|Year : 2021 | Volume
| Issue : 2 | Page : 871-876
Comparison of demographic profile, risk factors, and in-hospital outcome in young and old patients with acute coronary syndrome: A single-center experience
Nikhil Bush1, Yash Paul Sharma2, Krishna Prasad2, Pankaj Kumar1, Saurabh Mehrotra2
1 Department of Internal Medicine, Post Graduate institute of Medical Education and Research, Chandigarh, India
2 Department of Cardiology, Post Graduate institute of Medical Education and Research, Chandigarh, India
|Date of Submission||23-Sep-2020|
|Date of Decision||02-Dec-2020|
|Date of Acceptance||16-Dec-2020|
|Date of Web Publication||27-Feb-2021|
Dr. Saurabh Mehrotra
Additional Professor, Department of Cardiology, Advanced Cardiac Centre, Post Graduate Institute of Medical Education and Research, Sector 12, Chandigarh – 160 012
Source of Support: None, Conflict of Interest: None
Background: Coronary artery disease (CAD) is witnessing a demographic transition with increasing prevalence in younger individuals. Data is scarce comparing various characteristics of acute coronary syndrome (ACS) between young and old patients in an Indian setting. Hence, we evaluated the epidemiological, demographic, risk factor, and outcome profile of young and old ACS patients in Indian setting. Methods: This was a prospective observational study, which enrolled 50 consecutive ACS patients each into two groups: younger (≤45 years) and elderly (>45 years), respectively. Comparison of clinical presentation, electrocardiography, echocardiographic findings, conventional, nonconventional risk factors, and in-hospital outcomes including duration of hospital stay and major adverse cardiac events (MACE) were made between the two groups. Multivariate regression analysis of risk factors as determinants of MACE adjusting for other confounding factors was also performed. Results: Fifty patients in each group were compared. Mean age in the younger and elderly group was 36 ± 4.69 and 61.58 ± 10.69 years, respectively. Male sex, smoking, family history of CAD, hyperhomocysteinemia, and obesity were observed more in the younger population. While dyslipidemia, low physical activity, diabetes mellitus, and history of previous ACS was more in the older population. Single-vessel disease was more common in younger patients while multivessel involvement was more common in elderly patients. Older patients had longer hospital stays and more in-hospital MACE including deaths. By multivariate analysis, shock was found to be an independent predictor of MACE in both groups. Conclusion: Younger ACS patients have a different risk profile and better in-hospital outcomes compared to older patients.
Keywords: Acute coronary syndrome, major adverse cardiac events, risk factors, stroke
|How to cite this article:|
Bush N, Sharma YP, Prasad K, Kumar P, Mehrotra S. Comparison of demographic profile, risk factors, and in-hospital outcome in young and old patients with acute coronary syndrome: A single-center experience. J Family Med Prim Care 2021;10:871-6
|How to cite this URL:|
Bush N, Sharma YP, Prasad K, Kumar P, Mehrotra S. Comparison of demographic profile, risk factors, and in-hospital outcome in young and old patients with acute coronary syndrome: A single-center experience. J Family Med Prim Care [serial online] 2021 [cited 2021 Apr 21];10:871-6. Available from: https://www.jfmpc.com/text.asp?2021/10/2/871/310305
| Introduction|| |
The contribution of coronary artery disease (CAD) to cardiovascular disease burden is increasing in India., Acute coronary syndrome (ACS), which includes unstable angina, non-ST elevation myocardial infarction (NSTEMI), and ST elevation myocardial infarction (STEMI) is the major cause of mortality in CAD. Compared with people of European ancestry and western population, CAD occurs a decade earlier in Indians.,
Recently, the frequency of acute myocardial infarction has been increased in the younger population. Young patients (<40 years) with ACS had a high prevalence of smoking, family history of CAD, dyslipidemia, myocardial infarction with normal coronary arteries (MINOCA), and single-vessel disease (SVD)., Several risk factors of ACS have been reported; however, their role in the pathogenesis of ischemic heart disease and their importance in determining the clinical outcomes among young patients is still not convincingly established. Thus, the present study was designed to elucidate these lacunae in the demographic and risk profile of the younger ACS patients.
| Material and Methods|| |
This was a prospective, cross-sectional observational study conducted at a tertiary hospital of North India between January 2016 and May 2017. The study enrolled 50 consecutive patients with ACS, fulfilling the inclusion criteria, each into young (<45 years) and old (≥45 years) groups, respectively. The study strictly followed the standard clinical guidelines and institutional ethics committee has approved the study. Written informed consent was obtained from all patients or their guardians prior to enrollment.
Patients with an age ≥18 years, unstable angina: STEMI and NSTEMI were included in the study. However, patients who had already undergone revascularization by percutaneous or surgical [percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG)] or had developed stent thrombosis were excluded.
Definitions and data collection
Patients of unstable angina, NSTEMI, and STEMI were diagnosed based on previously established standard definitions.,
Chest pain was recorded either as typical angina, atypical angina, or nonanginal chest pain. Details of risk factors including age, gender, and family history of premature CAD (first-degree relatives with males <55 years and females <65 years) were noted. History of diabetes, hypertension, and other comorbidities in both groups were recorded. Anthropological data including body mass index (BMI) and waist circumference were recorded and categorized based on Asian standards and definitions.
Smoking history was also taken from all patients. Active smokers were defined as those who reported smoking at least 100 cigarettes in their lifetime and who, at the time of study, smoked either every day or some days. Ex-smokers as those who quit 30 days before enrollment and never smokers as those who had smoked less than 100 cigarettes in their lifetime. Self-reported physical activity data was collected using the international physical activity questionnaire form and based on these patients were divided into low, moderate, and high level of activity.,
Complete lipid profile was examined, and patients were classified as having dyslipidemia if they have represented low-density lipoprotein (LDL) >100 mg/dL, high density lipoprotein (HDL) <40 mg/dL in males and <50 mg/dL in females, triglycerides >150 mg/dL, or total cholesterol >200 mg/dL. Clinical chemistry analyzer based on particle-enhanced turbid metric immunoassay method was used to estimate high-sensitive C-reactive protein (hs-CRP) levels in serum. Plasma homocysteine levels were measured using chemiluminescence immunoassay method, and apolipoprotein-A1 was analyzed based on nephelometry.
All patients were followed up during hospital stay and in-hospital outcomes were noted. Pharmacotherapy in the form of aspirin, P2Y12 inhibitors (clopidogrel), statins, angiotensin converting enzyme inhibitor/angiotensin receptor blocker, beta-blockers, or novel antianginal drugs were given to all the patients. Major adverse cardiovascular events (MACE), including deaths, reinfarction, stroke, resuscitated cardiac arrest, and major bleeding were also determined during admission and at follow-up.
Mean or median was used for quantitative variables and frequencies or proportions for qualitative variables. Comparison between groups was done using Chi-square test for qualitative variables and independent t test or Mann Whitney U test for quantitative variables. Multivariate analysis was done to find independent predictors of MACE. All tests were two-tailed, and P value < 0.05 was considered as significant. Statistical analysis was done using SPSS 22 version.
| Results|| |
The mean age of patients in the younger group was 36 ± 4.69 years and in the elderly group was 61.58 ± 10.69 years. The percentage of females in the elderly group was almost double than that of younger group (33.3% vs 18.8%). A comparison of complete baseline and clinical profile of both groups is depicted in [Table 1].
In the elderly group, STEMI was found in 62.5%. However, in the younger group NSTEMI and unstable angina were more common (58.4%). Majority of patients presented with typical angina pain (83.3% in elderly group vs 68.8% in the younger group). Dyspnea and palpitations were more common in the elderly group. Furthermore, sympathetic over activity manifested as tachypnea, tachycardia, and sweating were more prevalent in the older age group. Among all, 79.2% younger patients presented with Killip class-1, while 58.3% of the elderly group had Killip class-1, and the remaining 41.7% of elderly belonged to Killip class ≥2. More number of elderly patients (6) than younger patients (1) developed cardiogenic shock (Killip class 4). Two patients from the elderly group reported a murmur of mitral regurgitation on examination.
More number of elderly patients were diabetic (20.8% vs 41.7%, P = 0.009). Four patients in each group had a history of a prior stroke. Low level of physical activity was significantly prevalent in the elderly patients. Arrhythmias were also of higher prevalence in the older age group. Left ventricular systolic function was preserved (ejection fraction >55%) in a higher proportion in younger patients compared to the elderly group (27 vs 21 patients, P = 0.031). Severe left ventricular systolic dysfunction (9 vs 1 patients) and mitral regurgitation was higher in the older patients (5 vs 2 patients).
Total cholesterol (209.82 ± 35.47 vs 197.96 ± 43.57, P = 0.147), low-density lipoprotein (LDL) (162.03 ± 31.21 vs 141.53 ± 40.27, P = 0.006), triglyceride (192.32 ± 34.78 vs 177.19 ± 52.92, P = 0.10), and high-sensitivity C-reactive Protein (hs-CRP) levels (39.16 ± 29.21 vs 29.52 ± 30.42, P = 0.11) were numerically higher in the elderly group. On the other hand, the younger group had higher levels of high-density lipoprotein (HDL) (42.48 ± 12.81 vs 40.99 ± 12.23, P = 0.56), homocysteine (16.23 ± 12.42 vs 13.86 ± 7.00, P = 0.25), and apolipoprotein A1 (125.44 ± 39.25 vs 122.74 ± 30.09, P = 0.706). One patient in each group was taken up for coronary artery bypass graft (CABG). Thrombolysis was done in a higher proportion of the older patients (27.1% vs 22.9%, P = 0.63). Coronary angiography was done in all patients, while percutaneous coronary intervention (PCI) was carried out in a higher number of younger patients compared to the older subjects (79.2% vs 70.8%, P = 0.346) Clinical findings and procedural characteristics were shown in [Table 2].
The incidence of MACE was reported in 17 (35.4%) elderly patients and 5 (10.4%) younger patients. The incidences of arrhythmias (6.3% vs 2.1%, P = 0.61), cardiac arrest (10.4% vs 4.2%, P = 0.435), and in-hospital myocardial infarction (2.1% in older group and none in younger group) were numerically higher in the older age group. The management and clinical outcomes are depicted in both the groups are depicted in [Table 3]. A univariate analysis represented a significant association of MACE with shock, dyslipidemia, hs-CRP levels, and significant CAD on angiography [Table 4]. Furthermore, multivariate logistic regression analysis of significant univariate variables with that of MACE showed shock to be a significant variable in determining MACE [Table 5].
|Table 4: Univariate analysis of risk factors for major adverse cardiovascular events|
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|Table 5: Multivariate logistic regression for risk factors for major adverse cardiac events|
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| Discussion|| |
India as a developing nation has witnessed major transitions in all spheres. One arena of change relevant to us has been the change in demographic patterns of certain diseases., Widespread globalization and alteration in socio-economic factors have altered the epidemiology of major noncommunicable diseases as well. Recently, CAD has grown to epidemic proportions, and there has been a demographic shift of the ACS spectrum in younger populations.,
In this comparative cross-sectional study between young and old ACS patients, we found STEMI as common in the older patient while NSTEMI and unstable angina were more prevalent in the younger patients. Diabetes mellitus, hypertension, dyslipidemia, and ex-smoking were more prevalent in the elderly group. While current smoking, family history of premature CAD, and hyper homocystinemia were more common in the younger group. Single-vessel involvement on angiography was more prevalent in the younger population while multi-vessel involvement was more common in the older population. MACE including in-hospital mortality was higher in the older population, and shock was found be an independent predictor of the same during hospital stay.
In our study, mean age of young and elderly patients was (36.23 ± 4.69 years vs. 61.58 ± 10.69 years) with 1/3rd being male population in both groups. These findings were similar to majority of all previous studies including the national registry of myocardial infarction (NRMI), which showed the ACS frequency in older group to be double than that in the younger age group (32.3 vs 16%). STEMI was more frequent in elderly patients than younger patients (62.5% vs 41.7%). NSTEMI and unstable angina were more frequent in the younger population. These results were supported by the observations of Avezum et al. from the Global Registry of Acute Coronary Events (GRACE). In the Kerala registry, STEMI was found in 37%, NSTEMI in 31%, and unstable angina in 32% of the patients. While in the HP ACS registry, NSTEMI and unstable angina (54.5%) outnumbered the number of STEMI cases (45.5%). This difference could be attributed to multitude of factors ranging from berkesonian bias, sample size, population genetics, difference in risk factors, and heterogeneity of presentation of ACS spectrum.
Another most common risk factor is the lifestyle and physical activity status of the individual. In our study, we found that majority of patient had moderate levels of physical activity as calculated by the International Physical Activity Questionnaires (IPAQ) scoring system (58.3% in the younger group and 54.2% in the older group). Sedentary lifestyle/low-level activity was more prevalent in the elderly group (41.7% vs 27.1%). These findings were discordant with a study by Marcus et al. where sedentary behavior was found as a significant risk factor in younger individuals as well. The difference in these results could be attributed to different methods of measuring physical activity, different population with different socio-demographic features, and difference in sample size. Prevalence of diabetes was higher in the older group (45.8% vs 20.8%).
We found the mean levels of cholesterol, LDL, triglycerides, and hs-CRP were significantly higher in the elderly population while HDL levels in elderly was comparatively lower. Similarly, Obaya et al. found dyslipidemia to be the most common risk factor for the elderly (96.8%) patients. This was, however, discordant with Avezum et al. of the GRACE registry, which stated that dyslipidemia in the elderly patients was 35%. In our study, homocysteine levels were higher in the younger group. F Martin–Herrero et al. found that high levels of homocysteine were strong predictors of cardiac events in young patients with ACS.
Single-vessel involvement was more common in the younger group (39.6% vs 27.1%) while double-vessel involvement was more common in the elderly group (45.8% vs 33.3%). Similarly, Zimmermann et al. also found single-vessel involvement to be more common in the younger group while multi-vessel in the elderly population.
Thrombolytic therapy was given to 27.1% and 22.9% in the older and younger age group, respectively. We found PCI rates were higher in the younger population (79.2% vs 70.8%), which was in line with previous studies.,, We found shorter hospital stays and better clinical outcomes in younger patients. The presence of shock was found to be a significant determinant of MACE. These findings were in line with previous reports.,, The overall in hospital mortality was 9.3%, which was higher than that reported in HP registry (7.6%), CREATE registry (5.6%), and the Kerala registry, which can be explained by our study being limited to a single tertiary center receiving terminal referrals and the limited sample size compared to these studies.,,
Acute coronary syndrome is initially diagnosed by primary care physicians. The knowledge of demographic profile, risk factors, and the nature of the disease helps primary physicians in identifying at early stages those who require aggressive management and risk factor modification. With our study, we could identify the demographic profile and risk factor pattern of young and old patients of ACS. Empowered with the findings of this study, primary care physicians could be able to identify those high-risk patients at first contact, and it will help in effective management of ACS patients.
The major drawback of this study is the sample size. Moreover, multiple comparisons with small sample size increase the probability of type-1 and type-2 errors. Despite these, the study has allayed relevant doubts with regards to the demographic and risk factor profile of ACS in young patients. A longer outpatient follow-up after discharge could have added more validity to our observations.
| Conclusion|| |
In conclusion, NSTEMI ACS with atypical angina tends to be more frequent in the young patients with involvement of single-vessel CAD. In elderly patients, Killip class ≥2 and MACE including in-hospital mortality were higher, and shock was found to be an independent predictor of the MACE during hospital stay.
Ethics approval and consent to participate
The institutional ethics committee PGIMER Chandigarh has approved the study, with reference number NK/2569/MD/1564-65. Written and informed consent has been taken from the participants.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]