women were also identified based on their reported acute symptoms. The most frequently reported acute symptoms by cluster are listed in Table 4. As with the prodromal clusters, each sequential cluster contained women with increasing number of symptoms and a higher intensity of those symptoms (acute symptom score). Women in cluster 1 reported having the fewest and least intense acute symptoms, whereas women in cluster 3 reported having the most number and intense acute symptoms.
More women reported having acute symptoms than prodromal symptoms. Any chest pain/discomfort and shortness of breath were the most frequently reported acute symptoms. The average intensity (0 = none, 3 = severe) of these 2 symptoms, as with all the acute symptoms, was lowest in cluster 1 and most severe in cluster 3. For example, the mean for severity of chest pain/discomfort was 1.23 in cluster 1, 1.61 for cluster 2, and 2.01 for cluster 3. The 635 women with membership in acute symptom cluster 1 reported only 2 symptoms more than 40%: chest pain/discomfort (53.1%) and shortness of breath (44.4%). No other symptom was reported by more than 40% of women in this cluster. Approximately 75% of 385 women in cluster 2 reported experiencing shortness of breath with no other symptom being reported by more than 70% of women. In contrast, women in cluster 3 reported a large number of frequently occurring acute symptoms. More than 91% of women in cluster 3 (n = 248) reported experiencing shortness of breath, and more than 78% of them reported chest pain/discomfort. Forty to sixty-nine percent of women in cluster 3 also reported a variety of 12 other symptoms (Table 4).
Similarly, as for the prodromal symptoms, we assessed the association between women's characteristics and acute cluster membership to determine if demographic characteristics or known cardiovascular risk factors could be used to inform cluster membership. The same 6 characteristics found to be associated with prodromal clusters were also found to be associated with acute cluster membership: race, age, BMI, personal history of heart disease, smoking status, and diabetes (Table 5).
As before, we used multinomial (polytomous) logistic regression to examine patient characteristics simultaneously (Table 6). Compared with women in cluster 1, women in cluster 3 were 60% more likely to be younger, 84% more likely to have a personal history of heart disease, and 72% more likely to be smokers. Compared with women in cluster 1, women in cluster 2 were also younger and were more often smokers. Therefore, the same names for prodromal clusters 1 (older, silent asymptomatic group), 2 (diverse, mildly symptomatic group), and 3 (young, minority, multiple distressing symptom group) were retained for the corresponding acute clusters.
Recursive partitioning yielded age as the most important factor in classifying acute symptoms, followed by race, personal history of CHD, BMI, and smoking status (Figure 2). Approximately 40% of young black women were classified in cluster 3, whereas only 22% of them were in cluster 1. More than 74% of young black women reported chest pain/discomfort, whereas less than 50% of normal-weight, older, nonsmoking white women, without a personal history of CHD reported chest pain/discomfort. Sixty percent of all other women reported chest pain/discomfort, and 71% of normal-weight, older (aged >50 years) women without a personal history of CHD were in cluster 1.
Women Reporting the Same Symptoms in Both Prodromal and Acute MI Phases
Finally, we calculated the percentage of women who reported experiencing the same symptoms in both the prodromal and acute MI phases regardless of cluster membership. Of the 16 prodromal and 22 acute symptoms, 11 symptoms were assessed in both the prodromal and acute phases, and only these were retained for analysis. Of these 11 symptoms, 8 of them were reported in both the prodromal and acute phases by more than 50% of women. Two symptoms, any chest pain/discomfort and shortness of breath, had more than 80% agreement, whereas arms weak and heavy had more than 65% agreement. Although chest pain/discomfort was not a frequently reported prodromal symptom for women in any cluster group, at least 80% of the women who experienced it prodromally also reported it in the acute phase (regardless of cluster membership) (Table 7).
Clinicians use clinical reasoning to develop diagnostic hypotheses about patient symptoms, selecting the most specific and critical findings that support a hypothesis. It is in this way that cluster analysis becomes most clinically relevant as it organizes the most associated symptoms and correlates them with readily identifiable characteristics of those patients most likely to experience the symptom cluster. For example, younger black women in this study who had a high BMI, smoked, and had personal history of CHD tended to present with a variety of vague prodromal symptoms, such as unusual fatigue, sleep disturbance, anxiety, shortness of breath, and uncomfortable arm sensations (weakness, heaviness, tingling). These symptoms, if viewed individually, are often unrecognized as potentially related to early CHD and most difficult to diagnose. However, this cluster analysis provides an organization to "sort" symptoms into more manageable groups to provide valuable clues as to which women may require cardiovascular evaluation during the prodromal period.
During the prodromal phase, women in the older, silent asymptomatic group, who had the fewest and least severe symptoms, were more likely to be white, elderly, of normal weight, without personal history of CHD, and nonsmokers. Unlike the other cluster groups, no symptoms were reported by more than 70% of women in this cluster, and only 1 symptom, very tired/unusual fatigue, was reported by 48.6% of the women, making this group perhaps the most difficult to diagnose during the prodromal phase. The symptom, very tired/unusual fatigue, was also an important symptom for women in clusters 2 and 3 as more than 70% reported this symptom. Other researchers have identified unusual and profound fatigue as an early symptom of impending MI.7,23,31-34 However, fatigue as a single symptom is included in many differential diagnoses such as disorders of mood, infections, metabolic disorders, anemia, and rheumatologic disorders35 and not very helpful in isolation for diagnosing CHD conditions. To increase the usefulness of fatigue in developing the differential diagnosis of CHD for women, especially those in the older, silent asymptomatic group (cluster 1), we recommend clinicians thoroughly assess the impact of unusual fatigue on women's ability to complete their activities of daily living because women have previously reported this fatigue as profound and interfering with completing usual activities.32
Prodromally, women in both the diverse, mildly symptomatic group and the younger, minority, multiple distressing symptom group with the most CHD risk factors and a multitude of prodromal symptoms present a more typical picture of CHD. When fatigue is viewed within a cluster of symptoms (heart racing, shortness of breath, anxiety, and frequent indigestion), such as reported by women in these clusters, clinicians may be more likely to recognize CHD as a differential diagnosis. Other researchers have also identified these various symptoms as common CHD symptoms.23,27,36-38 However, if women and clinicians view each symptom individually, it is easier to attribute these symptoms to other problems such as gastrointestinal distress, anxiety, or stress. Clustering these symptoms provides clinicians with a more complete picture of the pattern of CHD presentation in these women.
For acute symptoms, women in the older, silent asymptomatic group tended to be white, less likely to smoke, have a history of cardiovascular disease, or to be obese. Proportionately, this cluster contained the largest number of Hispanic women (88/186 Hispanic women). In comparison, more than 67% of the women in the younger, minority, multiple distressing symptom group were minorities, primarily black. They were also the youngest group and had the greatest number of risk factors, including smoking, obesity, personal history of cardiovascular disease, and diabetes. Women in the diverse, mildly symptomatic group (cluster 2) were also primarily minority women and had more risk factors than those in the silent asymptomatic group (cluster 1), but less than those in the younger, minority, multiple distressing symptom group (cluster 3). It is interesting to note that, when viewing characteristics of the women in each cluster, the prevalence of risk factors was least in the older, silent asymptomatic group compared with those in the younger, minority, multiple distressing symptom group, similarly to the intensity and frequency of symptoms, which were least in the older, silent asymptomatic group and greatest in younger, minority, multiple distressing symptom group. Unexpectedly, diabetes mellitus was not a significant predictor of cluster membership.
Chest pain/discomfort was reported by 40% to 69.9% of the 283 women in cluster 3, the younger, minority, multiple distressing symptom group, but not reported by women in cluster 1 or 2 as a common symptom during the prodromal period and thus was not identified as a primary prodromal symptom. During the acute MI, chest pain/discomfort was identified by 70% to 100% of women in cluster 3 and by 40% to 69.9% of the 1022 women in clusters 1 and 2. Other researchers have similarly reported chest pain as the most frequent acute symptom, regardless of sex,3,9 but little is known about racially diverse women's symptoms.39 Interestingly, more than 25% of these diverse women did not report any chest pain/discomfort during their acute MI. Brieger et al3 reported that 8.4% of 20881 patients admitted for ACS presented without chest pain, 23.8% of whom were misdiagnosed as something other than ACS. Furthermore, they reported that patients without chest pain were significantly older, more likely women, and nonsmokers, similar to the women in the older, silent asymptomatic group (cluster 1) in this analysis. Other researchers report that lack of chest pain in women often leads to misdiagnosis.9,40-42 The only other acute symptom that was reported with chest pain/discomfort by 40% or more women in each cluster was shortness of breath and was reported by more than 70% of women in clusters 2 and 3. Shortness of breath was also a frequently reported prodromal symptom by women in these clusters. Lindgren et al23 recently performed a cluster analysis of 247 elderly male and female CHD patients and identified fatigue and shortness of breath as the most frequently reported individual symptoms 1 week prior to MI.
Younger black women had the highest number of risk factors and also the highest frequency and intensity of both prodromal and acute symptoms. It is well known that black women also have the highest morbidity and mortality associated with CHD and MI as compared with other women.1 It is possible that these women may be difficult to diagnose because of the sheer number and variety of symptoms reported. Cluster analysis may be more clinically relevant in interpreting their symptom pattern for diagnosis than for the other groups with fewer symptoms.
It is well recognized that women delay seeking treatment when experiencing acute MI symptoms.43,44 One reason women delay seeking treatment may pertain to symptoms experienced during the prodromal phase. Women describe that prodromal symptoms are intermittent and resolve spontaneously after rest with little, if any, lasting effects.7 This study indicates a high percentage of recurrence between prodromal and acute symptoms; that is, symptoms that presented prodromally are likely to reoccur acutely. Similarly, other researchers reported that 57% of 356 patients (44% women) admitted with ACS described their ACS symptoms as similar to symptoms they had experienced in the past.8 Although prodromal chest pain/discomfort was not a commonly reported prodromal symptom for women in this study, for those women who experienced it prodromally, 82% also reported it with their acute MI. The other prodromal symptom with the highest percentage of recurrence (83%) was shortness of breath, and 6 other symptoms were more than 50% likely to reoccur. Because of this high rate of agreement between prodromal and acute symptoms, we suggest that women may delay seeking treatment for acute symptoms because they expect that acute MI symptoms will resolve spontaneously, as they had in the past because prodromal symptoms previously resolved without intervention. Once symptoms do not resolve, women may rest, try home remedies, or continue to wait, contributing to delay in treatment seeking and poor outcomes.
Results from this study provide useful information to assist women to recognize potential CHD symptom clusters, especially in the presence of known CHD risk factors, and this knowledge may prompt them to seek treatment while still in the prodromal phase, perhaps delaying or preventing MI. We need future research to replicate our findings and to fully describe symptom clusters and the temporal process of these symptoms for maximum diagnostic usefulness.
This study offers, to date, the most complete picture of how racially diverse women's prodromal and acute MI symptoms naturally cluster and describes the associated characteristics of the women who comprise each cluster. This study also found that women's symptoms reported in the prodromal symptom phase were likely to reoccur with the acute event, offering information about symptom patterns and how women's symptoms may evolve over time. Identifying CHD symptom clusters and characteristics of women who experience specific symptom clusters offers insight into multisymptom complaints that are challenging for patients and clinicians to identify and diagnose.
Only women with a diagnosis of CHD and MI were included in the database selected for this secondary analysis. Having a control group would have strengthened this analysis. Furthermore, clustering, like modeling in general, fits the data on which they are developed better than an arbitrary data set. However, because we used various clustering methods that yielded similar results, confidence in the conclusions is enhanced. In addition, original data were self-reported data based on participant's recall that may have been inaccurate. To minimize this limitation, all participants passed a cognitive screen prior to answering questions. Because MI is a life-altering experience, women reported no difficulty differentiating between intermittent prodromal and unrelenting acute symptoms. Others have reported that persons have accurate recall of experiences surrounding profound life events.45,46
Because CHD is a disabling and deadly disease in women, prompt diagnosis and efficacious treatment are essential in improving women's CHD outcomes. Identifying naturally occurring clusters of CHD symptoms in women and characteristics of women who experience specific symptom clusters could potentially assist women to recognize these symptom clusters as significant and promptly seek medical attention and could increase clinicians' suspicion of CHD when symptom clusters are present, thus promoting prompt and appropriate diagnostic testing and treatment. In addition, describing women's acute MI symptom clusters should assist health personnel in emergency departments in making informed and appropriate triaging decisions for women who present with a variety of non-chest-pain symptoms but who may be experiencing an MI or ACS.
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