Abstract
Background
There has been limited evidence about frailty in older patients with acute coronary syndrome (ACS) in Vietnam.
Aim
(1) To investigate the prevalence of frailty in older patients hospitalised with ACS and its associated factors; (2) To investigate the impact of frailty on percutaneous coronary intervention (PCI) and adverse outcomes in this population.
Methods
Patients aged ≥60 with ACS admitted to two teaching hospitals in Vietnam were recruited from 9/2017 to 4/2018. Frailty was defined by the Reported Edmonton Frail Scale. Multivariate logistic regression was applied to investigate the associated factors of frailty and the impact of frailty on PCI and adverse outcomes.
Results
There were 324 participants, mean age 73.5±8.3, 39.2% female. The prevalence of frailty was 48.1%. Advanced age, female gender, history of hypertension, heart failure, stroke and chronic kidney disease were significantly associated with a frailty status. Overall, 50.3% of the participants received PCI (58.3% in the non-frail vs 41.7% in the frail, p=0.003). However, frailty did not have an independent impact on PCI (adjusted OR 0.66, 95% CI 0.41–1.08). Frailty was significantly associated with increased risk of having arrhythmia during hospitalisation (adjusted OR 2.24, 95% CI 1.32–3.80), hospital-acquired pneumonia (adjusted OR 2.27, 95% CI 1.24–4.17), in-hospital mortality (adjusted OR 3.02, 95% CI 1.35–6.75), 30-day mortality (adjusted OR 3.28, 95% CI 1.59–6.76), and 30-day readmission (adjusted OR 2.53, 95% CI 1.38–4.63).
Conclusion
In this study, frailty was present in nearly half of older patients with ACS and was associated with increased adverse outcomes. These findings suggest that frailty screening should be performed in older patients with ACS in Vietnam.
Keywords: frailty, acute coronary syndrome, elderly, older patients, adverse outcomes, Vietnam
Introduction
Coronary heart disease is the world’s leading cause of mortality.1,2 Increasing age was associated with an increased incidence of acute coronary syndromes (ACS) and higher rates of adverse events after ACS.3 In older patients with ACS, the presence of frailty, a state of increased vulnerability and reduced physiological reserve, can create a burden for these patients.4,5 The development of frailty involves multiple physiological factors, including the cardiovascular systems.6,7 Previous studies showed that frailty was common in older people with cardiovascular disease, and was associated with increased adverse outcomes.8–19 In older people presenting to hospital with ACS, nearly one-third were frail, and they were less likely to receive an invasive coronary strategy and pharmacological therapies according to the current guidelines.20
Over the past decades, the global burden of coronary heart disease has shifted towards low- and middle-income countries.21 Vietnam is a lower-middle-income country in the Southeast Asia region with rapid urbanisation. In Vietnam, the proportion of older people (aged 60 or over) is increasing, with an estimate of 26.1% of the population in 2049.22 Cardiovascular disease is the leading cause of death in Vietnam.23–26 However, the evidence of frailty in older Vietnamese people is limited. In one study conducted in 2015, the prevalence of frailty in older hospitalised patients was 31.9%.27 There has been no study of frailty in older patients with ACS. Therefore, this study aims to investigate the prevalence of frailty in older patients hospitalised with ACS and its associated factors, and to investigate the impact of frailty on percutaneous coronary intervention (PCI) and adverse outcomes in this population.
Methods
Participants
A prospective cohort study was conducted in patients with ACS admitted to Thong Nhat Hospital in Ho Chi Minh City (Interventional Cardiology Department) and Cho Ray Hospital (Interventional Cardiology Department, Cardiology Department) from 9/2017 to 4/2018.
Inclusion criteria: age ≥60 and was diagnosed with ACS at this admission. Exclusion criteria include: (1) severe illness (dying or receiving intensive care), (2) blind or deaf, (3) severe dementia or delirium, (4) unable to speak or understand Vietnamese language.
The study was approved by the ethics committees of the University of Medicine and Pharmacy in Ho Chi Minh City, Cho Ray Hospital and Thong Nhat Hospital. Written informed consent was obtained from all participants.
Sample Size Calculation
Sample size was calculated for the first aim of this study. The sample size was determined using a single population proportion formula: n=Z2 1- α/2 * [p*(1-p)/d2], with n = the required sample size, Z1- α/2 = 1.96 (with α = 0.05 and 95% confidence interval), p = prevalence of frailty in older patients with ACS, and d = precision (assumed as 0.05). Previous studies showed that the prevalence of frailty in older patients with ACS ranged from 30.1% to 43.2%.28–30 Therefore, the sample size for this study is calculated to be at least 324 participants.
Data Collection
Data were collected from patient interviews and from medical records. Information obtained from medical records included: demographic characteristics, height, weight, medical history, comorbidities, admission diagnosis, Killip class, PCI during hospitalisation, and events during hospitalisation (arrhythmia, acquired pneumonia, cardiogenic shock, stroke, major bleeding, recurrent myocardial infarction, death, and length of stays).
All participants were followed up for 30 days after discharged. Phone calls were conducted to the phone numbers provided by participants to obtain information about readmission and mortality.
Frailty Definition
In this study, frailty was defined by the Reported Edmonton Frail Scale (REFS). The REFS was chosen because it is a validated tool and more feasible for research in older hospitalized patients. This scale was also recommended by several guidelines to identify frailty, particularly in older patients after ACS.31,32 The REFS has been applied in many studies.27,33–38 In a recent study in the North of Vietnam, the REFS was shown to be as effective as Fried’s frailty phenotype in detecting frailty and predicting mortality in older inpatients in Vietnam.39 The REFS includes nine frailty domains: cognition, general health status, functional independence, social support, medication use, nutrition, mood, continence and functional performance. The REFS has a maximum score of 18, and the cut point used to identify frailty was ≥8, as applied in previous studies using this scale.27,33–38 The REFS is based on a questionnaire on how the patient functioned prior to the illness that brought them into the hospital, is not heavily influenced by the acute illness, easy to apply for older inpatients and less time-consuming (Supplementary Table S1).
Outcome Variables
The primary outcomes of this study are the proportion of receiving PCI, and the adverse event rates during hospitalisation and during 30 days after discharge. Adverse events during hospitalisation included arrhythmia (defined as any of the following arrhythmias: sinus tachycardia, sinus bradycardia, atrial fibrillation, atrial flutter, atrioventricular block, ventricular tachycardia, ventricular flutter), acquired pneumonia, cardiogenic shock, stroke, recurrent myocardial infarction, major bleeding (bleeding that required blood transfusions), and all-cause mortality. Adverse events during 30 days after discharged included all-cause readmission and all-cause mortality.
Statistical Analysis
Analysis of the data was performed using SPSS for Windows 24.0 (IBM Corp., Armonk, NY, USA). Continuous variables are presented as means ± standard deviation, and categorical variables as frequencies and percentages. Comparisons between frail and non-frail participants were conducted using the Chi-square test or Fisher’s exact test for categorical variables and Student’s t-test or Mann–Whitney test for continuous variables.
To identify the factors independently associated with frailty in older patients hospitalised with ACS, multivariable logistic regression analysis was applied. First, univariate logistic regression was performed on all the potential associated factors for frailty on the study data (such as age, sex, comorbidities). Variables that had a p-value <0.20 on univariate analysis were selected for multivariate analysis. A backward elimination method was applied so that the final model retained only those variables significant at p <0.05.
To investigate the impact of frailty on PCI and on adverse events, first, we conducted univariate logistic regression of frailty on the outcome variables. Only outcomes with the number of cases of at least 30 were selected for logistic regression analysis. We also performed univariate logistic regression of other factors that can be associated with PCI and adverse events based on the literature such as age, sex, ACS types, Killip class.40–42 The relationship between frailty with PCI and adverse outcomes was then examined by multivariate logistic regression, adjusted to those variables that had a p-value <0.05 on univariate analyses.
All variables were examined for interaction and multicollinearity. Results were presented as odds ratios and 95% confidence intervals.
Results
There were 324 participants, mean age 73.5 ± 8.3 years, 39.2% female. The prevalence of frailty was 48.1% (40.6% in men, 59.8% in women, p<0.001).
In these studied participants, the most common comorbidity was hypertension, followed by diabetes. Compared to the non-frail, frail patients with ACS were older and had a significantly higher prevalence of hypertension, diabetes, heart failure, stroke, and chronic kidney disease. Frail patients were more likely to present with ST-segment elevation myocardial infarction (STEMI) or non-ST segment elevation myocardial infarction (NSTEMI) rather than unstable angina. Overall, 50.3% of the participants received PCI and this rate was significantly higher in the non-frail compared to the frail (58.3% vs 41.7%, p=0.003, respectively). (Table 1)
Table 1.
Participant General Characteristics
| Characteristics | All (N=324) | Non-Frail (N=168) | Frail (N=156) | P |
|---|---|---|---|---|
| Age, years | 73.48 ± 8.32 | 70.42 ± 7.55 | 76.77 ± 7.87 | <0.001 |
| Female | 127 (39.2) | 51 (30.4) | 76 (48.7) | 0.001 |
| Smoking | 168 (51.9) | 96 (57.1) | 72 (46.2) | 0.048 |
| BMI | ||||
| Underweight (<18.5) | 47 (14.5) | 20 (11.9) | 27 (17.3) | 0.185 |
| Normal (18.5–22.9) | 161 (49.7) | 91 (54.2) | 70 (44.9) | |
| Overweight (≥23.0) | 116 (35.8) | 57 (33.9) | 59 (37.8) | |
| Comorbidities: | ||||
| Hypertension | 247 (76.2) | 119 (70.8) | 128 (82.1) | 0.018 |
| Diabetes | 98 (30.2) | 39 (23.2) | 59 (37.8) | 0.004 |
| Dyslipidaemia | 54 (16.7) | 27 (16.1) | 27 (17.3) | 0.765 |
| Heart failure | 44 (13.6) | 12 (7.1) | 32 (20.5) | <0.001 |
| Previous stroke | 43 (13.3) | 11 (6.5) | 32 (20.5) | <0.001 |
| Previous PCI | 41 (12.7) | 22 (13.1) | 19 (12.2) | 0.804 |
| Previous myocardial infarction | 24 (7.4) | 8 (4.8) | 16 (10.3) | 0.059 |
| Chronic kidney disease | 13 (4.0) | 1 (0.6) | 12 (7.7) | 0.001 |
| Peripheral vascular disease | 8 (2.5) | 2 (1.2) | 6 (3.8) | 0.161 |
| Previous CABG | 2 (0.6) | 0 (0) | 2 (1.3) | 0.231 |
| ACS type: | ||||
| NSTEMI | 133 (41.0) | 57 (33.9) | 76 (48.7) | 0.002 |
| STEMI | 120 (37.0) | 62 (36.9) | 58 (37.2) | |
| Unstable angina | 71 (21.9) | 49 (29.2) | 22 (14.1) | |
| PCI | 163 (50.3) | 98 (58.3) | 65 (41.7) | 0.003 |
| Length of stay (days) | 8.7 ± 5.6 | 8.05 ± 4.85 | 9.40 ± 6.30 | 0.031 |
Notes: Continuous data are presented as mean ± standard deviation. Categorical data are shown as n (%).
Abbreviations: BMI, body mass index; PCI, percutaneous coronary intervention; CABG, Coronary artery bypass grafting; ACS, Acute coronary syndromes; NSTEMI, non-ST segment elevation myocardial infarction; STEMI, ST-segment elevation myocardial infarction.
The components of the REFS in male and female participants are presented in Table 2. Multivariate logic regression showed that age (adjusted OR 1.12, 95% CI 1.08–1.16), female gender (adjusted OR 1.88, 95% CI 1.11–3.17), history of hypertension (adjusted OR 1.88, 95% CI 1.01–3.50), heart failure (adjusted OR 4.08, 95% CI 1.82–9.15), stroke (adjusted OR 4.03, 95% CI 1.80–9.01) and chronic kidney disease (adjusted OR 17.50, 95% CI 2.06–148.52) were significantly associated with a frailty status (Table 3).
Table 2.
Components of the Reported Edmonton Frail Scale in the Studied Participants
| Components | All (N=324) | Male (N=197) | Female (N=127) | P |
|---|---|---|---|---|
| Cognition: clock drawing test | ||||
| No errors | 63 (19.4) | 46 (23.4) | 17 (13.4) | 0.011 |
| Minor spacing errors | 147 (45.4) | 93 (47.2) | 54 (42.5) | |
| Other errors | 114 (35.2) | 58 (29.4) | 56 (44.1) | |
| Health status: | ||||
| Admissions to hospital in the past year | ||||
| No admission | 182 (56.2) | 109 (55.3) | 73 (57.5) | 0.643 |
| 1–2 admissions | 118 (36.4) | 75 (38.1) | 43 (33.9) | |
| >2 admissions | 24 (7.4) | 13 (6.6) | 11 (8.7) | |
| Description of health | ||||
| Excellent/very good/good | 24 (7.4) | 17 (8.6) | 7 (5.5) | 0.148 |
| Fair | 281 (86.7) | 172 (87.3) | 109 (85.8) | |
| Poor | 19 (5.9) | 8 (4.1) | 11 (8.7) | |
| Functional independence: activities requiring help | ||||
| 0–1 activities | 58 (17.9) | 46 (23.4) | 12 (9.4) | 0.001 |
| 2–4 activities | 169 (52.2) | 104 (52.8) | 65 (51.2) | |
| 5–8 activities | 97 (29.9) | 47 (23.9) | 50 (39.4) | |
| Social support: someone able to help | ||||
| Always | 219 (67.6) | 125 (63.5) | 94 (74.0) | 0.047 |
| Sometimes | 105 (32.4) | 72 (36.5) | 33 (26.0) | |
| Never | 0 | 0 | 0 | |
| Medication: | ||||
| Using ≥5 medications | 149 (46.0) | 80 (40.6) | 69 (54.3) | 0.016 |
| Forget to take medication sometimes | 70 (21.6) | 50 (25.4) | 20 (15.7) | 0.040 |
| Nutrition: weight loss | 18 (5.6) | 10 (5.1) | 8 (6.3) | 0.639 |
| Mood: sadness or depression | 98 (30.2) | 55 (27.9) | 43 (33.9) | 0.256 |
| Incontinence | 79 (24.4) | 50 (25.4) | 29 (22.8) | 0.602 |
| Self-reported performance | ||||
| Can do heavy work around the house without help | 70 (21.6) | 52 (26.4) | 18 (14.2) | 0.009 |
| Can go up and down stairs without help | 209 (64.5) | 145 (73.6) | 64 (50.4) | <0.001 |
| Can walk 1 km without help | 68 (21.0) | 47 (23.9) | 21 (16.5) | 0.114 |
Note: Data are shown as n (%).
Table 3.
Factors Associated with Frailty in the Study Participants
| Outcomes | Univariate Analysis | Multivariate Analysis | ||
|---|---|---|---|---|
| Unadjusted OR (95% CI) | P | Adjusted OR (95% CI) | P | |
| Age (per year) | 1.11 (1.08–1.15) | <0.001 | 1.12 (1.08–1.16) | <0.001 |
| Sex | ||||
| Male (reference) | 1 | 0.001 | 1 | 0.019 |
| Female | 2.18 (1.38–3.43) | 1.88 (1.11–3.17) | ||
| Underweight (BMI<18.5) | 1.43 (0.76–2.70) | 0.265 | – | |
| Overweight (BMI≥23.0) | 1.18 (0.75–1.87) | 0.465 | – | |
| History of chronic diseases: | ||||
| Hypertension | 1.88 (1.11–3.19) | 0.019 | 1.88 (1.01–3.50) | 0.047 |
| Diabetes | 2.01 (1.24–3.26) | 0.005 | – | |
| Dyslipidaemia | 1.09 (0.61–1.96) | 0.765 | – | |
| Heart failure | 3.36 (1.66–6.78) | 0.001 | 4.08 (1.82–9.15) | 0.001 |
| Previous stroke | 3.68 (1.79–7.60) | <0.001 | 4.03 (1.80–9.01) | 0.001 |
| Previous PCI/CABG | 0.92 (0.48–1.78) | 0.804 | – | |
| Previous myocardial infarction | 2.29 (0.95–5.50) | 0.065 | – | |
| Chronic kidney disease | 13.92 (1.79–108.33) | 0.012 | 17.50 (2.06–148.52) | 0.009 |
| Peripheral vascular disease | 3.32 (0.66–16.70) | 0.145 | – | |
Notes: All variables with p value <0.20 on univariate analyses were selected for multivariable logistic regression. Backward elimination method was applied and the final model contained only variables with p<0.05.
Abbreviations: BMI, body mass index; PCI, percutaneous coronary intervention; CABG, Coronary artery bypass grafting.
Table 4 describes the event rates during hospitalisation and at 30 days after discharge. Overall, the most common adverse event during admission was arrhythmia (37.7%), followed by acquired pneumonia (24.4%). Compared to non-frail participants, frail participants had significantly higher rates of arrhythmia, acquired pneumonia, cardiogenic shock, major bleeding, recurrent myocardial infarction, in-hospital mortality, 30-day mortality and 30-day readmission.
Table 4.
Adverse Events During Hospitalisation and at 30 Days After Discharged
| Characteristics | All (N= 324) | Non-Frail (N=168) | Frail (N=156) | P |
|---|---|---|---|---|
| In-hospital outcomes: | ||||
| Arrhythmia | 122 (37.7) | 44 (26.2) | 78 (50.0) | <0.001 |
| Acquired pneumonia | 79 (24.4) | 23 (13.7) | 56 (35.9) | <0.001 |
| Death | 48 (14.8) | 10 (6.0) | 38 (24.4) | <0.001 |
| Cardiogenic shock | 32 (9.9) | 13 (7.7) | 19 (12.2) | 0.181 |
| Major bleeding | 15 (4.6) | 4 (2.4) | 11 (7.1) | 0.046 |
| Stroke | 6 (1.9) | 1 (0.6) | 5 (3.2) | 0.110 |
| Recurrent myocardial infarction | 5 (1.5) | 0 (0) | 5 (3.2) | 0.025 |
| 30-day outcomes: | ||||
| 30-day mortality | 68 (21.0) | 15 (8.9) | 53 (34.0) | <0.001 |
| 30-day readmission | 74 (27.9) | 23 (17.2) | 51 (38.9) | <0.001 |
Notes: Continuous data are presented as mean ± standard deviation. Categorical data are shown as n (%).
On multivariable logistic regression analysis, frailty was independently associated with increased risk of arrhythmia, acquired pneumonia, in-hospital mortality, 30-day mortality, and 30-day readmission (Table 5). These multivariate logistic models were adjusted with the variables that showed a significant relationship with the outcome variables on univariate analyses (Table 6). Frailty was significantly associated with a reduced likelihood of receiving PCI on univariate logistic regression (unadjusted OR 0.51, 95% CI 0.33–0.79). However, the relationship became insignificant after allowing for age and sex (adjusted OR 0.66, 95% CI 0.41–1.08).
Table 5.
The Impact of Frailty on PCI Treatment and Adverse Outcomes
| Outcomes | Univariate Analysis | Multivariate Analysis | ||
|---|---|---|---|---|
| Unadjusted OR (95% CI) | P | Adjusted OR (95% CI) | P | |
| Receiving PCI | 0.51 (0.33–0.79) | 0.003 | 0.66 (0.41–1.08)a | 0.095 |
| Cardiogenic shock during hospitalisation | 1.65 (0.79–3.47) | 0.184 | 1.13 (0.47–2.74)b | 0.788 |
| Arrhythmia during hospitalisation | 2.82 (1.77–4.49) | <0.001 | 2.24 (1.32–3.80)c | 0.003 |
| Pneumonia during hospitalisation | 3.53 (2.04–6.11) | <0.001 | 2.27 (1.24–4.17)d | 0.008 |
| Death during hospitalisation | 5.09 (2.44–10.63) | <0.001 | 3.02 (1.35–6.75)d | 0.007 |
| 30-day mortality | 5.25 (2.81–9.81) | <0.001 | 3.28 (1.59–6.76)c | 0.001 |
| 30-day readmission | 3.08 (1.74–5.44) | <0.001 | 2.53 (1.38–4.63)e | 0.003 |
Notes: aAdjusted to age, sex. bAdjusted to Killip class. cAdjusted to age, ACS type, Killip class, PCI. dAdjusted to ACS type, Killip class, PCI. eAdjusted to age.
Abbreviations: PCI, percutaneous coronary intervention; ACS, acute coronary syndrome.
Table 6.
Univariate Regression of Potential Factors Associated with PCI and Adverse Outcomes
| Unadjusted OR (95% CI) for PCI | Unadjusted OR (95% CI) for Cardiogenic Shock | Unadjusted OR (95% CI) for Arrhythmia | Unadjusted OR (95% CI) for Pneumonia | Unadjusted OR (95% CI) for in-Hospital Mortality | Unadjusted OR (95% CI) for 30-Day Mortality | Unadjusted OR (95% CI) for 30-Day Readmission | |
|---|---|---|---|---|---|---|---|
| Age (years) | 0.96 (0.93–0.99), p=0.003 | 0.97 (0.92–1.01), p=0.152 | 1.03 (1.00–1.06), p=0.036 | 1.03 (1.00–1.06), p=0.057 | 1.02 (0.99–1.06), p=0.244 | 1.05 (1.01–1.08), p=0.007 | 1.05 (1.02–1.09), p=0.002 |
| Sex: | |||||||
| Female | Reference | 1 | 1 | 1 | 1 | 1 | 1 |
| Male | 1.86 (1.19–2.93), p=0.007 | 1.47 (0.67–3.22), p=0.334 | 1.17 (0.74–1.86), p=0.508 | 0.71 (0.42–1.18), p=0.183 | 0.73 (0.39–1.35), p=0.309 | 0.72 (0.42–1.23), p=0.226 | 1.20 (0.69–2.09), p=0.516 |
| ACS type: | |||||||
| Unstable angina | Reference | N/A (event rates were too small for this analysis) | 1 | 1 | 1 | 1 | 1 |
| NSTEMI | 1.06 (0.60–1.90), p=0.833 | 3.73 (1.80–7.74), p<0.001 | 35.80 (4.81–266.17), p<0.001 | 17.01 (2.26–128.21), p=0.006 | 25.98 (3.48–194.02), p=0.001 | 1.50 (0.70–3.22), p=0.292 | |
| STEMI | 1.65 (0.91–2.98), p=0.097 | 4.94 (2.36–10.30), p<0.001 | 26.55 (3.54–199.00), p=0.001 | 14.85 (1.95–112.98), p=0.009 | 24.38 (3.25–183.03), p=0.002 | 1.59 (0.74–3.44), p=0.234 | |
| Killip class | 0.80 (0.62–1.02), p=0.076 | 4.01 (2.75–5.85), p<0.001 | 1.78 (1.37–2.30), p<0.001 | 2.14 (1.64–2.80), p<0.001 | 2.08 (1.56–2.77), p<0.001 | 1.87 (1.44–2.45), p<0.001 | 0.80 (0.57–1.11), p=0.798 |
| PCI | N/A | 0.48 (0.23–1.04), p=0.062 | 0.61 (0.39–0.96), p=0.032 | 0.33 (0.19–0.57), p<0.001 | 0.13 (0.06–0.30), p<0.001 | 0.21 (0.11–0.39), p<0.001 | 0.93 (0.54–1.59), p=0.779 |
Notes: Killip class was treated as a continuous variable (values from 1 to 4).
Abbreviations: ACS, acute coronary syndrome; PCI, percutaneous coronary intervention; STEMI, ST-segment elevation myocardial infarction; NSTEMI, non-ST segment elevation myocardial infarction.
Discussion
In this study in older patients with ACS admitted to two teaching hospitals in Vietnam, frailty was present in nearly half of the participants. Advanced age, female gender, history of hypertension, heart failure, stroke and chronic kidney disease were significantly associated with a frailty status. Although frailty did not have an independent impact on whether the participants received PCI or not, it significantly increased the risk of adverse events during hospitalisation and during 30 days after discharge.
The prevalence of frailty in this study is similar to previous studies. Many studies around the world have reported a high prevalence of frailty in older patients with ACS, from around 30% to 49%.10,28,30,43–46 However, when compared with studies using similar frailty definition, the prevalence of frailty in our study was higher. In the study conducted by Graham et al in 183 patients with ACS aged ≥65 in Canada, the prevalence of frailty was 30.5% using the Edmonton Frailty Scale.29 In another study in 236 patients with ACS aged ≥80 in France, the prevalence of frailty defined by the Edmonton Frailty Scale was 20.8%.47 Our study found that the prevalence of frailty in women was higher than in men, which is consistent with the literature on sex difference in frailty.48,49
Overall, only half of the participants received PCI and we found that frailty did not have an independent impact on whether the patients received PCI or not. In a recent published review of frailty in older patients with ACS, older people with frailty were significantly less likely to receive guideline-indicated ACS care, including percutaneous coronary intervention (from 6.7% to 43.7% in the frail compared to 30.4% - 69.5% in the non-frail).20 However, these studies just reported the proportions and no logistic regression analysis was performed to examine the independent impact of frailty on PCI.
In this study, frail participants consistently had higher event rates across all of the study outcomes. The impact of frailty on adverse outcomes in our study is compatible with other studies. Previous studies showed that frailty was associated with longer length of stay, in-hospital complications and short-term mortality.20,50 Notably, acquired pneumonia was the second most common adverse event during hospitalisation in the studied participants (24.4% overall, 13.7% in the non-frail and 35.9% in the frail). Frailty is a complex process that involves multiple system impairments, including the immune system.4 The previous study showed that frailty was associated with loss of physiological reserve in the respiratory.51 There has been evidence of reduced responses to influenza and pneumococcal vaccines in frail people.52–54
In this study, the prevalence of frailty in older patients with ACS was high. As advanced age, female gender, history of hypertension, heart failure, stroke and chronic kidney disease were significantly associated with frailty status, older patients with ACS with these factors may require more attention in terms of frailty assessment. Future studies may target these high-risk patients for intervention to prevent frailty. This study is compatible with a previous study in Vietnam in older hospitalised patients, in which frailty was significantly associated with CVD.27 These findings support the development of a frailty-screening program for older hospitalised patients in Vietnam, particularly for patients with coronary heart disease. Frailty assessment could provide an opportunity to prevent adverse outcomes related to frailty in this population. According to a recent systematic review and meta-analysis of 21 randomised controlled trials in 5262 participants, physical activity intervention, when compared to placebo and standard care, was associated with reductions in frailty.55 Physical intervention for older people with coronary heart disease may not only help prevent frailty but also help reduce cardiovascular risk.
To our best knowledge, this is the first study investigating the prevalence and impact of frailty on PCI and adverse outcomes in older patients with ACS in Vietnam. This study was conducted at two large hospitals in Ho Chi Minh City, Vietnam and contained high-quality detailed clinical information. This study has several limitations. First, socioeconomics information of the participants was not collected. In Vietnam, socioeconomic circumstances may have a significant impact on whether patients with ACS may receive PCI or not. Secondly, the follow-up time was short, and the sample size was calculated for the first aim of the study to investigate the prevalence of frailty. Therefore, the sample size may not be large enough to detect a significant association between frailty and PCI, and other adverse outcomes such as major bleeding, stroke, recurrent ischemia.
Conclusion
In this study, frailty was present in nearly half of older patients with ACS and was associated with increased adverse outcomes. These findings suggest that routine frailty screening should be performed in older patients with ACS in Vietnam.
Ethical Approval
The study protocol was approved by the ethics committees of the University of Medicine and Pharmacy in Ho Chi Minh City, Cho Ray Hospital and Thong Nhat Hospital, Ho Chi Minh City, Vietnam. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation and with the Helsinki Declaration of 1964, as revised in 2013. Informed consent was obtained from all participants for being included in the study. Participants could withdraw anytime without it affecting their current treatment. Their information was kept confidential and used only for research purposes.
Author Contributions
Conceptualization: T.V.N. and T.N.N. Methodology: T.V.N., D.L., K.D.T., K.X.B. and T.N.N. Software: T.V.N., D.L., K.D.T., K.X.B. and T.N.N. Validation: T.V.N. and T.N.N. Formal analysis, T.V.N., D.L. and T.N.N. Investigation: T.V.N., D.L. Resources: T.V.N. Data curation: T.V.N., D.L. Original draft preparation: T.V.N. and T.N.N. Supervision, T.V.N. Project administration, T.V.N., D.L. All authors contributed to data analysis, drafting and revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.
Disclosure
All authors report no conflicts of interests in this work.
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Data Citations
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