|Year : 2020 | Volume
| Issue : 3 | Page : 270-277
Frailty in Elderly Patients with Acute Coronary Syndrome in India
Amit Kumar1, Praveen Singh2, Aarti Gupta3, Saurabh Sharma2
1 Nephrology, Action Super Speciality Hospital, Delhi, India
2 Department of Cardiology, Rajiv Gandhi Super Speciality Hospital, Delhi, India
3 Department of Critical Care and Emergency, Rajiv Gandhi Super Speciality Hospital, Delhi, India
|Date of Submission||13-Aug-2020|
|Date of Decision||07-Sep-2020|
|Date of Acceptance||22-Nov-2020|
|Date of Web Publication||23-Dec-2020|
Dr. Praveen Singh
Rajiv Gandhi Super Speciality Hospital, Delhi
Source of Support: None, Conflict of Interest: None
Background: Association of frailty in Indian elderly patients with acute coronary syndrome (ACS) has not been investigated. Our objective was to estimate the prevalence of frailty due to the severity of ACS in elderly patients in India. The study also aimed to find out the association between the number of health deficits with ACS severity among elderly patients and to explore the association of frailty with conventional risk factors for coronary artery disease among the patients. Materials and Methods: A cross-sectional study was performed for a period of 18 months where 100 elderly patients ≥60 years age, having ACS, regardless of gender, were recruited from the Department of Medicine, Guru Teg Bahadur Hospital, Delhi. Frailty was assessed with the help of Fried index along with a number of deficits in health. Assessment of ACS was done on the basis of the ECHO findings that took into account the left ventricular ejection fraction. Results: The occurrence of frailty was 44% among elderly patients presenting with ACS. The association between the number of health deficits with the severity of ACS at presentation among elderly patients was found to be statistically significant (P < 0.001), with ACS severity increases as health deficit increases. No significant association was observed on co-relating each conventional factor with frailty, while a strong association between depression and frailty was observed (P < 0.001). Conclusion: This study concludes that in Indian elderly patients, there is an important association between frailty and ACS in terms of incidence, diagnosis, and prognosis.
Keywords: Acute coronary syndrome, cardiovascular disease, frailty, Fried's index
|How to cite this article:|
Kumar A, Singh P, Gupta A, Sharma S. Frailty in Elderly Patients with Acute Coronary Syndrome in India. J Pract Cardiovasc Sci 2020;6:270-7
|How to cite this URL:|
Kumar A, Singh P, Gupta A, Sharma S. Frailty in Elderly Patients with Acute Coronary Syndrome in India. J Pract Cardiovasc Sci [serial online] 2020 [cited 2021 Apr 17];6:270-7. Available from: https://www.j-pcs.org/text.asp?2020/6/3/270/304530
| Introduction|| |
Frailty is a common clinical syndrome in older adults characterized by increased susceptibility to stressors due to a decline in reserve and function across multiple physiologic systems such that there is an inefficiency of mobility, strength, balance, motor processing, cognition, nutrition, endurance, and physical activity., Common symptoms of frailty include weight loss, low physical activity, exhaustion, slow walking speed, and low grip strength. One of the leading causes of morbidity and mortality among older individuals is the occurrence of cardiovascular disease (CVD), and thus, their effective management will have a substantial impact on the health benefits.
Epidemiological studies have consistently demonstrated that frailty carries a relative risk of >2 for mortality and morbidity across diverse forms of CVD, thus demonstrating the utility of frailty assessment as a prognostic marker. Frail patients with CVD, especially those undergoing invasive procedures or suffering from coronary artery disease (CAD) and heart failure (HF), are more likely to suffer adverse outcomes as compared to nonfrail counterparts. Despite acceptance of frailty as a key factor in the evaluation of older adults with CVD, a road map to promote its inclusion still remains in daily clinical practice.
CAD or atherosclerosis is a long-lasting and continually progressing CVD with various clinical symptoms, ranging from asymptomatic to stable angina, acute coronary syndrome (ACS), HF, and sudden cardiac death. ACS includes unstable angina (UA) and acute myocardial infarction. Frailty and CVD share a common biological pathway, and frailty may hasten CVD progression with a greater impact in elderly patients with ACS. Frailty is paramount importance to cardiologists due to the aging of patients admitted to hospital for ACS and a clear association between aging and frailty, thus representing one of the greatest challenges for health-care professionals dealing with aging populations.
Data from observational studies and some systematic reviews have shown a significant correlation between frailty and CVD morbidity and mortality in the elderly population globally,,,,, and assessing these patients has been found to be instrumental in improving their prognosis and in determining optimum care and treatment for elderly. However, the association of frailty in context with ACS among elderly patients in India has not been explored. The rationale of the study was therefore to determine the occurrence of frailty in view of severity at the presentation of ACS among elderly patients in India. The study also aimed to find out the association between the number of health deficits with ACS severity among elderly patients and to explore the association of frailty with conventional risk factors for CAD among the patients.
| Materials and Methods|| |
A cross-sectional study was performed from November 2014 to April 2016 for a period of 18 months in the Department of Medicine, Community Medicine, and Biochemistry in Guru Teg Bahadur Hospital, Delhi - 110 095, following the guidelines laid down in the Declaration of Helsinki (2013).
Approval from Institutional Ethics Committee, Human Research, University College of Medical Sciences, University of Delhi, Delhi, on October 30, 2014, was obtained for conducting the study. Informed written consent was obtained from the patients maintaining confidentiality about the study subjects for research and educational purposes. Every patient was given the utmost care during the study.
Elderly patients aged 60 or older, regardless of gender, were recruited from the emergency medicine, medical wards, and coronary care unit (CCU). The total number of subjects recruited for the study was 100. The inclusion criteria were elderly patients ≥60 years age having any of the spectrum of ACS, i.e., ST elevation myocardial infarction (STEMI), UA, non-ST elevation myocardial infarction (NSTEMI), and who gave their full, voluntary, and informed consent for the study. Myocardial infarction (MI) was defined using the Third Universal Definition of MI. Patients who were unable to give interview responses, for whom no reliable informant was available for answering questionnaire, and those who did not complete the questionnaire were excluded from the study.
Sample size calculation
Frailty was identified in 25%–50% of patients with CVD, depending on the frailty scale used and the population studied. The expected prevalence is being set as 50% to give the maximum sample size for a given level of precision. Assuming an expected prevalence of frailty as 50% among ACS patients, with 95% confidence interval (CI), and 10% absolute precision, the minimum sample size required was calculated to be 96 using Epi Info software (Epi Info Software, Center for Disease Control and Prevention, Atlanta, Georgia, USA).
Assessment of frailty was made on the basis of a questionnaire. The questionnaire includes five criteria defined by Fried et al. under the Fried index and the modification used by Wilhelm-Leen et al. Frailty is defined as the presence of at least three of the following five conditions:
- Unintentional weight loss: more than 5 kg in the past 1 year
- Slowness: time to walk 15 feet, adjusting for gender and standing height
- Weakness: Weakness is defined by grip strength stratified by gender and body mass index (BMI) quartiles
- Exhaustion: Exhaustion is present if subjects answered “some difficulty,” “much difficulty,” or “unable to do” when asked about how much difficulty they have “walking from one room to another on the same level”
- Low physical activity: if the subject felt less active as compared to most men/women of their age.
The walk test and grip test were done preferentially at the time of discharge of the patient keeping in view to minimize the effect of acute cardiac event on patients' performance.
A number of deficits in health were also studied based on the standard procedure for creating a frailty index and list of deficits given by Searle et al. It was done with the help of a questionnaire that includes a number of health deficits. Each variable was given a score of 0 and 1 where 0 indicates that that the patient is not having that particular deficit and 1 indicates that the patient has that particular deficit. In case of pill burden, if the patient is taking >4 pills per day, it is considered a deficit and 1 is given as a score. In case of get up and go test, deficit is counted as 1 if the patient takes >16 s for the test. All the health deficits are added and a final score is derived reflecting the total number of health deficit counts in a particular patient. The questionnaire includes the following questions regarding various organ systems and other personal inquiries: (1) sensory-organ involvement, (2) musculoskeletal system, (3) nervous system, (4) cardiovascular and renal system, (5) gastrointestinal system, (6) respiratory system, (7) endocrine system and reproductive organs, (8) activities of daily living, (9) psychological assessment, (10) personal inquiries, (11) drug history, (12) other miscellaneous, and (13) objective parameters that were measured on clinical examination.
Based on the questionnaire, the number of health deficits is calculated and the relationship between number of health deficits and severity of ACS at presentation is tried to establish between the two.
Assessment of severity of acute coronary syndrome
Assessment of ACS was done on the basis of the ECHO findings that took into account the left ventricular ejection fraction (LVEF%). Echocardiography was done preferably within 24 h and definitely before discharge of the patient. LVEF was estimated for each patient and its value was titrated with the number of deficits in health. A note of conventional risk factors was made for each patient. In all subjects, relevant investigations were done as per case record form which included hemoglobin, fasting and postprandial blood sugar, lipid profile, peak expiratory flow rate, and electrocardiogram.
All analyses were performed using Statistical Package for the Social Sciences (IBM Inc., Version 20, New York, USA). The statistical analysis comprised calculating means and proportions. Chi-square test (Fisher's exact test, if required) was used to test the differences in categorical measures. Correlation coefficient was analyzed for association between continuous variables. Statistical significance was set at P < 0.05.
| Results|| |
Sociodemographic characteristics and presentation of the study participants
The median age of study participants was 63.5 years (interquartile range [IQR]: 60–65), with BMI more than 23 in majority of the subjects. The mean height of the patients was 164.64 cm with a standard deviation (SD) of 8.25. The average weight of the study participants was around 64.94 kg (SD: 13.24). The mean waist circumference of the patients was about 83.80 cm (SD: 9.60). The cutoff taken for raised waist circumference was >90 cm for males and >80 cm for females. Males (73%) were in a higher proportion than women (27%) among the study participants [Figure 1] and [Table 1] and [Table 2]. Forty-seven percent presented with STEMI, 36% with NSTEMI, and 17% with UA, STEMI being the most common presentation.
|Figure 1: The median age of the study participants was 63.5 years (interquartile range: 60–65). The minimum age was 60 years and the maximum age of the patient included in the study was 82 years.|
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Socioeconomic characteristics of the subjects
Socioeconomic characteristics of the study subjects revealed that 57% of the subjects were living in nuclear families, of which 30% of the study subjects were unemployed. Most of the women were housewives, 25 of 27. Majority of the patients were financially dependent (63%), either fully or partially. The study subjects also revealed that 70% had smoking habits in form of either cigarette, beedi, or smokeless tobacco. Twelve percent (12%) of the patients used to take alcohol on a daily basis [Table 3].
Conventional risk factors for coronary artery disease in the study subjects
The distribution of conventional risk factors for CAD among the study subjects is depicted in [Table 4]. As old age is a risk factor for CAD, and as all our sample was 60 years and above, all have been considered as having the risk factor positive. Hence, eight risk factors are studied in our study including age [Table 4].
|Table 4: Occurrence of conventional risk factors in the study subjects (n=100)|
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Furthermore, we also considered the number of deficits in health in study subjects. The deficit counts have been arranged in descending order in the table. In this case, difficulty in chewing is the most common deficit, present in 84% of the patients [Table 5].
Fried's index: The tool for calculating frailty score
Frailty score was calculated with the help of Fried's index among the study subjects. This table shows the percentage of the patients with one or other criteria of the frailty index. Among the subjects, the slow gait speed was found to be the most common parameter [Table 6].
On the basis of distribution of Fried's criteria, a bar graph was plotted which shows the frailty score on frailty scoring system. Out of 100 patients, 27 had a frailty score of 0. Eighteen had 1, 11 had 2, 6 had 3, 28 had 4, and 10 had 5 as their frailty score. The median is zero (IQR: 0–4). Patients were designated as nonfrail with the score 0 and with the score 1 or 2, they were considered as prefrail. Frail patients are those who had their frailty score as 3 or >3. Thus, we observed that 30% of the patients were nonfrail, 26% were prefrail, and 44% were frail as per the Fried's criteria. Hence, in our study, the occurrence of frailty was 44% among elderly patients presenting with ACS [Figure 2].
|Figure 2: (a) Distribution of frailty score as per the Fried's criteria among patients (b) Distribution of frailty status as per the Fried's criteria among subjects (n = 100).|
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Furthermore, the association between the number of deficits in health with the severity of ACS at presentation among elderly patients was found to be statistically significant. The Pearson's correlation coefficient for the association between deficits in health and LVEF was −0.485 (P < 0.001). As the number of deficits in health increased, the severity of ACS at presentation (as reflected by a poorer LVEF) was also increased [Figure 3].
|Figure 3: Association between the number of health deficits and severity of acute coronary syndrome at presentation.|
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Association of frailty with conventional risk factors and other risk factors
We further studied the association of frailty with conventional and other risk factors (like depression and anemia). No significance was observed among frailty and conventional risk factors such as smoking, hypertension, diabetes, family history of CAD, abnormal waist circumference, dyslipidemia, male sex, and age (P > 0.05) [Table 7]. However, frailty was associated with other risk factors such as depression and anemia (P < 0.05) [Table 8] and [Table 9].
|Table 8: Association of depression and frailty in the study subjects (n=100)|
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|Table 9: Association of anemia and frailty in the study subjects (n=100)|
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| Discussion|| |
In the present study, we explored the association of frailty with ACS in elderly patients. Frailty, a geriatric syndrome of increased vulnerability to stressors due to impairments in multiple interrelated systems, provides a multidimensional approach toward patient management. The study was done to find the occurrence of frailty, its association with the conventional risk factors for CAD, and its impact on the outcome. A number of health deficits were also studied in these elderly patients and used to determine if there was an association between the health deficits and severity of ACS at presentation.
In this study, the median age of the study participants was 63.5 years (IQR: 60–65) which was found to be in conjunction of reports of Gale et al., Cesari et al., and Kojima where similar age groups were studied.,, Our study also revealed a gender specific affect with respect to frailty where females were more susceptible toward frailty (59.3%) than males (38.4%). A meta-analysis conducted by Collard et al. revealed that the average prevalence of frailty was significantly higher in women (9.6%, 95% CI: 9.2%–10%) than in men (5.2%, 95% CI: 4.9%–5.5%) (P < 0.001). Similar reports were also reported by Chen et al. (P < 0.001).
In our study, 81% of patients lived with their spouse and children, 42% of whom were found to be frail. On the other hand, frailty in the remaining subjects who did not live with their partner was 52.6%. This was backed by a study by Kuroda et al. who found that there was a greater proportion of serious impairment in patients who did not stay with their spouse. Neri et al. also suggested that frailty was more prevalent in widowed. Furthermore, our study also showed that 42% of patients had an abnormal BMI where males (24.27) had a higher mean BMI than females (23.43). However, this was not in accordance with a study Hatahet et al. in which the females had higher mean BMI, probably due to the reason of a different population-based study. In our study, 57% of subjects resided in nuclear families. Frailty in nuclear families was 54.4% of the cases and in case of joint families, it was 30.2%. As no research has been identified to the best of our knowledge, the possible reason for a lower prevalence of frailty in joint families could be sufficient social support from a larger family.
All of the participants enrolled in the study were at CAD risk due to the elderly age group, of which 29 patients had a positive CAD family history. This reflected a very strong association between positive family history and ACS and was in accordance with Burazeri et al. where positive family history in 26% men and 33% women were reported. Our study also indicated that the majority of subjects with ACS used tobacco on a daily basis (67%), which was consistent with González-Pacheco et al. (68%). Similarly, in our study, 39% of patients had diabetes, while 57% had hypertension which tally with the similar findings conducted by González-Pacheco et al. Moreover, 92% of our study subjects had dyslipidemia which is in conjunction with the study of González-Pacheco et al. (85.1%). Almost all the conventional risk factors were present in higher frequencies in our study subjects, indicating their importance in the pathophysiology of heart disease. However, on co-relating each conventional factor with frailty, we found no significant association between them.
Besides conventional risk factors, we studied depression as a risk factor that may predispose individuals to CAD. Out of the 100 patients, 29% of patients were found to be depressed on the depression scale, of which 27 were frail (P < 0.05), thereby reflecting a strong association of depression with frailty. Uchmanowicz and Gobbens reported that frailty had a negative impact on health-related quality of life, with participants showing increased levels of frailty, anxiety, and depression. This was supported by another study by Mezuk et al. who examined the joint relationship between the constructs of depression and frailty. Anemia was also associated with frailty in our study where frail subjects had hemoglobin: 12.45, lower than the nonfrail counterparts (P < 0.05). This result was in accordance with the study by Chaves et al. where low hemoglobin was related to frailty and CVD status.
Frailty was identified based on the Fried's criteria. Patients with a score 0 were categorized as nonfragile, with a score of 1 or 2 as prefrail, and with a score of 3 or more as frail. Based on this classification, our study had 30% nonfrail, 26% prefrail, and 44% as frail subjects. Such results were consistent with Joosten et al.'s research findings where 1.5% of the study patients were classified as nonfrail, 58.5% as prefrail, and 40% as frail. Alonso Salinas et al. in a prospective, observational study observed that frail patients (n = 71, 35.1%) were older, more often women and had higher comorbidities. Similarly, another study by Kang et al. reported that 43.18% were frail and concluded that frailty was strongly and independently associated with short-term outcomes for elderly ACS patients. Gait speed is the single best parameter to assess the frailty status. In our study, about 58% of patients had lower gait speed and 44% had frailty prevalence. This was in relation to a cross-sectional study by Castell et al. where walking speed <0.8 m/s is considered to be the simplest approach to diagnose frailty in primary care setting.
Our study also reported 74 health deficits. These were divided into clinical parameters, previously established comorbidities, dependence, disabilities, and few other parameters of morbidities. Of 100 subjects, 84% had trouble chewing and 82% took more than 4 pills a day, i.e., polypharmacy. We correlated the number of deficits in health with the severity of ACS at presentation. ACS severity was determined by LVEF (%). The association between the number of deficits in health and LVEF (%) reflecting the severity of ACS at presentation among elderly patients was found to be statistically significant (P < 0.001). As the number of health deficits increased, ACS severity also increased at presentation (as reflected by poorer LVEF).
| Conclusion|| |
From the study, we therefore conclude that there is a significant association between frailty and ACS in Indian elderly patients in terms of occurrence, treatment, and prognosis. Frailty is also associated with anemia in this population.
Ethical clearance was given by the Institutional Research Board. All patients were included according to Inclusion and exclusion criteria after written informed consent.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9]