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International Journal of Nursing Practice 2015; 21: 166–174
RESEARCH PAPER
Cluster dyads of risk factors and symptoms are associated with major adverse cardiac events in patients with acute myocardial infarction Seon Young Hwang RN PhD Associate Professor, College of Nursing, Hanyang University, Seoul, South Korea
JinShil Kim RN PhD Associate Professor, College of Nursing, Gachon University, Incheon, South Korea
Accepted for publication May 2013 Hwang SY, Kim JS. International Journal of Nursing Practice 2015; 21: 166–174 Cluster dyads of risk factors and symptoms are associated with major adverse cardiac events in patients with acute myocardial infarction The purpose of this study was to examine the cluster dyads of risk factors and symptoms and their impact on the incidence of 12 month major adverse cardiac events (MACEs) among patients with first-time myocardial infarction (MI). In a descriptive study, a total of 522 patients completed semi-structured interviews for data on risk factors and symptoms. Patients were followed for 12 months to determine MACEs. Latent class cluster analysis was performed to identify risk factor clusters and symptom clusters. Logistic regression analysis was performed to determine the impact of cluster dyads on 12 month MACEs. There were 436 event-free survivors and 86 patients with MACEs for 12 months. Ten risk factors and 14 symptoms were clustered into two (dyslipidemia/smoking, hypertension/diabetes dominant) and three (typical, multiple, atypical) memberships, respectively. Six cluster dyads which were generated based on the association between risk factors and symptom clusters were a significant predictor of 12 month MACEs, with the incidence occurring three times higher in a dyad of hypertension/diabetes-and-atypical symptoms than a dyad of dyslipidemia/smoking-and-typical symptoms (odds ratio = 3.10, P = 0.01), after adjustment for age, gender and a type of MI diagnosis. The information on cluster dyads suggests that health-care providers need to consider both risk factors and symptoms at hospital presentation for risk stratification to prevent adverse outcomes. Key words: acute myocardial infarction, cardiovascular disease, major adverse cardiac events, risk factors, symptom cluster.
INTRODUCTION Acute myocardial infarction (MI) has increased in prevalence over the past decade in South Korea, as a result
Correspondence: JinShil Kim, PhD, RN, Associate Professor, Gachon University, College of Nursing, 191 Hambakmoero, Yeonsu-gu, Incheon, South Korea; telephone (82) 032-820-4229; fax (82) 032820-4201; Email: [email protected] © 2014 Wiley Publishing Asia Pty Ltd
of longer exposure to cardiovascular diseases (CVDs).1 Mortality rates of patients with CVDs are high, ranking second in causes of death in Korea.1 Among cardiovascular deaths, approximately one in four dies of MI, with a rapid increase from 18% in 2001 to 24% in 2010.1 Cardiovascular risk stratification of individuals according to risk factors and symptoms and providing primary preventative intervention accordingly might beone strategy to decrease the prevalence and mortality of MI.2 doi:10.1111/ijn.12241
Risk factor-and-symptom cluster dyads
Major risk factors for developing MI include cigarette smoking, elevated blood pressure, dyslipidemia and diabetes mellitus.3 Clustering of these risk factors is likely to have synergistic effects on CVDs, which is greater than the sum of the risks associated with each abnormality.3,4 In a nationwide Korea Acute Myocardial Infarction Registry study in which 13 133 MI patients participated, patients reported current cigarette use most often (57%), followed by hypertension (49%), diabetes mellitus (28%), and a history of cardiac events or heart attack (26%).5 Determination of the risk clusters should be investigated, with preventive efforts targeting groups of people at greater risk for MI, rather than management of each risk factor. In addition, early recognition of symptoms associated with an impending event, which might assist people in seeking prompt medical attention, is needed.6 The most typical and primary symptom reported by MI patients is chest pain. A single symptom experience of chest pain was reported in 35% of patients only, whereas symptom clusters consisting primarily of chest pain accompanied by other symptoms, such as indigestion or epigastric discomfort, were reported in 57% of patients.7 In conjunction with research on single symptoms in patients with MI, symptom cluster, defined as ‘multiple concurrent symptoms that are related and might have a common etiology’, should be determined.8 Understanding of an individual symptom experience or symptom cluster prior to or during the acute period of MI and one’s underlying risk factors is imperative, thereby allowing health-care providers to conduct further evaluation and to establish a plan for individual care. Information on the association between risk factors and symptom clusters might assist health-care providers to detect patients early at high risk for an impending episode of MI. It could also serve as a basis for educational provision and clinical guideline for primary and secondary prevention targeting high-risk cluster groups. Compared with the well documentation for each of the cardiovascular risk factors and symptom cluster,9–11 the cluster dyads of risk factors and symptoms remain unknown. Further, symptom experiences of patients with CVDs and their synergistic influence on clinical outcomes are well known,12,13 yet there is a lack of knowledge on whether the dyadic clusters are associated with prospective clinical outcomes of major adverse cardiac events (MACEs), including revascularization, re-infarction, and cardiac or non-cardiac deaths among
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MI patients. An empirical study is needed to provide a basic data for clinical guideline aimed at preventing cardiac events and facilitating prioritized care for patients with MI. To address these concerns, the aims of this study were to: (i) identify risk factor clusters and symptom clusters; (ii) determine the dyads of risk factors-and-symptoms clusters; and (iii) examine whether the cluster dyads of risk factors and symptoms predict the incidence of 12-month MACEs after the acute event among first-time MI patients.
METHODS Design and procedure This was a prospective, descriptive study, in which risk factors and symptom clusters were evaluated among firsttime MI patients who underwent percutaneous coronary intervention. Patients were recruited from a universityaffiliated medical centre after approval of the study by the University Institutional Review Board. Data were obtained from a research project, entitled ‘Treatmentseeking behaviors and factors associated with pre-hospital delay in MI patients’. Some of the data of this study were used in previously published studies.14,15 Prior to the conduction of face-to-face interviews, MI patients were contacted and each person signed a written informed consent statement. Patients completed semi-structured interviews to obtain data on risk factors and symptoms occurring during the acute phase of MI. An abstraction form developed by the principal investigator was used for retrieval of 10 risk factors and 14 acute and associated symptoms. These risk factors and acute and associated symptoms were adopted from the evidence of clinical studies and an extensive review of the literature conducted by the American Heart Association.3 Semi-structured interviews were conducted at a designated area for research in a hospital where patients received the care. Baseline data were collected from November 2007 through March 2010 by research nurses with a degree in graduate nursing trained to conduct semi-structured interviews with a written script. Each patient’s medical record was also reviewed for collection of demographic and clinical characteristics, including medications and laboratory data. The interviews took approximately 20–30 min to complete. Patients were followed for 12 months to collect data on MACEs, with confirmation by electronic medical record review. © 2014 Wiley Publishing Asia Pty Ltd
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Participants The study population consisted of a convenience sample of 522 MI patients. Eligibility criteria for the study were: (i) hospitalized patients who underwent percutaneous coronary intervention for first-time MI with or without ST segment elevation; and (ii) aged 18 years or older at the time of informed consent. Patients were excluded if they: (i) had conditions that would confound symptoms experienced with MI, such as heart failure, terminal cancer or renal failure; or (ii) have documented cognitive impairment, such as dementia and/or stroke or psychiatric diagnosis, that precluded symptom recognition or the ability to engage in a face-to-face interview.
Statistical analysis Latent class cluster analysis, using Latent Gold software, version 3.016 was performed to address aim (i), which was to divide MI patients into separate homogeneous subgroups of risk factors and symptoms. Ten risk factors and 14 symptom indicators were used as variables for definition of clusters. The values of Bayesian Information Criterion, Akaike Information Criterion (AIC), and AIC3 log likelihoods were used to identify the best model fits for risk factor and symptom clusters. Wald tests were used for determination of overall statistical significances of the sets of parameter estimates of the cluster models. Thereby, a two-cluster model for risk factors (Wald = 26.37, P < 0.001) and a three-cluster model for symptoms (Wald = 30.14, P < 0.001) were adopted for further statistical analyses. Cluster memberships of the two- and three-cluster models were then converted to a data set using Statistical Package for the Social Sciences, version 21 (IBM Corp., Korea). Then bivariate analyses were performed to fully describe cluster membership, using χ2 tests, t-tests or one-way analysis of variance with Scheffé’s tests. For example, one-way analyses of variance were performed to determine group differences in age, pre-hospital delay, or hospital and cardiac care unit stays among six cluster dyads. To address aim (ii), which was to determine cluster dyads, based on associations between risk factor clusters and symptom clusters, χ2 test statistics were computed. To address aim (iii), which was to determine whether the cluster dyads of risk factors and symptoms predict the incidences of 12 month MACEs after the acute event, χ2 test and logistic regression analysis were performed with adjustment for age, gender and a type of MI diagnosis. A total sample of 522 was an enough sample size to perform © 2014 Wiley Publishing Asia Pty Ltd
a logistic regression analysis using G*Power program,17 which had a power of 0.85, alpha of.05 and a medium effect size of 0.30.
RESULTS Sample characteristics A total of 522 patients completed semi-structured interviews. The mean age of patients was 63 years (63.13 ± 12.32, range 26–89 years). Seventy three per cent of patients were men and 40% had an elementary school education or lower. Approximately half of patients had a diagnosis of MI with ST segment elevation (51%). The major risk factors reported by patients were hypertension (49%), dyslipidemia (56%), current smokers (40%), diabetes (32%), and family history of CVDs, including hypertension, stroke or ischaemic heart disease (21%). The most frequent symptom experienced by patients was chest pain or discomfort (86%), and symptoms reported by at least one third of patients included cold sweat, shortness of breath, nausea/vomiting, and weakness/ fatigue. The average number of symptoms experienced was 3.72 ± 1.83 during the cardiac event.
Risk factor clusters and symptom clusters Using latent class cluster analysis, two distinct clusters of risk factors and three symptom clusters were identified (Table 1). The first risk factor cluster was composed of 282 MI patients (54%): dyslipidemia and smoking were dominant risk factors, both reported by 67% of patients. The second risk factor cluster was composed of 240 patients (46%): hypertension and diabetes mellitus were dominant risk factors, reported by 78% and 53% of patients, respectively. The largest symptom cluster consisted of 309 MI patients (59%) (Cluster 1), and the following two clusters, respectively, included 145 (28%, Cluster 2) and 68 (13%, Cluster 3) patients. The most frequent symptom of Cluster 1 was chest pain, which was reported by all patients. Patients in Cluster 2 experienced many classic MI symptoms and complained of the most acute symptoms, including chest pain (95%), left (90%) or right (50%) shoulder pain, cold sweating (61%), and nausea/ vomiting (40%). Unlike patients in the two clusters, only one patient in Cluster 3 reported chest pain and greater than half reported shortness of breath and weakness or fatigue. Clusters were then labelled as typical, multiple and atypical symptom clusters, respectively.
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Table 1 Risk factor cluster and symptom cluster (N = 522) Risk factor variables No. (%) of patients
Symptom variables
Cluster 1 Cluster 2 n = 282 (54%) n = 240 (46%) Hypertension 69 (24) Diabetes 39 (14) Current smoking 190 (67) Dyslipidemia 190 (67) 92 (33) BMI > 25 kg/m2 No exercise 140 (50) Heavy drinking 67 (24) Emotional stress 95 (34) Past CVD history 13 (5) Family CVD history 84 (30)
188 (78) 127 (53) 18 (8) 100 (42) 60 (25) 106 (44) 9 (4) 30 (13) 43 (18) 23 (10)
No. (%) of patients Typical Multiple Atypical n = 309 (59%) n = 145 (28%) n = 68 (13%)
Chest pain 309 (100) Left shoulder/arm pain 0 (0) Right shoulder/arm pain 0 (0) Neck/Jaw pain 27 (9) Back pain 41 (13) Headache 36 (12) Indigestion/Abdominal pain 32 (10) Weakness/Fatigue 84 (27) Syncope 15 (5) Cold sweat 152 (49) Nausea/Vomiting 94 (30) Shortness of breath 110 (36) Cough/Chills 32 (10) Palpitation 28 (9)
137 (95) 131 (90) 73 (50) 29 (20) 38 (26) 28 (19) 24 (17) 40 (28) 7 (5) 89 (61) 58 (40) 43 (30) 12 (8) 21 (15)
1 (2) 4 (6) 3 (4) 2 (3) 7 (10) 6 (9) 32 (47) 36 (53) 14 (21) 19 (28) 21 (31) 38 (56) 17 (25) 5 (7)
Note. Bold indicates major risk factors or symptoms in dominance. BMI, body mass index; CVD, cardiovascular disease.
Cluster dyads of risk factors and symptoms Six cluster dyads were yielded based on the association between risk factor clusters and symptom clusters (χ2 = 8.50, P = 0.014), as shown in Figure 1 (Groups 1–6). A dyad of the typical symptom cluster and the risk factor cluster, dyslipidemia/smoking in dominance, had the largest number of patients (Group 1), composed of 160 MI patients (31%), followed by a dyad of the typical symptom cluster and the risk factor cluster, hypertension/ diabetes in dominance (Group 4, n = 149, 28%). Multiple symptom cluster with risk factors of either dyslipidemia/ smoking or hypertension/diabetes consisted of 18% and 10% of patients, respectively (Groups 2 and 5). Dyads of atypical symptom cluster and risk factor clusters of either dyslipidemia/smoking or hypertension/diabetes had the smallest number of patients, composed of less than 10% (Groups 3 and 6). Further analyses were performed to describe demographic and clinical characteristic memberships across six cluster dyads (Table 2). Significant differences in age, gender, MI with ST elevation, left ventricular ejection fraction, total cholesterol, low-density lipoprotein, triglyceride, pre-hospital delay, and hospital and cardiac care unit stays were observed among six groups. Group 2
included the youngest patients, whereas, those in Group 6 were the oldest (mean age = 54.23 ± 11.62 vs. 70.42 ± 10.31 years, P < 0.001). Group 5 included more women (49%) and Group 2 included fewer women (9%) (P < 0.001). More MI patients with ST elevation (64%) belonged to Group 2, whereas more non-ST elevated MI patients (79%) belonged to Group 6 (P < 0.01). Patients in Group 6 had significantly lower ejection fraction, and longer hospital/and cardiac care unit stays, compared with other groups.
12 month MACEs by six-cluster dyads Among MI patients, there were 436 event-free survivors and 86 patients with MACEs (17%) during the 12 month follow-up period (Fig. 2). A significant difference in MACEs was observed among six cluster dyads (χ2 = 11.63, P = 0.040). Particularly, 12-month cardiac or non-cardiac deaths differed significantly among six cluster dyads, with occurrence of more deaths in Group 6 (χ2 = 19.93, P = 0.001). After adjusting for age, gender and a type of MI diagnosis, logistic regression analysis showed that a membership in Group 6 (a dyad of atypical symptoms and hypertension/diabetes in dominance) was a significant predictor of 12 month MACEs (B = 1.13, P = 0.01), with the likelihood of having incidences of © 2014 Wiley Publishing Asia Pty Ltd
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Figure 1. Six cluster dyads of risk factors and symptoms: (a) Group 1, RF cluster 1 by Sx cluster 1; (b) Group 2, RF cluster 1 by Sx cluster 2; (c) Group 3, RF cluster 1 by Sx cluster 3; (d) Group 4, RF cluster 2 by Sx cluster 1; (e) Group 5, RF cluster 2 by Sx cluster 2; (f) Group 6, RF cluster 2 by Sx cluster 3. RF, risk factors; Sx, symptoms.
MACEs approximately three times higher than Group 1 (a dyad of typical symptom and dyslipidemia/smoking in dominance) (odds ratio = 3.10, 95% confidence interval = 1.32–7.29) (Table 3).
recurrent heart attacks and delay of medical treatment, targeting the cluster dyads at higher risk for greater incidence of MACEs.
DISCUSSION
Cluster dyads of risk factors and symptoms
This study is one of a few studies that investigated a possible association between risk factor clusters and symptom clusters and the differential impacts of cluster dyads of risk factors and symptoms on MACEs. The dyads of a two-cluster model for risk factors and a three-cluster model for symptoms yielded six groups, with two dyads of typical symptom cluster and either one of the two risk factor clusters being the largest, each composed of approximately 30% of patients. In contrast, two dyads of atypical symptom cluster and either one of the two risk factor clusters were the smallest, composed of less than 10% of patients. In particular, membership characteristics of the dyad of atypical symptom cluster-and-risk factor cluster of hypertension/diabetes included older age, lower ejection fraction, and longer hospital and cardiac care unit stays. More importantly, those who were in the dyad of atypical symptom cluster-and-risk factor cluster of hypertension/diabetes had a higher risk of 12 month MACEs in comparison with those who were in other dyads. Information on cluster dyads of risk factors and symptoms might be beneficial for the prevention of
Among three symptom clusters identified, chest pain or discomfort, the most common symptom, was clustered in both typical and multiple symptom clusters. In previous studies, chest symptom was dominant, particularly in younger populations.9,10 On the other hand, patients in the atypical symptom cluster reported shortness of breath and weakness/or fatigue, with few reporting chest pain. It was noteworthy that, consistent with previous studies in which 9–20% of MI patients experienced no chest symptoms,10,18 14% of 522 MI patients in this study did not have any pain or discomfort anywhere in the chest. In a previous study that investigated symptom clusters in women with MI, chest pain was rarely reported during the prodromal period.9 In contrast, fatigue was presented in all three symptom clusters, with the majority of older patients (76%) reporting fatigue 1 week prior to the acute event.11 However, in this study, fatigue was not often reported, with an exception of those in atypical cluster. A possible explanation would be that Koreans tend to recognize fatigue as a non-cardiac specific health problem or weakened body strength associated in part with aging.
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* P < 0.05; ** P < 0.01; *** P < 0.001. CCU, coronary care unit; HDL, high-density lipoprotein; LDL, low-density lipoprotein; LVEF, left ventricular ejection fraction; Mdn, median; STEMI, ST-elevation myocardial infarction; TC, total cholesterol; TG, triglyceride.
24.4*** 76.1*** 20.8** 4.0** 8.1*** 0.7 4.9*** 4.3** 18.1** 6.8*** 3.1** 70.4 ± 10.3, 71.5 16 (42) 8 (21) 44.3 ± 16.5 151.3 ± 45.7 48.8 ± 28.2 99.3 ± 45.7 112.8 ± 57.1 19 (54) 19.2 ± 16.8 5.9 ± 13.9 65.8 ± 9.9, 67.0 26 (49) 24 (45) 50.5 ± 15.4 165.1 ± 45.7 47.2 ± 14.4 102.6 ± 34.3 142.1 ± 106.9 30 (60) 11.4 ± 8.2 2.6 ± 2.1 68.4 ± 9.7, 70.0 65 (44) 75 (50) 53.5 ± 13.5 174.9 ± 48.5 46.4 ± 17.4 111.7 ± 36.4 96.3 ± 48.5 67 (47) 11.6 ± 10.8 3.4 ± 2.3 60.3 ± 12.0, 60.0 17 (11) 84 (53) 54.0 ± 12.8 193.6 ± 46.8 48.2 ± 20.1 132.8 ± 85.6 134.7 ± 103.1 50 (32) 9.6 ± 5.6 3.2 ± 2.1 Age, M ± SD, Mdn Gender (Female, %) STEMI (%) LVEF (M ± SD) TC HDL LDL TG Pre-hospital delay > 6 h Hospital stay (day) (M ± SD) CCU stay (day) (M ± SD)
54.2 ± 11.6, 52.0 8 (9) 59 (64) 53.9 ± 11.1 200.6 ± 45.7 44.7 ± 18.8 135.6 ± 43.8 144.5 ± 122.4 36 (40) 10.1 ± 10.0 3.4 ± 1.9
64.4 ± 14.5, 65.0 8 (27) 15 (50) 50.1 ± 16.2 175.5 ± 35.8 42.5 ± 15.7 113.1 ± 31.3 109.7 ± 52.7 15 (54) 12.9 ± 7.4 2.9 ± 2.1
Group 5 n = 53 Group 4 n = 149 Group 3 n = 30 Group 2 n = 92 Group 1 n = 160 Variables
Table 2 Differences in demographic and clinical characteristics by six cluster dyads (N = 522)
Group 6 n = 38
χ2/F
Risk factor-and-symptom cluster dyads
35 30 25 20 15 10 5 0 Group 1
Group 2
Group 3
Group 4
Group 5
Group 6
Figure 2. Incidences of 12 month major adverse cardiac events and deaths among patients with acute myocardial infarction by six cluster dyads (Groups 1–6). , Total MACEs P = 0.040; , Cardiac/Noncardiac death P = 0.001.
Symptom clusters were further examined with regard to the association with risk factor clusters. Six cluster dyads of risk factors and symptoms were generated based on significant relationships between the two sets of clusters. Patients included in the two largest cluster dyads had both typical chest symptoms and risk factors of dyslipidemia/cigarette use or hypertension/diabetes. The demographic membership for both cluster dyads indicated that patients were more likely to be younger than those who reported atypical symptoms. Younger adults at risk for MI appear to present with typical symptoms and receive prompt medical attention, supporting the notion that management of those risk factors is essential to prevent MI and to educate the public about typical symptoms. A substantial number of patients were included in one of the two risk factor clusters with multiple symptoms. The youngest patients with MI with ST elevation belonged to Group 2 (those with risk factors of dyslipidemia/cigarette use), whereas more women (49%) were included in Group 5 (those with risk factors of hypertension/diabetes), compared with other cluster dyads. Therefore, patients with young age or women tend to present with multiple symptoms that might mislead the diagnosis, and need to undergo a thorough evaluation for cardiovascular symptoms. On the other hand, patients who experienced more atypical symptoms, such as shortness of breath, fatigue or indigestion, but the least chest pain, were included in the smallest cluster dyads (Groups 3 and 6). In particular, those with older age, MI with non-ST elevation, more compromised cardiac function indicated by lower ejection © 2014 Wiley Publishing Asia Pty Ltd
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Table 3 Prediction of the cluster dyads of risk factors and symptoms for the 12 month major adverse cardiac events and deaths among patients with first-time myocardial infarction Variables†
B
SE
Odds ratio
95% CI
P
Group 2 (multiple symptom/dyslipidemia, smoking) Group 3 (atypical symptom/dyslipidemia, smoking) Group 4 (typical symptom/hypertension, diabetes) Group 5 (multiple symptom/hypertension, diabetes) Group 6 (atypical symptom/hypertension, diabetes)
−0.39 −0.43 0.26 0.41 1.13
0.41 0.65 0.33 0.43 0.44
0.68 0.65 1.30 1.50 3.10
0.30–1.50 0.18–2.34 0.68–2.49 0.65–3.49 1.32–7.29
0.338 0.511 0.427 0.342 0.010
Note. † Indicates predictor variables of cluster dyads in a logistic regression analysis, with adjustment for age, gender and a type of myocardial infarction; a reference cluster dyad was Group 1 (typical symptom and dyslipidemia/smoking in dominance). The overall predictive model was statistically significant (χ2 = 15.74, P = 0.046).
fraction, and longer hospital and cardiac care unit stays belonged to Group 6, compared to other cluster dyads. In past studies, MI patients with atypical symptoms were more likely to be older, women and diabetic.6,19,20 These atypical symptoms might be bearable, so that the patients wait until symptoms disappear naturally or subside rather than visit the hospital, resulting in a longer pre-hospital delay. This delayed decision to seek care has been well documented in patients with acute coronary syndrome, in which an atypical symptom was a predictor of pre-hospital delay.21–23 Further, absence of chest pain at hospital presentation was a significant predictor of lower use of thrombolytic therapy and adverse hospital outcomes.12,24,25 Six cluster dyads demonstrated a variety of demographic and clinical characteristics that might provide useful information for early detection and diagnosis of MI. Not all patients followed a similar trajectory during the acute event, and, in particular, it might guide in establishment of preventive educational strategies for members of the public who are at high risk for MI and shorten pre-hospital delay of women or older patients with diabetes who often present with atypical or multiple symptoms. In addition, medical attention should be paid to these high-risk subgroups who have demonstrated atypical appearance during the acute event.
Impact of six-cluster dyads on the 12 month MACEs With an extension of the past study, which reported a significant difference in mortality by symptom clusters,13,15 we found that cluster dyads of risk factors and symptoms showed an association with the incidences of 12 month MACEs among first-time MI patients. The 12 © 2014 Wiley Publishing Asia Pty Ltd
month MACEs, including mortality, were higher in patients in cluster dyads of Groups 4–6 who dominantly had risk factors of hypertension/diabetes, with the highest incidence occurring in patients of Group 6 who had atypical symptoms. In particular, 12 month cardiac or noncardiac deaths differed significantly among the six groups, with more deaths occurring in Group 6. Furthermore, a variable of cluster dyads was a significant predictor of 12 month MACEs after adjusting for the effects of age, gender and a type of MI diagnosis, with those in Group 6 (atypical symptom cluster and risk factor cluster of hypertension/diabetes) having three times higher incidences in 12 month MACEs than those in Group 1 (typical symptom cluster-and-risk factor cluster of hypertension/ diabetes) (odds ratio = 3.10, P = 0.010). In a previous cluster study, patients with acute coronary syndrome in the diffuse symptom cluster had significantly higher mortality at 2 year follow-up (17%), compared with those in other three symptom clusters of classic chest pain, multiple pains or stress (2–5%).13 These findings support the need for particular attention to MI patients who present with atypical symptoms, especially older adults with hypertension/diabetes for early detection of acute events and prevention of adverse outcomes. The focus of the investigation was to determine the impact of cluster classification on the cumulative incidences of MACEs, which indicates the proportion of new cases that develop in a 12 month follow-up. Further studies are needed to evaluate whether the cluster dyads are useful to predict incidence rates that demonstrate the new cases of MACEs per population at risk per unit time, or to prevent delay in seeking treatment in patients with MI, compared with the current guideline26,27
Risk factor-and-symptom cluster dyads
In conclusion, among first-time MI patients, 10 risk factors and 14 symptoms were clustered into two and three, respectively, with the two sets of clusters showing a significant relationship. More importantly, cluster dyads showed an association with 12 month MACEs in MI patients, with the highest incidence of 12 month MACEs occurring in those who were older and diabetic and who presented with atypical symptoms. The results obtained from this study provide a predictable basis for cardiovascular nurses in clinical practice who have difficulty in coping with critical conditions. Clinical implications of the findings include the need for patients to undergo thorough cardiovascular evaluation and those at higher risk for adverse outcomes, particularly having fatigue or other vague symptoms should be a priority group for further evaluation and treatment due to the fact that it is probably a signal of impending heart attack. These vulnerable patients might have difficulty in being aware of an impending episode and seeking help promptly. Therefore, public education might be essential to assist them in recognizing early signs of an impending attack and prevent prehospital delay, which in turn decreases adverse health outcomes, including poorer recovery with longer hospital stay. This study had a couple of limitations. One limitation was that patients were enrolled from only one universityaffiliated hospital; however, it is one of the largest hospitals specializing in cardiac care, including MI, in Korea. The hospital is located in a predominantly rural area where the residents tend to have lower educational level and poorer socioeconomic status than would be typical of patients in a large urban hospital. Therefore, the study population cannot be considered representative of all MI patients in Korea. In addition, symptoms were self-reported while patients were hospitalized for revascularization; thereby, information obtained was retrospective in nature, raising a concern about the accuracy of symptom reports. Last, it is beyond the scope of this investigation to determine the relative efficacy of cluster dyads in prognostic values and therapeutic decision-making in daily practice with regard to the risk stratification, treatment decisions or prediction of prognosis.
ACKNOWLEDGEMENT This paper was supported by the research fund of Hanyang University (HY-201200000002403).
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with first-time acute coronary syndrome. Journal of Korean Academy of Nursing 2009; 39: 888–898. Moser DK, Kimble LP, Alberts MJ et al. Reducing delay in seeking treatment by patients with acute coronary syndrome and stroke: A scientific statement from the American Heart Association Council on Cardiovascular Nursing and Stroke Council. Journal of Cardiovascular Nursing 2007; 22: 326–343. Thuresson M, Berglin M, Lindahl B, Svensson L, Zedigh C, Herlitz J. Thoughts, actions, and factors associated with prehospital delay in patients with acute coronary syndrome. Heart & Lung: The Journal of Critical Care 2007; 36: 398–409. Barron HV, Bowlby LJ, Breen T et al. Use of reperfusion therapy for acute myocardial infarction in the United States. Circulation 1998; 97: 1150–1156. Gregoratos G. Clinical manifestations of acute myocardial infarction in older patients. American Journal of Geriatric Cardiology 2001; 10: 345–347. Braunwald E, Antman EM, Beasley JW et al. ACC/AHA 2002 guideline update for the management of patients with unstable angina and non-ST-segment elevation myocardial infarction–summary article: A report of the American College of Cardiology/American Heart Association task force on practice guidelines. Journal of the American College of Cardiology 2002; 40: 1366–1374. Antman EM, Cohen M, Bernink PJ et al. The TIMI risk score for unstable angina/non-ST elevation MI: A method for prognostication and therapeutic decision making. JAMA: The Journal of the American Medical Association 2000; 284: 835–842.
International Journal of Nursing Practice 2015; 21: 166–174
RESEARCH PAPER
Cluster dyads of risk factors and symptoms are associated with major adverse cardiac events in patients with acute myocardial infarction Seon Young Hwang RN PhD Associate Professor, College of Nursing, Hanyang University, Seoul, South Korea
JinShil Kim RN PhD Associate Professor, College of Nursing, Gachon University, Incheon, South Korea
Accepted for publication May 2013 Hwang SY, Kim JS. International Journal of Nursing Practice 2015; 21: 166–174 Cluster dyads of risk factors and symptoms are associated with major adverse cardiac events in patients with acute myocardial infarction The purpose of this study was to examine the cluster dyads of risk factors and symptoms and their impact on the incidence of 12 month major adverse cardiac events (MACEs) among patients with first-time myocardial infarction (MI). In a descriptive study, a total of 522 patients completed semi-structured interviews for data on risk factors and symptoms. Patients were followed for 12 months to determine MACEs. Latent class cluster analysis was performed to identify risk factor clusters and symptom clusters. Logistic regression analysis was performed to determine the impact of cluster dyads on 12 month MACEs. There were 436 event-free survivors and 86 patients with MACEs for 12 months. Ten risk factors and 14 symptoms were clustered into two (dyslipidemia/smoking, hypertension/diabetes dominant) and three (typical, multiple, atypical) memberships, respectively. Six cluster dyads which were generated based on the association between risk factors and symptom clusters were a significant predictor of 12 month MACEs, with the incidence occurring three times higher in a dyad of hypertension/diabetes-and-atypical symptoms than a dyad of dyslipidemia/smoking-and-typical symptoms (odds ratio = 3.10, P = 0.01), after adjustment for age, gender and a type of MI diagnosis. The information on cluster dyads suggests that health-care providers need to consider both risk factors and symptoms at hospital presentation for risk stratification to prevent adverse outcomes. Key words: acute myocardial infarction, cardiovascular disease, major adverse cardiac events, risk factors, symptom cluster.
INTRODUCTION Acute myocardial infarction (MI) has increased in prevalence over the past decade in South Korea, as a result
Correspondence: JinShil Kim, PhD, RN, Associate Professor, Gachon University, College of Nursing, 191 Hambakmoero, Yeonsu-gu, Incheon, South Korea; telephone (82) 032-820-4229; fax (82) 032820-4201; Email: [email protected] © 2014 Wiley Publishing Asia Pty Ltd
of longer exposure to cardiovascular diseases (CVDs).1 Mortality rates of patients with CVDs are high, ranking second in causes of death in Korea.1 Among cardiovascular deaths, approximately one in four dies of MI, with a rapid increase from 18% in 2001 to 24% in 2010.1 Cardiovascular risk stratification of individuals according to risk factors and symptoms and providing primary preventative intervention accordingly might beone strategy to decrease the prevalence and mortality of MI.2 doi:10.1111/ijn.12241
Risk factor-and-symptom cluster dyads
Major risk factors for developing MI include cigarette smoking, elevated blood pressure, dyslipidemia and diabetes mellitus.3 Clustering of these risk factors is likely to have synergistic effects on CVDs, which is greater than the sum of the risks associated with each abnormality.3,4 In a nationwide Korea Acute Myocardial Infarction Registry study in which 13 133 MI patients participated, patients reported current cigarette use most often (57%), followed by hypertension (49%), diabetes mellitus (28%), and a history of cardiac events or heart attack (26%).5 Determination of the risk clusters should be investigated, with preventive efforts targeting groups of people at greater risk for MI, rather than management of each risk factor. In addition, early recognition of symptoms associated with an impending event, which might assist people in seeking prompt medical attention, is needed.6 The most typical and primary symptom reported by MI patients is chest pain. A single symptom experience of chest pain was reported in 35% of patients only, whereas symptom clusters consisting primarily of chest pain accompanied by other symptoms, such as indigestion or epigastric discomfort, were reported in 57% of patients.7 In conjunction with research on single symptoms in patients with MI, symptom cluster, defined as ‘multiple concurrent symptoms that are related and might have a common etiology’, should be determined.8 Understanding of an individual symptom experience or symptom cluster prior to or during the acute period of MI and one’s underlying risk factors is imperative, thereby allowing health-care providers to conduct further evaluation and to establish a plan for individual care. Information on the association between risk factors and symptom clusters might assist health-care providers to detect patients early at high risk for an impending episode of MI. It could also serve as a basis for educational provision and clinical guideline for primary and secondary prevention targeting high-risk cluster groups. Compared with the well documentation for each of the cardiovascular risk factors and symptom cluster,9–11 the cluster dyads of risk factors and symptoms remain unknown. Further, symptom experiences of patients with CVDs and their synergistic influence on clinical outcomes are well known,12,13 yet there is a lack of knowledge on whether the dyadic clusters are associated with prospective clinical outcomes of major adverse cardiac events (MACEs), including revascularization, re-infarction, and cardiac or non-cardiac deaths among
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MI patients. An empirical study is needed to provide a basic data for clinical guideline aimed at preventing cardiac events and facilitating prioritized care for patients with MI. To address these concerns, the aims of this study were to: (i) identify risk factor clusters and symptom clusters; (ii) determine the dyads of risk factors-and-symptoms clusters; and (iii) examine whether the cluster dyads of risk factors and symptoms predict the incidence of 12-month MACEs after the acute event among first-time MI patients.
METHODS Design and procedure This was a prospective, descriptive study, in which risk factors and symptom clusters were evaluated among firsttime MI patients who underwent percutaneous coronary intervention. Patients were recruited from a universityaffiliated medical centre after approval of the study by the University Institutional Review Board. Data were obtained from a research project, entitled ‘Treatmentseeking behaviors and factors associated with pre-hospital delay in MI patients’. Some of the data of this study were used in previously published studies.14,15 Prior to the conduction of face-to-face interviews, MI patients were contacted and each person signed a written informed consent statement. Patients completed semi-structured interviews to obtain data on risk factors and symptoms occurring during the acute phase of MI. An abstraction form developed by the principal investigator was used for retrieval of 10 risk factors and 14 acute and associated symptoms. These risk factors and acute and associated symptoms were adopted from the evidence of clinical studies and an extensive review of the literature conducted by the American Heart Association.3 Semi-structured interviews were conducted at a designated area for research in a hospital where patients received the care. Baseline data were collected from November 2007 through March 2010 by research nurses with a degree in graduate nursing trained to conduct semi-structured interviews with a written script. Each patient’s medical record was also reviewed for collection of demographic and clinical characteristics, including medications and laboratory data. The interviews took approximately 20–30 min to complete. Patients were followed for 12 months to collect data on MACEs, with confirmation by electronic medical record review. © 2014 Wiley Publishing Asia Pty Ltd
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Participants The study population consisted of a convenience sample of 522 MI patients. Eligibility criteria for the study were: (i) hospitalized patients who underwent percutaneous coronary intervention for first-time MI with or without ST segment elevation; and (ii) aged 18 years or older at the time of informed consent. Patients were excluded if they: (i) had conditions that would confound symptoms experienced with MI, such as heart failure, terminal cancer or renal failure; or (ii) have documented cognitive impairment, such as dementia and/or stroke or psychiatric diagnosis, that precluded symptom recognition or the ability to engage in a face-to-face interview.
Statistical analysis Latent class cluster analysis, using Latent Gold software, version 3.016 was performed to address aim (i), which was to divide MI patients into separate homogeneous subgroups of risk factors and symptoms. Ten risk factors and 14 symptom indicators were used as variables for definition of clusters. The values of Bayesian Information Criterion, Akaike Information Criterion (AIC), and AIC3 log likelihoods were used to identify the best model fits for risk factor and symptom clusters. Wald tests were used for determination of overall statistical significances of the sets of parameter estimates of the cluster models. Thereby, a two-cluster model for risk factors (Wald = 26.37, P < 0.001) and a three-cluster model for symptoms (Wald = 30.14, P < 0.001) were adopted for further statistical analyses. Cluster memberships of the two- and three-cluster models were then converted to a data set using Statistical Package for the Social Sciences, version 21 (IBM Corp., Korea). Then bivariate analyses were performed to fully describe cluster membership, using χ2 tests, t-tests or one-way analysis of variance with Scheffé’s tests. For example, one-way analyses of variance were performed to determine group differences in age, pre-hospital delay, or hospital and cardiac care unit stays among six cluster dyads. To address aim (ii), which was to determine cluster dyads, based on associations between risk factor clusters and symptom clusters, χ2 test statistics were computed. To address aim (iii), which was to determine whether the cluster dyads of risk factors and symptoms predict the incidences of 12 month MACEs after the acute event, χ2 test and logistic regression analysis were performed with adjustment for age, gender and a type of MI diagnosis. A total sample of 522 was an enough sample size to perform © 2014 Wiley Publishing Asia Pty Ltd
a logistic regression analysis using G*Power program,17 which had a power of 0.85, alpha of.05 and a medium effect size of 0.30.
RESULTS Sample characteristics A total of 522 patients completed semi-structured interviews. The mean age of patients was 63 years (63.13 ± 12.32, range 26–89 years). Seventy three per cent of patients were men and 40% had an elementary school education or lower. Approximately half of patients had a diagnosis of MI with ST segment elevation (51%). The major risk factors reported by patients were hypertension (49%), dyslipidemia (56%), current smokers (40%), diabetes (32%), and family history of CVDs, including hypertension, stroke or ischaemic heart disease (21%). The most frequent symptom experienced by patients was chest pain or discomfort (86%), and symptoms reported by at least one third of patients included cold sweat, shortness of breath, nausea/vomiting, and weakness/ fatigue. The average number of symptoms experienced was 3.72 ± 1.83 during the cardiac event.
Risk factor clusters and symptom clusters Using latent class cluster analysis, two distinct clusters of risk factors and three symptom clusters were identified (Table 1). The first risk factor cluster was composed of 282 MI patients (54%): dyslipidemia and smoking were dominant risk factors, both reported by 67% of patients. The second risk factor cluster was composed of 240 patients (46%): hypertension and diabetes mellitus were dominant risk factors, reported by 78% and 53% of patients, respectively. The largest symptom cluster consisted of 309 MI patients (59%) (Cluster 1), and the following two clusters, respectively, included 145 (28%, Cluster 2) and 68 (13%, Cluster 3) patients. The most frequent symptom of Cluster 1 was chest pain, which was reported by all patients. Patients in Cluster 2 experienced many classic MI symptoms and complained of the most acute symptoms, including chest pain (95%), left (90%) or right (50%) shoulder pain, cold sweating (61%), and nausea/ vomiting (40%). Unlike patients in the two clusters, only one patient in Cluster 3 reported chest pain and greater than half reported shortness of breath and weakness or fatigue. Clusters were then labelled as typical, multiple and atypical symptom clusters, respectively.
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Table 1 Risk factor cluster and symptom cluster (N = 522) Risk factor variables No. (%) of patients
Symptom variables
Cluster 1 Cluster 2 n = 282 (54%) n = 240 (46%) Hypertension 69 (24) Diabetes 39 (14) Current smoking 190 (67) Dyslipidemia 190 (67) 92 (33) BMI > 25 kg/m2 No exercise 140 (50) Heavy drinking 67 (24) Emotional stress 95 (34) Past CVD history 13 (5) Family CVD history 84 (30)
188 (78) 127 (53) 18 (8) 100 (42) 60 (25) 106 (44) 9 (4) 30 (13) 43 (18) 23 (10)
No. (%) of patients Typical Multiple Atypical n = 309 (59%) n = 145 (28%) n = 68 (13%)
Chest pain 309 (100) Left shoulder/arm pain 0 (0) Right shoulder/arm pain 0 (0) Neck/Jaw pain 27 (9) Back pain 41 (13) Headache 36 (12) Indigestion/Abdominal pain 32 (10) Weakness/Fatigue 84 (27) Syncope 15 (5) Cold sweat 152 (49) Nausea/Vomiting 94 (30) Shortness of breath 110 (36) Cough/Chills 32 (10) Palpitation 28 (9)
137 (95) 131 (90) 73 (50) 29 (20) 38 (26) 28 (19) 24 (17) 40 (28) 7 (5) 89 (61) 58 (40) 43 (30) 12 (8) 21 (15)
1 (2) 4 (6) 3 (4) 2 (3) 7 (10) 6 (9) 32 (47) 36 (53) 14 (21) 19 (28) 21 (31) 38 (56) 17 (25) 5 (7)
Note. Bold indicates major risk factors or symptoms in dominance. BMI, body mass index; CVD, cardiovascular disease.
Cluster dyads of risk factors and symptoms Six cluster dyads were yielded based on the association between risk factor clusters and symptom clusters (χ2 = 8.50, P = 0.014), as shown in Figure 1 (Groups 1–6). A dyad of the typical symptom cluster and the risk factor cluster, dyslipidemia/smoking in dominance, had the largest number of patients (Group 1), composed of 160 MI patients (31%), followed by a dyad of the typical symptom cluster and the risk factor cluster, hypertension/ diabetes in dominance (Group 4, n = 149, 28%). Multiple symptom cluster with risk factors of either dyslipidemia/ smoking or hypertension/diabetes consisted of 18% and 10% of patients, respectively (Groups 2 and 5). Dyads of atypical symptom cluster and risk factor clusters of either dyslipidemia/smoking or hypertension/diabetes had the smallest number of patients, composed of less than 10% (Groups 3 and 6). Further analyses were performed to describe demographic and clinical characteristic memberships across six cluster dyads (Table 2). Significant differences in age, gender, MI with ST elevation, left ventricular ejection fraction, total cholesterol, low-density lipoprotein, triglyceride, pre-hospital delay, and hospital and cardiac care unit stays were observed among six groups. Group 2
included the youngest patients, whereas, those in Group 6 were the oldest (mean age = 54.23 ± 11.62 vs. 70.42 ± 10.31 years, P < 0.001). Group 5 included more women (49%) and Group 2 included fewer women (9%) (P < 0.001). More MI patients with ST elevation (64%) belonged to Group 2, whereas more non-ST elevated MI patients (79%) belonged to Group 6 (P < 0.01). Patients in Group 6 had significantly lower ejection fraction, and longer hospital/and cardiac care unit stays, compared with other groups.
12 month MACEs by six-cluster dyads Among MI patients, there were 436 event-free survivors and 86 patients with MACEs (17%) during the 12 month follow-up period (Fig. 2). A significant difference in MACEs was observed among six cluster dyads (χ2 = 11.63, P = 0.040). Particularly, 12-month cardiac or non-cardiac deaths differed significantly among six cluster dyads, with occurrence of more deaths in Group 6 (χ2 = 19.93, P = 0.001). After adjusting for age, gender and a type of MI diagnosis, logistic regression analysis showed that a membership in Group 6 (a dyad of atypical symptoms and hypertension/diabetes in dominance) was a significant predictor of 12 month MACEs (B = 1.13, P = 0.01), with the likelihood of having incidences of © 2014 Wiley Publishing Asia Pty Ltd
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Figure 1. Six cluster dyads of risk factors and symptoms: (a) Group 1, RF cluster 1 by Sx cluster 1; (b) Group 2, RF cluster 1 by Sx cluster 2; (c) Group 3, RF cluster 1 by Sx cluster 3; (d) Group 4, RF cluster 2 by Sx cluster 1; (e) Group 5, RF cluster 2 by Sx cluster 2; (f) Group 6, RF cluster 2 by Sx cluster 3. RF, risk factors; Sx, symptoms.
MACEs approximately three times higher than Group 1 (a dyad of typical symptom and dyslipidemia/smoking in dominance) (odds ratio = 3.10, 95% confidence interval = 1.32–7.29) (Table 3).
recurrent heart attacks and delay of medical treatment, targeting the cluster dyads at higher risk for greater incidence of MACEs.
DISCUSSION
Cluster dyads of risk factors and symptoms
This study is one of a few studies that investigated a possible association between risk factor clusters and symptom clusters and the differential impacts of cluster dyads of risk factors and symptoms on MACEs. The dyads of a two-cluster model for risk factors and a three-cluster model for symptoms yielded six groups, with two dyads of typical symptom cluster and either one of the two risk factor clusters being the largest, each composed of approximately 30% of patients. In contrast, two dyads of atypical symptom cluster and either one of the two risk factor clusters were the smallest, composed of less than 10% of patients. In particular, membership characteristics of the dyad of atypical symptom cluster-and-risk factor cluster of hypertension/diabetes included older age, lower ejection fraction, and longer hospital and cardiac care unit stays. More importantly, those who were in the dyad of atypical symptom cluster-and-risk factor cluster of hypertension/diabetes had a higher risk of 12 month MACEs in comparison with those who were in other dyads. Information on cluster dyads of risk factors and symptoms might be beneficial for the prevention of
Among three symptom clusters identified, chest pain or discomfort, the most common symptom, was clustered in both typical and multiple symptom clusters. In previous studies, chest symptom was dominant, particularly in younger populations.9,10 On the other hand, patients in the atypical symptom cluster reported shortness of breath and weakness/or fatigue, with few reporting chest pain. It was noteworthy that, consistent with previous studies in which 9–20% of MI patients experienced no chest symptoms,10,18 14% of 522 MI patients in this study did not have any pain or discomfort anywhere in the chest. In a previous study that investigated symptom clusters in women with MI, chest pain was rarely reported during the prodromal period.9 In contrast, fatigue was presented in all three symptom clusters, with the majority of older patients (76%) reporting fatigue 1 week prior to the acute event.11 However, in this study, fatigue was not often reported, with an exception of those in atypical cluster. A possible explanation would be that Koreans tend to recognize fatigue as a non-cardiac specific health problem or weakened body strength associated in part with aging.
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* P < 0.05; ** P < 0.01; *** P < 0.001. CCU, coronary care unit; HDL, high-density lipoprotein; LDL, low-density lipoprotein; LVEF, left ventricular ejection fraction; Mdn, median; STEMI, ST-elevation myocardial infarction; TC, total cholesterol; TG, triglyceride.
24.4*** 76.1*** 20.8** 4.0** 8.1*** 0.7 4.9*** 4.3** 18.1** 6.8*** 3.1** 70.4 ± 10.3, 71.5 16 (42) 8 (21) 44.3 ± 16.5 151.3 ± 45.7 48.8 ± 28.2 99.3 ± 45.7 112.8 ± 57.1 19 (54) 19.2 ± 16.8 5.9 ± 13.9 65.8 ± 9.9, 67.0 26 (49) 24 (45) 50.5 ± 15.4 165.1 ± 45.7 47.2 ± 14.4 102.6 ± 34.3 142.1 ± 106.9 30 (60) 11.4 ± 8.2 2.6 ± 2.1 68.4 ± 9.7, 70.0 65 (44) 75 (50) 53.5 ± 13.5 174.9 ± 48.5 46.4 ± 17.4 111.7 ± 36.4 96.3 ± 48.5 67 (47) 11.6 ± 10.8 3.4 ± 2.3 60.3 ± 12.0, 60.0 17 (11) 84 (53) 54.0 ± 12.8 193.6 ± 46.8 48.2 ± 20.1 132.8 ± 85.6 134.7 ± 103.1 50 (32) 9.6 ± 5.6 3.2 ± 2.1 Age, M ± SD, Mdn Gender (Female, %) STEMI (%) LVEF (M ± SD) TC HDL LDL TG Pre-hospital delay > 6 h Hospital stay (day) (M ± SD) CCU stay (day) (M ± SD)
54.2 ± 11.6, 52.0 8 (9) 59 (64) 53.9 ± 11.1 200.6 ± 45.7 44.7 ± 18.8 135.6 ± 43.8 144.5 ± 122.4 36 (40) 10.1 ± 10.0 3.4 ± 1.9
64.4 ± 14.5, 65.0 8 (27) 15 (50) 50.1 ± 16.2 175.5 ± 35.8 42.5 ± 15.7 113.1 ± 31.3 109.7 ± 52.7 15 (54) 12.9 ± 7.4 2.9 ± 2.1
Group 5 n = 53 Group 4 n = 149 Group 3 n = 30 Group 2 n = 92 Group 1 n = 160 Variables
Table 2 Differences in demographic and clinical characteristics by six cluster dyads (N = 522)
Group 6 n = 38
χ2/F
Risk factor-and-symptom cluster dyads
35 30 25 20 15 10 5 0 Group 1
Group 2
Group 3
Group 4
Group 5
Group 6
Figure 2. Incidences of 12 month major adverse cardiac events and deaths among patients with acute myocardial infarction by six cluster dyads (Groups 1–6). , Total MACEs P = 0.040; , Cardiac/Noncardiac death P = 0.001.
Symptom clusters were further examined with regard to the association with risk factor clusters. Six cluster dyads of risk factors and symptoms were generated based on significant relationships between the two sets of clusters. Patients included in the two largest cluster dyads had both typical chest symptoms and risk factors of dyslipidemia/cigarette use or hypertension/diabetes. The demographic membership for both cluster dyads indicated that patients were more likely to be younger than those who reported atypical symptoms. Younger adults at risk for MI appear to present with typical symptoms and receive prompt medical attention, supporting the notion that management of those risk factors is essential to prevent MI and to educate the public about typical symptoms. A substantial number of patients were included in one of the two risk factor clusters with multiple symptoms. The youngest patients with MI with ST elevation belonged to Group 2 (those with risk factors of dyslipidemia/cigarette use), whereas more women (49%) were included in Group 5 (those with risk factors of hypertension/diabetes), compared with other cluster dyads. Therefore, patients with young age or women tend to present with multiple symptoms that might mislead the diagnosis, and need to undergo a thorough evaluation for cardiovascular symptoms. On the other hand, patients who experienced more atypical symptoms, such as shortness of breath, fatigue or indigestion, but the least chest pain, were included in the smallest cluster dyads (Groups 3 and 6). In particular, those with older age, MI with non-ST elevation, more compromised cardiac function indicated by lower ejection © 2014 Wiley Publishing Asia Pty Ltd
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Table 3 Prediction of the cluster dyads of risk factors and symptoms for the 12 month major adverse cardiac events and deaths among patients with first-time myocardial infarction Variables†
B
SE
Odds ratio
95% CI
P
Group 2 (multiple symptom/dyslipidemia, smoking) Group 3 (atypical symptom/dyslipidemia, smoking) Group 4 (typical symptom/hypertension, diabetes) Group 5 (multiple symptom/hypertension, diabetes) Group 6 (atypical symptom/hypertension, diabetes)
−0.39 −0.43 0.26 0.41 1.13
0.41 0.65 0.33 0.43 0.44
0.68 0.65 1.30 1.50 3.10
0.30–1.50 0.18–2.34 0.68–2.49 0.65–3.49 1.32–7.29
0.338 0.511 0.427 0.342 0.010
Note. † Indicates predictor variables of cluster dyads in a logistic regression analysis, with adjustment for age, gender and a type of myocardial infarction; a reference cluster dyad was Group 1 (typical symptom and dyslipidemia/smoking in dominance). The overall predictive model was statistically significant (χ2 = 15.74, P = 0.046).
fraction, and longer hospital and cardiac care unit stays belonged to Group 6, compared to other cluster dyads. In past studies, MI patients with atypical symptoms were more likely to be older, women and diabetic.6,19,20 These atypical symptoms might be bearable, so that the patients wait until symptoms disappear naturally or subside rather than visit the hospital, resulting in a longer pre-hospital delay. This delayed decision to seek care has been well documented in patients with acute coronary syndrome, in which an atypical symptom was a predictor of pre-hospital delay.21–23 Further, absence of chest pain at hospital presentation was a significant predictor of lower use of thrombolytic therapy and adverse hospital outcomes.12,24,25 Six cluster dyads demonstrated a variety of demographic and clinical characteristics that might provide useful information for early detection and diagnosis of MI. Not all patients followed a similar trajectory during the acute event, and, in particular, it might guide in establishment of preventive educational strategies for members of the public who are at high risk for MI and shorten pre-hospital delay of women or older patients with diabetes who often present with atypical or multiple symptoms. In addition, medical attention should be paid to these high-risk subgroups who have demonstrated atypical appearance during the acute event.
Impact of six-cluster dyads on the 12 month MACEs With an extension of the past study, which reported a significant difference in mortality by symptom clusters,13,15 we found that cluster dyads of risk factors and symptoms showed an association with the incidences of 12 month MACEs among first-time MI patients. The 12 © 2014 Wiley Publishing Asia Pty Ltd
month MACEs, including mortality, were higher in patients in cluster dyads of Groups 4–6 who dominantly had risk factors of hypertension/diabetes, with the highest incidence occurring in patients of Group 6 who had atypical symptoms. In particular, 12 month cardiac or noncardiac deaths differed significantly among the six groups, with more deaths occurring in Group 6. Furthermore, a variable of cluster dyads was a significant predictor of 12 month MACEs after adjusting for the effects of age, gender and a type of MI diagnosis, with those in Group 6 (atypical symptom cluster and risk factor cluster of hypertension/diabetes) having three times higher incidences in 12 month MACEs than those in Group 1 (typical symptom cluster-and-risk factor cluster of hypertension/ diabetes) (odds ratio = 3.10, P = 0.010). In a previous cluster study, patients with acute coronary syndrome in the diffuse symptom cluster had significantly higher mortality at 2 year follow-up (17%), compared with those in other three symptom clusters of classic chest pain, multiple pains or stress (2–5%).13 These findings support the need for particular attention to MI patients who present with atypical symptoms, especially older adults with hypertension/diabetes for early detection of acute events and prevention of adverse outcomes. The focus of the investigation was to determine the impact of cluster classification on the cumulative incidences of MACEs, which indicates the proportion of new cases that develop in a 12 month follow-up. Further studies are needed to evaluate whether the cluster dyads are useful to predict incidence rates that demonstrate the new cases of MACEs per population at risk per unit time, or to prevent delay in seeking treatment in patients with MI, compared with the current guideline26,27
Risk factor-and-symptom cluster dyads
In conclusion, among first-time MI patients, 10 risk factors and 14 symptoms were clustered into two and three, respectively, with the two sets of clusters showing a significant relationship. More importantly, cluster dyads showed an association with 12 month MACEs in MI patients, with the highest incidence of 12 month MACEs occurring in those who were older and diabetic and who presented with atypical symptoms. The results obtained from this study provide a predictable basis for cardiovascular nurses in clinical practice who have difficulty in coping with critical conditions. Clinical implications of the findings include the need for patients to undergo thorough cardiovascular evaluation and those at higher risk for adverse outcomes, particularly having fatigue or other vague symptoms should be a priority group for further evaluation and treatment due to the fact that it is probably a signal of impending heart attack. These vulnerable patients might have difficulty in being aware of an impending episode and seeking help promptly. Therefore, public education might be essential to assist them in recognizing early signs of an impending attack and prevent prehospital delay, which in turn decreases adverse health outcomes, including poorer recovery with longer hospital stay. This study had a couple of limitations. One limitation was that patients were enrolled from only one universityaffiliated hospital; however, it is one of the largest hospitals specializing in cardiac care, including MI, in Korea. The hospital is located in a predominantly rural area where the residents tend to have lower educational level and poorer socioeconomic status than would be typical of patients in a large urban hospital. Therefore, the study population cannot be considered representative of all MI patients in Korea. In addition, symptoms were self-reported while patients were hospitalized for revascularization; thereby, information obtained was retrospective in nature, raising a concern about the accuracy of symptom reports. Last, it is beyond the scope of this investigation to determine the relative efficacy of cluster dyads in prognostic values and therapeutic decision-making in daily practice with regard to the risk stratification, treatment decisions or prediction of prognosis.
ACKNOWLEDGEMENT This paper was supported by the research fund of Hanyang University (HY-201200000002403).
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