Skip to main content
NCBI home page
Journal of Geriatric Cardiology : JGC logoLink to Journal of Geriatric Cardiology : JGC
. 2026 Mar 28;23(3):173–183. doi: 10.26599/1671-5411.2026.03.004

Frailty scale with the best prediction of mortality in individuals diagnosed with acute coronary syndrome: systematic review and meta-analysis

Letycia Netto de Paula Cunha 1,*, Samara Vieira de Oliveira 2, Taline Lazzarin 1, Lara Lívia Santos da Silva 2, Marcos Ferreira Minicucci 1, Nara Aline Costa 2
PMCID: PMC13156543  PMID: 42109757

Abstract

Background

Acute Coronary Syndrome (ACS) is a major cause of hospitalizations and deaths worldwide. Conditions such as frailty worsen these outcomes. Frailty assessment improves risk stratification, complements scores and favors personalized treatments. However, there are numerous tools available for assessing frailty, and there is still no consensus on which would be the most recommended in conditions such as ACS. The objective was to evaluate which frailty diagnostic scale has the best predictive value for mortality in individuals with ACS.

Methods

This meta-analysis was conducted using Medline, Embase, and Cochrane, with a search conducted on March 5, 2024. Studies that met the PECOS criteria were included: adult and elderly individuals diagnosed with ACS, frailty assessment determined by a scale, mortality registry and intervention studies or prospective and retrospective cohorts. The risk of bias and quality of evidence were assessed by two researchers using the Joana Briggs Institute Case Series tool and the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) system, respectively. The meta-analysis was conducted using Review Manager software and subgroup analyses using R software.

Results

The results of the meta-analysis indicate that frailty is associated with a significantly higher risk of mortality in patients with ACS (P < 0.001). However, the results of the meta-regression did not indicate a significant difference between the five scales evaluated (P = 0.227). The choice of scale, therefore, can be based on other factors such as practicality and availability of resources, without compromising the prognosis.

Conclusion

Individuals with ACS and frailty have a higher chance of mortality, and all scales evaluated showed good predictive value, with no statistical difference. We suggest that the Clinical Frailty Scale (CFS) is suitable for hospital settings and acute conditions, such as ACS.

It is estimated that around 7 million people are affected by Acute Coronary Syndrome (ACS),[1,2] representing approximately 1 million hospitalizations in the United States of America (USA) and 80,000 in the United Kingdom (UK).[3] In Brazil, around 43% of mortality from cardiovascular causes are attributed to ACS.[4] The treatment of patients with ACS represents a major economic challenge for healthcare systems, due to the complexity and resources required.[5] In addition, the high rate of readmissions due to complications or lack of adherence to treatment worsens the scenario.[6] Lack of adequate treatment also contributes to the development of other conditions, further increasing costs and the impact on the patient.[7]

Conditions such as frailty[8,9] may intensify negative outcomes in ACS, such as reduced recovery capacity and increased mortality risk.[10,11] Frailty is characterized by greater physiological vulnerability resulting from impaired functionality and is closely associated with negative outcomes such as longer hospital stays, readmissions, bleeding, functional decline and mortality.[9,12] Currently, two theoretical models are most commonly used to identify frailty: the physical phenotype model and the cumulative deficit model.[9,13] The first model considers fragility by physical and measurable parameters, being the most used.[9] However, there are difficulties in its application, especially in the hospital environment.[14,15] The second model assesses frailty by adding together the individual's deficiencies, comorbidities and conditions, creating an index, and is more recommended in acute or critical conditions, due to its easy applicability.[13,16]

In this sense, the inclusion of frailty assessment has contributed to improving the risk stratification of individuals with ACS, complementing scores widely recognized in the literature and favoring the adoption of personalized treatment strategies, with a focus on improving prognosis.[10,17-19] However, there is a wide variety of tools for assessing frailty, and there is still no consensus on which method is most appropriate, mainly due to the heterogeneity of studies that address this condition as an object of research.[14,15] Considering the lack of consensus on the standardization of the tool, as well as the importance of early diagnosis of frailty, the objective of this meta-analysis was to evaluate which frailty diagnostic scale has the best predictive value for mortality in individuals with ACS.

Methods

Eligibility Criteria

This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA)[20] and is registered with PROSPERO (registration number CRD42024518509). We sought to include prospective and retrospective intervention and cohort studies that met the following inclusion criteria (PECO): Participants (P): Individuals over 18 years of age, of both sexes, diagnosed with ACS assessed by frailty scale. Exposure (E): frailty diagnosis by scales that consider this condition as a state of increased vulnerability to external stressors.[9] Furthermore, considering that there is still no internationally recognized standard definition for this condition, individuals with frailty can transition between states of severity, we also consider those classified as pre-frail within this group, since they may present compatible clinical manifestations, even if they are not identified as frail by the scales.[8,9] Comparator (C): individuals considered no frail by the scales. Outcomes (O): all-cause mortality.

Exclusion Criteria

Publications prior to December 2012, studies conducted among individuals diagnosed with other cardiovascular conditions were excluded, as well as those that assessed frailty exclusively through objective measures (walking speed, handgrip strength, physical tests such as the short physical performance battery (SPPB), among others). In addition, letters to the editors, reviews, conference abstracts and articles published in languages other than English, in cases where there was no response from the authors after contacting them requesting the English version of the publication, were also excluded.

Search Strategy

The search strategy was developed by an independent researcher (LNPC) and reviewed by a senior researcher experienced in systematic reviews and the topic of interest of the study (MFM). The search was carried out on March 5, 2024 in the Medline, Embase and Cochrane databases. Articles from the reference list of relevant articles or from previously published reviews were not considered.

The main terms included were: “frailty”, “tool”, “scale”, “instrument”, “acute coronary syndrome”, “acute myocardial infarction”, “non-ST-segment elevation myocardial infarction”, “ST-segment elevation myocardial infarction” and “unstable angina”. The terms were combined with the Boolean operators “AND” and “OR”, and specifically, for the Medline database, the terms were used according to the MeSH (Medical Subject Headings) vocabulary. The complete search strategy, considering the databases used, is provided in the Supplementary Material (Table S1).

Study Selection Process

All search results were exported and organized using the Rayyan web tool[21] by two independent researchers (LNPC and SVO). Subsequently, selection process was performed. Conflicts throughout the process of conducting this review were resolved by a third researcher (NAC).

The study selection process was carried out in two stages. After the exclusion of duplicate articles, the first stage consisted of reading the title and abstract, and publications that did not meet the eligibility criteria were excluded. In the second stage, we read the studies included in the first stage in full. Conflicts were resolved by a third researcher (NAC).

Data Extraction

The articles included in the second stage had the following information collected: author, year, country, study design (including sample size, most prevalent sex and age), frailty tool used and diagnostic criteria to define this condition as present, number of individuals considered to have pre-frailty and frailty, total mortality and mortality in individuals with pre-frailty and frailty. The data were extracted by two independent researchers (LNPC and SVO) and recorded in an Excel spreadsheet. Verification and disagreements were resolved by a third researcher (NAC).

We contacted authors by email requesting the full-text manuscript when only abstracts were available, or to obtain the information needed to fill out the data sheet when it was not clear in the publications.

Assessment of Risk of Bias

The risk of bias of the selected studies was assessed using the Joana Briggs Institute Case Series tool.[22] This tool assesses the methodological quality of observational studies that describe a group of patients with specific outcomes or conditions. The checklist contains ten questions, with possible answers: “yes”, “no”, “unclear” and “not applicable”.[22] Two researchers (LNCP and SVO) assessed the risk of bias of the studies, considering it as: low (≥ 70% of “yes” scores), moderate (50% to 69% of “yes” scores) and high (≤ 49% of “yes” scores).[22]

Data Analysis

The results were expressed by scale, number of events and the odds expressed in terms of odds for each scale. The effects were reported as odds ratios (OR) and respective 95% confidence intervals (95% CI). The heterogeneity of the effects between the studies was quantified by the I 2 statistic (I 2 value > 50% indicates high heterogeneity).[23] Subgroup analysis was performed to investigate the influence of the frailty scale on mortality prediction. The adopted model was random effects, considering the heterogeneity between the studies. The subgroups were organized according to the scales used in the included studies. The Review Manager software (version 5.4)[24] and the R software (version 4.3.3) were used for meta-analysis and subgroup analyses, respectively. A P-value < 0.05 was considered statistically significant.

Quality of Evidence

To determine the quality of the evidence presented by the included articles, we used the GRADE system.[25] Each article was graded according to the preferred method into one of four levels: high, moderate, low and very low. GRADE-pro GDT software was employed in this process.

RESULTS

Selection of Articles

A total of 1684 studies were retrieved in the database search. After removing duplicates, 1315 titles and abstracts were evaluated, and 157 studies were selected for full-text reading. Twenty-nine studies were included in this systematic review, and twenty-five studies were also evaluated quantitatively. Figure 1 shows the flow diagram for publication selection.

Figure 1.

Figure 1

Open in a new tab

Publication selection flow diagram.

Characteristics of the Studies

As shown in Table 1, 489,097 participants were included.[26-54] The majority (46.2%) were male and elderly (≥ 65 years).[26-54] Among the studies included,[26-54] most had an observational design and only one was a double-blind randomized clinical trial.[42] The follow-up time varied from the period of hospitalization to up to five years.[26-54] The most widely used frailty scale to assess this condition was the Clinical Frailty Scale (CFS). Thirteen studies[26-38] assessed frailty using only the CFS, three[28,39,40] only the Edmonton Frailty Scale (EFS), four[28,36,41,42] the Fried Criteria (FC), six[28,29,43-46] only the Frail Scale (FS), four[47-50] only the Frailty Instrument for Primary Care of the Survey of Health, Ageing and Retirement in Europe (SHARE-FI). In addition, three studies[28,29,36] assessed frailty by comparing the use of different scales such as FC, EFS, FS and CFS. Scales such as the Claims-Based Frailty Index (CFI),[51] ACTION Frailty Scale,[52] Frailty Index (FI)[53] and, Modified Frailty Index (mFI)[54] are still little studied in SCA, with no more than one study published with each of these tools. Due to the lack of studies for comparison, these works[51-54] were not included in the meta-analysis.

Table 1. Summary of characteristics of included studies.

Author, year Country Study design,
follow-up period		 Sample (total n, sex, age) Frailty (tool, diagnostic criteria for pre- frailty
and frailty)		 Total mortality Mortality in pre- frail and frail individuals
CFS: clinical frailty scale; FS: frail scale; FC: Fried Criteria; USA: United States; SHARE-FI: Survey of Health, Aging and Retirement in Europe Frailty Index; CFI: Claims-Based Frailty Index; EFS: Edmonton Frailty Scale; FI: Frailty Index; mFI: Modified Frailty Index.
Alegre, et al. 2018 Spain Cohort; 12 months
follow-up		 N = 532; 61.7% male;
age 84.3 ± 4 years		 FS ≥ 3 points; n = 145 75 34
Anand, et al., 2020 United Kingdom Cohort; 12 months
follow-up		 N = 198; 58% male;
79 ± 6 years		 CFS ≥ 5 points; n = 40 33 19
Batty, et al., 2018 United Kingdom Cohort; 12 months
follow-up		 N = 280; 60% male;
81 ± 4 years		 FC ≥ 3 criteria; n = 77 18 10
Bernal, et al., 2017 Spain Cohort; during hospital admission N = 254; 57.5% male;
82.1 ± 4.5 years		 FS ≥ 3 points; n = 42 21 9
Calvo, et al., 2019 Spain Cohort; during hospital admission N = 259; 57.9% male;
82.6 years old		 FS ≥ 3 points; n = 51 18 11
Damluji, et al., 2019 USA Cohort; during hospital admission N = 469,390; 53.2% female;
82.3 (75-89) years old		 CFI score 0.2; n = 89,820 48,347 11,856
Ekerstad, et al., 2018 Sweden Cohort; mean follow-up 6.7 years N= 307; 51.1% male;
75-79 years old		 CFS ≥ 5 points; n = 149 213 128
Ekerstad, et al., 2022 Sweden Cohort; 6 months
follow-up		 N = 3,381; 70.6% male;
71 (61-79) years old		 CFS ≥ 5 points; n = 426 255 122
Graham, et al., 2013 Canada Cohort; 12 months
follow-up		 N = 183; 54.1% female;
no age record		 EFS ≥ 7 points; n = 55 13 7
Kang, et al., 2015 China Cohort; 4 months
follow-up		 N = 352; 57.7% male;
74 years old		 CFS ≥ 5 points; n = 152 18 16
Kurobe, et al., 2021 Japan Cohort; mean follow-up 47.9 months N = 266; 77% male;
no age record		 CFS ≥ 5 points; n = 59 15 10
Murali-Krishnan, et al., 2015 United Kingdom Cohort; 12 months
follow-up		 N = 746; 70% male;
62 ± 12 years old		 CFS ≥ 5 points; n = 81 31 11
Nguyen, et al., 2019 Vietnam Cohort; 30-day
follow-up		 N = 324; 60.8% male;
73.5 ± 8.3 years		 EFS ≥ 7 points; n = 156 68 53
Nishihira, et al., 2021 Japan Cohort; 12 months
follow-up		 N = 546; 52.2% female;
84.5 (82-88) years old		 ACTION Frailty Scale ≥ 3 points; n = 152 184 69
Nowak, et al., 2022 Poland Cohort; mean follow-up 637.5 days N = 174; 55.2% male;
74.8 years old		 FC ≥ 3 criteria; EFS ≥ 7 points; FS ≥ 3 points; CFS: ≥ 5 points; FC: 72; EFS: 70; FS: 68; CFS: 68 15 FC: 13; EFS: 12; FS: 13; CFS: 12
Patel, et al., 2018 Australia Cohort; follow-up up to 6 months after hospital discharge N = 3,944; no record of
sex and age		 Adaptation FI score ≥ 0.25;
n = 1,049		 423 202
Pham, et al., 2023 Vietnam Cohort; during hospital admission N = 116; 65.5% male;
72.91 ± 6.22 years old		 FS ≥ 3 points; n = 38 2 1
Ramos, et al., 2022 Brazil Cohort; follow-up up to 3 months after hospital discharge N = 111; 61.3% male;
62.3 ± 12.4 years		 FS ≥ 3 points; CFS: ≥ 5 points; FS: 76; CFS: 23 13 FS: 10; CFS: 7
Ratcovich, et al, 2022 United Kingdom Cohort; 5-year follow-up N = 263; 61.2% male;
81.2 ± 4.1 years		 FC ≥ 3 criteria; CFS: ≥ 5 points; FC: 70; CFS: 33 82 FC: 30; CFS:17
Ratcovich, et al, 2024 United Kingdom Cohort; 12 months
follow-up		 N = 455; 66.3% male;
no age record		 CFS ≥ 5 points; n = 69 67 21
Salinas, et al, 2017 Spain Cohort; 6 months
follow-up		 N = 234; 59.4% male;
no age record		 SHARE-FI ≥ 6 points; n = 94 28 19
Salinas, et al., 2018 Spain Cohort; 12 months
follow-up		 N = 285; 60% male;
82.5 years old		 SHARE-FI ≥ 6 points; n = 109 55 38
Salinas (a), et al., 2016 Spain Cohort; follow-up up to 30 days after hospital discharge N = 190; 60.5% male;
82.7 ± 5.1 years		 SHARE-FI ≥ 6 points; n = 72 10 6
Salinas (b), et al., 2016 Spain Cohort; follow-up during hospital stay N = 202; 60% male;
82 (79-86) years old		 SHARE-FI ≥ 6 points; n = 71 7 6
Tashiro, et al., 2022 Japan Cohort; during hospital admission N = 244; 52% female;
84.4 ± 3.7 years		 CFS ≥ 5 points; n = 72 28 20
White, et al., 2016 52 countries (Europe, Latin and North America, Asia, Africa and Oceania) Double-blind randomized clinical trial; 30 months
follow-up		 N = 4,996; 53.4% male;
no age record		 FC ≥ 3 criteria; n = 237 1,111 86
Yoshioka, et al., 2019 Japan Cohort; mean follow-up 474 days N = 354; 76.6% male;
69.8 ± 12.4 years		 CFS ≥ 5 points; n = 11 39 5
Yoshioka, et al., 2019 Japan Cohort; 2-year follow-up N = 273; 53.8% female;
84.6 ± 3.8 years		 CFS ≥ 5 points; n = 34 65 14
Zong, et al., 2023 China Cohort; mean follow-up 31.98±10.92 days N = 238; % 52.5 female;
81.17 ± 4.3 years		 mFI ≥ 0.27 points; n = 143 74 60
Open in a new tab

Approximately 93,884 (19.2%) individuals were considered pre-frail or frail by the studies, and 12,947 (13.8%) died in this group.[26-54] The studies were conducted in different regions of the world. Seven[44-50] were carried out in Spain, five[30,34,36,28,41] in the UK, two[35,54] in China, five[26,31,32,37,52] in Japan, two[39,43] in Vietnam, two[27,33] in Sweden, one in the USA,[51] Canada,[40] Poland,[28] Australia,[53] Brazil[29] and one using a sample of fifty-two countries[42] (Table 1).

Association of Fragility with Mortality by Scales

In general, the presence of frailty was a risk factor for mortality regardless of the scale used to assess adults or elderly individuals with ACS (P < 0.001). A 4.6-fold higher risk of mortality was observed in individuals living with frailty, when compared to non-frail individuals (OR: 4.60; 95% CI: 3.62-5.86). However, it was possible to verify high heterogeneity (I 2 = 63%) among the studies (Figure 2).

Figure 2.

Figure 2

Open in a new tab

Forest plot association of frailty with mortality by scales.

Given the high heterogeneity identified in the meta-analysis, a meta-regression was performed to explore possible sources of this variability. Specifically, it was assessed whether the type of scale used in each study could explain part of the observed heterogeneity. The results showed that the CFS presented the highest odds ratio (OR) among all the scales evaluated. However, this difference did not reach statistical significance in predicting mortality (OR = 4.50; 95% CI: 3.71–5.46; P = 0.228) (Figure 3). In addition, the subgroup analysis by type of scale did not result in a significant reduction in heterogeneity, which remained high (I 2 = 63.4%) (Figure 3).

Figure 3.

Figure 3

Open in a new tab

Subgroup analysis (scales) considering the prediction for mortality.

CFS: Clinical Frailty Scale; EFS: Edmonton Frailty Scale; FC: Fried Criteria; FS: Frail Scale; SHARE-FI: Survey of Health, Aging and Retirement in Europe Frailty Index.

Risk of Bias from Study

Almost all studies presented low risk of bias considering the domains of the tool used.[22] Only two studies[26,28] were considered to have a moderate risk of bias (Figure 4). The risk of bias was influenced by the lack of information regarding sociodemographic characteristics and the criteria used for the diagnosis of ACS. Supplementary Table 2 presents in detail the assessment of the risk of bias of the included studies.

Figure 4.

Figure 4

Open in a new tab

Funnel plot for evaluating publication bias in studies included in the meta-analysis.

Quality of Evidence from Study

Of the 25 studies included in the meta-analysis, the assessment of the quality of evidence was conducted using the GRADE system for the 24 cohort studies, as they represent the majority of the evidence base and have similar methodological characteristics. The only randomized clinical trial was not included in this assessment, as its inclusion together with observational studies could compromise the homogeneous application of the GRADE criteria, given the difference in design and initial level of quality of evidence.

The result shows that despite the magnitude of the observed effect, the certainty of the evidence was classified as very low (Table S3). The high heterogeneity between the studies, and the absence of direct comparisons between the different frailty scales used by the included studies, may have influenced this result.

DISCUSSION

Summary of Evidence

Our study aimed to evaluate which scale for diagnosing frailty has the best predictive value for estimating mortality in individuals diagnosed with ACS. Our results showed that all the scales evaluated had good predictive value and that there was no statistically significant difference between them. Therefore, the use of any of these scales is reliable in predicting mortality in ACS. We suggest that given the severity of the patients and the reliability of the methods, the use of easy-to-use bedside tools such as the CFS can be prioritized. Furthermore, we highlight that the diversity of subjective and objective criteria used in each scale may have influenced the prevalence rates of frailty (3.1%-68.5%). This condition associated with the number of outcomes may have influenced the lack of association between the type of scale adopted and the prediction of mortality.

The physical frailty phenotype has been the most widely used tool in the diagnosis of frailty in various health conditions.[14] However, the characteristics of individuals and the assessment environment may influence the application of this method. Recent studies recommend the use of CFS to assess frailty in acute settings, such as in critically ill or hospitalized patients.[16,55,56] This recommendation is based on the ease of application, low cost and speed in obtaining results, allowing early identification of frailty in these patients, contributing to the optimization of treatment and to making more assertive clinical decisions.[56]

In this meta-analysis, the most widely used scale in the studies was the CFS.[26-38] This tool consists of a clinical judgment scale composed of nine items that analyze the impact of health conditions, cognition and mobility on the ability to perform activities of daily living and instrumental activities of daily living. Higher scores indicate worse functional status and the presence of frailty.[16]

It is known that the relationship between frailty and mortality is already well established in the literature in different cardiovascular conditions.[57-59] In acute conditions such as ACS, frailty has also been shown to be an independent risk factor for mortality,[10] so the European Society of Cardiology (ESC) emphasizes the importance of evaluating this condition in these patients.[60] In this meta-analysis, frailty increased the chance of mortality in terms of odds in individuals living with frailty by 4.6 times, when compared to non-frail individuals. In another meta-analysis also conducted with individuals with ACS followed during the period of hospitalization and for more than 5 years, they identified that the relative risk of mortality in individuals living with frailty was 2.3 times higher when compared to non-frail individuals.[61] In contrast, a recent meta-analysis that included elderly patients with acute myocardial infarction did not identify a statistically significant association in relation to the presence of frailty and mortality (P = 0.285).[62]

The difference in our meta-analysis was the proposal to identify which frailty scale has the best predictive value for mortality. With such a determination, there could be advances in the standardization of the type of tool used, with the aim of favoring the results obtained regarding the global prevalence and also stimulating the development of care therapies that can be widely adopted. The confusion in the concept of frailty and other conditions such as sarcopenia, reduced functional capacity and geriatric conditions contribute to the improper use of diagnostic tools.[7,8,14] Because these conditions are similar in terms of their pathophysiological mechanisms and complications, they are often mistakenly confused and do not consider the differences in diagnostic and definition criteria.[14] Despite the synergy and possibility of overlap between such nutritional conditions, it is worth highlighting that frailty is considered a more serious condition, with a lower probability of recovery.[63] In this sense, the use of the appropriate tool for diagnosing frailty becomes essential to minimize the chance of false negative diagnoses.[14]

In our study, during the eligibility assessment process, twelve articles[64-75] were excluded because they did not use recognized tools for the diagnosis of frailty, with the tools most used by the included studies being CFS,[16] EFS,[76] FC,[9] FS[77] and, SHARE-FI.[78] Of these tools, FS derives from the physical frailty phenotype model, however, its assessment is entirely self-reported and subjective.[77] The EFS, FC and SHARE-FI scales are considered mixed models with objective, subjective and self-report assessments.[9,76,78] The CFS is the only tool derived from the cumulative deficit model[16] which was included in this meta-analysis. Thus, even though in our study it was not possible to identify any preponderance between the tools in predicting mortality, our evidence reinforces the importance of evaluating this condition in ACS and supports the ESC recommendations.[60]

Limitations

Among the limitations of this study, we highlight the small number of randomized clinical studies and the wide use of different tools for the diagnosis of frailty, which may have over- or underestimated this condition. In addition, the great heterogeneity between the studies may have influenced the results obtained. However, the tools evaluated contemplate the most widely used concepts of frailty currently, demonstrating that all tools are capable of predicting negative outcomes in this population.

In conclusion, patients with ACS living with frailty had a mortality rate almost five times higher than patients with the same condition but non-frail. Among the five frailty assessment scales included, all showed good predictive value for mortality, but without statistically significant difference between them.

DISCLOSURE

SUPPLEMENTARY DATA

Supplementary data to this article can be found online.

jgc-23-3-173-S1.zip (959.7KB, zip)

Authors’ contributions

L.N.P. Cunha: investigation, methodology, reviewer, writing; S.V. Oliveira: investigation, methodology, reviewer, writing; T. Lazzarin: investigation, methodology, reviewer, writing; L.L.S. Silva: methodology, expert critical review for intellectual improvement; M.F. Minicucci: expert critical review for intellectual improvement; N.A. Costa: expert critical review for intellectual improvement, project administration, and supervision.

Funding Statement

This research was funded by the scholarship for Goiás State Research Support Foundation (FAPEG), grant numbers 2023410267000612 (01/2023).

Declaration of Interests

None.

References

  • 1.Nicolau JC, Feitosa Filho GS, Petriz JL, et al Brazilian Society of Cardiology guidelines on unstable angina and acute myocardial infarction without ST-segment elevation–2021. Arq Bras Cardiol. 2021;117:181–264. doi: 10.36660/abc.20210180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Bhatt DL, Lopes RD, Harrington RA Diagnosis and treatment of acute coronary syndromes: a review. JAMA. 2022;327:662–675. doi: 10.1001/jama.2022.0358. [DOI] [PubMed] [Google Scholar]
  • 3.Kotecha T, Rakhit RD. Acute coronary syndromes. Clin Med (Lond) 2016; 16(Suppl 6): S43-S48.
  • 4.Nascimento K, Ramadan HR, Baccaro BM, et al Acute coronary syndrome in Brazil: registration of predisposing factors and population profile in a national public reference cardiological institute. Arq Bras Cardiol. 2025;122:e20240165. doi: 10.36660/abc.20240165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Expert Panel on Cardiac Imaging; Batlle JC, Kirsch J, Bolen MA, et al ACR Appropriateness Criteria® Chest Pain-Possible Acute Coronary Syndrome. J Am Coll Radiol. 2020;17:S55–S69. doi: 10.1016/j.jacr.2020.01.027. [DOI] [PubMed] [Google Scholar]
  • 6.Rashidi A, Whitehead L, Glass C Factors affecting hospital readmission rates following an acute coronary syndrome: A systematic review. J Clin Nurs. 2022;31:2377–2397. doi: 10.1111/jocn.16122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Damluji AA, Forman DE, Wang TY, et al Management of acute coronary syndrome in the older adult population: a scientific statement from the American Heart Association. Circulation. 2023;147:e32–e62. doi: 10.1161/CIR.0000000000001112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Dent E, Kowal P, Hoogendijk EO Frailty measurement in research and clinical practice: A review. Eur J Intern Med. 2016;31:3–10. doi: 10.1016/j.ejim.2016.03.007. [DOI] [PubMed] [Google Scholar]
  • 9.Fried LP, Tangen CM, Walston J, et al Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146–M157. doi: 10.1093/gerona/56.3.M146. [DOI] [PubMed] [Google Scholar]
  • 10.Bebb O, Smith FGD, Clegg A, Hall M Frailty and acute coronary syndrome: a structured literature review. Eur Heart J Acute Cardiovasc Care. 2018;7:166–175. doi: 10.1177/2048872617700873. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Chad T, Koulouroudias M, Layton GR, et al Frailty in acute coronary syndromes. A systematic review and narrative synthesis of frailty assessment tools and interventions from randomized controlled trials. Int J Cardiol. 2024;15:131764. doi: 10.1177/2047487318803679. [DOI] [PubMed] [Google Scholar]
  • 12.Buigues C, Juarros-Folgado P, Fernandez-Garrido J, et al Frailty syndrome and pre-operative risk evaluation: a systematic review. Arch Gerontol Geriatr. 2015;61:309–321. doi: 10.1016/j.archger.2015.08.002. [DOI] [PubMed] [Google Scholar]
  • 13.Mitnitski AB, Mogilner AJ, Rockwood K Accumulation of deficits as a proxy measure of aging. Scientific World J. 2001;1:323–336. doi: 10.1100/tsw.2001.58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Dent E, Martin FC, Bergman H, et al Management of frailty: opportunities, challenges, and future directions. Lancet. 2019;394:1376–1386. doi: 10.1016/S0140-6736(19)31785-4. [DOI] [PubMed] [Google Scholar]
  • 15.Gordon EH, Hubbard RE Frailty: understanding the difference between age and ageing. Age Ageing. 2022;51:afac185. doi: 10.1093/ageing/afac185. [DOI] [PubMed] [Google Scholar]
  • 16.Rockwood K, Song X, MacKnight C, et al A global clinical measure of fitness and frailty in elderly people. Cmaj. 2005;173:489–495. doi: 10.1503/cmaj.050051. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Tonet E, Pavasini R, Biscaglia S, Campo G. Frailty in patients admitted to hospital for acute coronary syndrome: when, how and why? J Geriatr Cardiol 2019; 16(2): 129−137.
  • 18.Antonsen L, Jensen LO, Terkelsen CJ, et al Outcomes after primary percutaneous coronary intervention in octogenarians and nonagenarians with ST-segment elevation myocardial infarction: from the Western Denmark heart registry. Catheter Cardiovasc Interv. 2013;81:912–919. doi: 10.1016/j.jacc.2010.08.102. [DOI] [PubMed] [Google Scholar]
  • 19.Kvakkestad KM, Abdelnoor M, Claussen PA, et al Long-term survival in octogenarians and older patients with ST-elevation myocardial infarction in the era of primary angioplasty: a prospective cohort study. Eur Heart J Acute Cardiovasc Care. 2016;5:243–252. doi: 10.1177/2048872615574706. [DOI] [PubMed] [Google Scholar]
  • 20.Page MJ, McKenzie JE, Bossuyt PM, et al The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi: 10.1136/bmj.n71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A Rayyanda web and mobile app for systematic reviews. Syst Rev. 2016;5:210. doi: 10.1186/s13643-016-0384-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Munn Z, Barker T, Moola S, et al Methodological quality of case series studies. JBI Evid Synth. 2020;18:2127–2133. doi: 10.11124/JBISRIR-D-19-00099. [DOI] [PubMed] [Google Scholar]
  • 23.Higgins JP, Thompson SG, Deeks JJ, Altman DG Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–560. doi: 10.1136/bmj.327.7414.557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Deeks JJ, Higgins JP. Statistical algorithms in review manager 5. Statistical Methods Group of The Cochrane Collaboration 2010; 1−11.
  • 25.Guyatt G, Oxman AD, Akl EA, et al GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol. 2011;64:383–394. doi: 10.1016/j.jclinepi.2010.04.026. [DOI] [PubMed] [Google Scholar]
  • 26.Tashiro H, Tanaka A, Takagi K, et al Incidence and predictors of frailty progression among octogenarians with ST-elevation myocardial infarction undergoing primary percutaneous coronary intervention. Arch Gerontol Geriatr. 2022;102:104737. doi: 10.1016/j.archger.2022.104737. [DOI] [PubMed] [Google Scholar]
  • 27.Ekerstad N, Javadzadeh D, Alexander KP, et al Clinical frailty scale classes are independently associated with 6-month mortality for patients after acute myocardial infarction. Eur Heart J Acute Cardiovasc Care. 2022;11:89–98. doi: 10.1093/ehjacc/zuab114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Nowak W, Kowalik I, Kuzin M, et al Comparison of the prognostic value of frailty assessment tools in patients aged ≥ 65 years hospitalized in a cardiac care unit with acute coronary syndrome. J Geriatr Cardiol. 2022;19:343–353. doi: 10.11909/j.issn.1671-5411.2022.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Ramos JTG, Ferrari FS, Andrade MF, et al Association between frailty and C-terminal Agrin fragment with 3-month mortality following ST-elevation myocardial infarction. Exp Gerontol. 2022;158:111658. doi: 10.1016/j.exger.2021.111658. [DOI] [PubMed] [Google Scholar]
  • 30.Anand A, Cudmore S, Robertson S, et al. Frailty assessment and risk prediction by GRACE score in older patients with acute myocardial infarction. BMC Geriatr 202; 20(1): 102.
  • 31.Yoshioka N, Takagi K, Morita Y, et al Impact of the clinical frailty scale on mid-term mortality in patients with ST-elevated myocardial infarction. Int J Cardiol Heart Vasc. 2019;22:192–198. doi: 10.1016/j.ijcha.2019.02.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Yoshioka N, Takagi K, Morishima I, et al Influence of preadmission frailty on short- and mid-term prognoses in octogenarians with ST-elevation myocardial infarction. Circ J. 2020;84:109–118. doi: 10.1253/circj.cj-19-0467. [DOI] [PubMed] [Google Scholar]
  • 33.Ekerstad N, Pettersson S, Alexander K, et al Frailty as an instrument for evaluation of elderly patients with non-ST-segment elevation myocardial infarction: A follow-up after more than 5 years. Eur J Prev Cardiol. 2018;25:1813–1821. doi: 10.1177/2047487318799438. [DOI] [PubMed] [Google Scholar]
  • 34.Murali-Krishnan R, Iqbal J, Rowe R, et al Impact of frailty on outcomes after percutaneous coronary intervention: a prospective cohort study. Open Heart. 2015;2:e000294. doi: 10.1136/openhrt-2015-000294. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Kang L, Zhang SY, Zhu WL, et al. Is frailty associated with short-term outcomes for elderly patients with acute coronary syndrome? J Geriatr Cardiol 2015; 12: 662−667.
  • 36.Ratcovich H, Beska B, Mills G, et al Five-year clinical outcomes in patients with frailty aged ≥ 75 years with non-ST elevation acute coronary syndrome undergoing invasive management. Eur Heart J Open. 2022;2:oeac035. doi: 10.1093/ehjopen/oeac035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Kurobe M, Uchida Y, Ishii H, et al Impact of the clinical frailty scale on clinical outcomes and bleeding events in patients with ST-segment elevation myocardial infarction. Heart Vessels. 2021;36:799–808. doi: 10.1007/s00380-020-01764-0. [DOI] [PubMed] [Google Scholar]
  • 38.Ratcovich H, Joshi FR, Palm P, et al Prevalence and impact of frailty in patients ≥ 70 years old with acute coronary syndrome referred for coronary angiography. Cardiology. 2024;149:1–13. doi: 10.1159/000535116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Nguyen TV, Le D, Tran KD, et al Frailty in older patients with acute coronary syndrome in Vietnam. Clin Interv Aging. 2019;14:2213–2222. doi: 10.2147/CIA.S234597. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Graham MM, Galbraith PD, O'Neill D, et al Frailty and outcome in elderly patients with acute coronary syndrome. Can J Cardiol. 2013;29:1610–1615. doi: 10.1093/eurheartj/ehy566.p5431. [DOI] [PubMed] [Google Scholar]
  • 41.Batty J, Qiu W, Gu S, et al One-year clinical outcomes in older patients with non-ST elevation acute coronary syndrome undergoing coronary angiography: an analysis of the ICON1 study. Int J Cardiol. 2018;274:45–51. doi: 10.1016/j.ijcard.2018.09.086. [DOI] [PubMed] [Google Scholar]
  • 42.White HD, Westerhout CM, Alexander KP, et al Frailty is associated with worse outcomes in non-ST-segment elevation acute coronary syndromes: Insights from the TaRgeted platelet Inhibition to cLarify the Optimal strateGy to medicallY manage Acute Coronary Syndromes (TRILOGY ACS) trial. Eur Heart J Acute Cardiovasc Care. 2016;5:231–242. doi: 10.1177/2048872615581502. [DOI] [PubMed] [Google Scholar]
  • 43.Pham HM, Nguyen AP, Nguyen HTT, et al The Frail Scale - A Risk Stratification in Older Patients with Acute Coronary Syndrome. J Multidiscip Healthc. 2023;16:1521–1529. doi: 10.2147/JMDH.S409535. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Calvo E, Teruel L, Rosenfeld L, et al Frailty in elderly patients undergoing primary percutaneous coronary intervention. Eur J Cardiovasc Nurs. 2019;18:132–139. doi: 10.1177/1474515118796836. [DOI] [PubMed] [Google Scholar]
  • 45.Alegre O, Formiga F, López-Palop R, et al An easy assessment of frailty at baseline independently predicts prognosis in very elderly patients with acute coronary syndromes. J Am Med Dir Assoc. 2018;19:296–303. doi: 10.1016/j.jamda.2017.10.007. [DOI] [PubMed] [Google Scholar]
  • 46.Bernal E, Bayés-Genís A, Ariza-Solé A, et al Interatrial block, frailty and prognosis in elderly patients with myocardial infarction. J Electrocardiol. 2018;51:1–7. doi: 10.1016/j.jelectrocard.2017.08.026. [DOI] [PubMed] [Google Scholar]
  • 47.Alonso Salinas GL, Sanmartin M, Pascual Izco M, et al The role of frailty in acute coronary syndromes in the elderly. Gerontology. 2018;64:422–429. doi: 10.1159/000488390. [DOI] [PubMed] [Google Scholar]
  • 48.Alonso Salinas GL, Sanmartin M, Pascual Izco M, et al Frailty is an independent prognostic marker in elderly patients with myocardial infarction. Clin Cardiol. 2017;40:925–931. doi: 10.1002/clc.22749. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Alonso Salinas GL, Sanmartín Fernández M, Pascual Izco M, et al Frailty predicts major bleeding within 30 days in elderly patients with Acute Coronary Syndrome. Int J Cardiol. 2016;222:590–593. doi: 10.1016/j.ijcard.2016.07.268. [DOI] [PubMed] [Google Scholar]
  • 50.Alonso Salinas GL, Sanmartín Fernández M, Pascual Izco M, et al Frailty is a short-term prognostic marker in acute coronary syndrome of elderly patients. Eur Heart J Acute Cardiovasc Care. 2016;5:434–440. doi: 10.1177/2048872616644909. [DOI] [PubMed] [Google Scholar]
  • 51.Damluji AA, Huang J, Bandeen-Roche K, et al Frailty among older adults with acute myocardial infarction and outcomes from percutaneous coronary interventions. J Am Heart Assoc. 2019;8:e013686. doi: 10.1161/JAHA.119.013686. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Nishihira K, Yoshioka G, Kuriyama N, et al Impact of frailty on outcomes in elderly patients with acute myocardial infarction who undergo percutaneous coronary intervention. Eur Heart J Qual Care Clin Outcomes. 2021;7:189–197. doi: 10.1093/ehjqcco/qcaa018. [DOI] [PubMed] [Google Scholar]
  • 53.Patel A, Goodman SG, Yan AT, et al frailty and outcomes after myocardial infarction: insights from the CONCORDANCE registry. J Am Heart Assoc. 2018;7:e009859. doi: 10.1161/JAHA.118.009859. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Zong M, Guan X, Huang W, et al Effect of frailty on the long-term prognosis of elderly patients with acute myocardial infarction. Clin Interv Aging. 2023;18:2021–2029. doi: 10.2147/CIA.S433221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Costa NA, Minicucci MF, Pereira AG, et al Current perspectives on defining and mitigating frailty in relation to critical illness. Clin Nutr. 2021;40:5430–5437. doi: 10.1016/j.clnu.2021.09.017. [DOI] [PubMed] [Google Scholar]
  • 56.Pulok MH, Theou O, Van AM, Rockwood K The role of illness acuity on the association between frailty and mortality in emergency department patients referred to internal medicine. Age Ageing. 2020;49:1071–79. doi: 10.1093/ageing/afaa089. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Afilalo J, Lauck S, Kim DH, et al Frailty in older adults undergoing aortic valve replacement the FRAILTY-AVR study. JACC. 2017;70:689–700. doi: 10.1016/j.jacc.2017.06.024. [DOI] [PubMed] [Google Scholar]
  • 58.Denfeld QE, Winters-Stone K, Mudd JO, et al The prevalence of frailty in heart failure: a systematic review and meta-analysis. Int J Cardiol. 2017;1:283–289. doi: 10.1002/ehf2.15300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Tse G, Gong M, Nunez J, et al Frailty and mortality outcomes after percutaneous coronary intervention: a systematic review and meta-analysis. J Am Med Dir Assoc. 2017;18:e1091–e1097. doi: 10.1016/j.jamda.2017.09.002. [DOI] [PubMed] [Google Scholar]
  • 60.Roffi M, Patrono C, Collet JP, et al 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Cardiol Pol. 2015;73:1207–1294. doi: 10.1093/eurheartj/ehab285. [DOI] [PubMed] [Google Scholar]
  • 61.Putthapiban P, Vutthikraivit W, Rattanawong P, et al Association of frailty with all-cause mortality and bleeding among elderly patients with acute myocardial infarction: a systematic review and meta-analysis. J Geriatr Cardiol. 2020;17:270–278. doi: 10.11909/j.issn.1671-5411.2020.05.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Yu Q, Guo D, Peng J, et al Prevalence and adverse outcomes of frailty in older patients with acute myocardial infarction after percutaneous coronary interventions: A systematic review and meta-analysis. Clin Cardiol. 2023;46:5–12. doi: 10.1002/clc.23929. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Hoogendijk EO, Afilalo J, Ensrud KE, et al Frailty: implications for clinical practice and public health. Lancet. 2019;394:1365–1375. doi: 10.1016/S0140-6736(19)31786-6. [DOI] [PubMed] [Google Scholar]
  • 64.Sokhal BS, Matetić A, Rashid M, et al Association of frailty status on the causes and outcomes of patients admitted with cardiovascular disease. Am J Cardiol. 2023;192:7–15. doi: 10.1016/j.amjcard.2022.12.029. [DOI] [PubMed] [Google Scholar]
  • 65.Heaton J, Singh S, Nanavaty D, et al Impact of frailty on outcomes in acute ST-elevated myocardial infarctions undergoing percutaneous coronary intervention. Catheter Cardiovasc Interv. 2023;101:773–786. doi: 10.1002/ccd.30595. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Christensen DM, Strange JE, Falkentoft AC, et al Frailty, treatments, and outcomes in older patients with myocardial infarction: a nationwide registry-based study. J Am Heart Assoc. 2023;12:e030561. doi: 10.1161/JAHA.123.030561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Bai W, Hao B, Meng W, et al Association between frailty and short- and long-term mortality in patients with critical acute myocardial infarction: Results from MIMIC-IV. Front Cardiovasc Med. 2022;9:1056037. doi: 10.3389/fcvm.2022.1056037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Rubens M, Ramamoorthy V, Saxena A, et al Management and outcomes of ST-segment elevation myocardial infarction in hospitalized frail patients in the United States. Am J Cardiol. 2022;175:1–7. doi: 10.1016/j.amjcard.2022.04.006. [DOI] [PubMed] [Google Scholar]
  • 69.Fishman B, Sharon A, Itelman E, et al Invasive Management in older adults (≥ 80 years) with non-ST elevation myocardial infarction. Mayo Clin Proc. 2022;97:1247–1256. doi: 10.1016/j.mayocp.2022.03.021. [DOI] [PubMed] [Google Scholar]
  • 70.Borovac JA, Mohamed MO, Kontopantelis E, et al Frailty among patients with acute ST-elevation myocardial infarction in the United States: the impact of the primary percutaneous coronary intervention on in-hospital outcomes. J Invasive Cardiol. 2022;34:E55–E64. doi: 10.25270/jic/21.00069. [DOI] [PubMed] [Google Scholar]
  • 71.Lopez D, Preen DB, Etherton-Beer C, Sanfilippo FM Frailty, and not medicines with anticholinergic or sedative effects, predicts adverse outcomes in octogenarians admitted for myocardial infarction: Population-level study. Australas J Ageing. 2021;40:e155–e162. doi: 10.1111/ajag.12891. [DOI] [PubMed] [Google Scholar]
  • 72.Su W, Wang M, Zhu J, et al Underweight predicts greater risk of cardiac mortality post acute myocardial infarction. Int Heart J. 2020;61:658–664. doi: 10.1536/ihj.19-635. [DOI] [PubMed] [Google Scholar]
  • 73.Kundi H, Wadhera RK, Strom JB, et al Association of frailty with 30-day outcomes for acute myocardial infarction, heart failure, and pneumonia among elderly adults. JAMA Cardiol. 2019;4:1084–1091. doi: 10.1001/jamacardio.2019.3511. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Kwok CS, Lundberg G, Al-Faleh H, et al Relation of frailty to outcomes in patients with acute coronary syndromes. Am J Cardiol. 2019;124:1002–1011. doi: 10.1016/j.amjcard.2019.07.003. [DOI] [PubMed] [Google Scholar]
  • 75.Kundi H, Coskun N, Yesiltepe M Association of entirely claims-based frailty indices with long-term outcomes in patients with acute myocardial infarction, heart failure, or pneumonia: a nationwide cohort study in Turkey. Lancet Reg Health Eur. 2021;10:100183. doi: 10.1016/j.lanepe.2021.100183. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Rolfson DB, Majumdar SR, Tsuyuki RT, et al Validity and reliability of the Edmonton Frail Scale. Age Ageing. 2006;35:526–529. doi: 10.1093/ageing/afl041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Morley JE, Malmstrom TK, Miller DK A simple frailty questionnaire (FRAIL) predicts outcomes in middle aged African Americans. J Nutr Health Aging. 2012;16:601–608. doi: 10.1007/s12603-012-0084-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Romero-Ortuno R, Walsh CD, Lawlor BA, Kenny RA A frailty instrument for primary care: findings from the Survey of Health, Ageing and Retirement in Europe (SHARE) BMC Geriatr. 2010;10:57. doi: 10.1186/1471-2318-10-57. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary data to this article can be found online.

jgc-23-3-173-S1.zip (959.7KB, zip)

Articles from Journal of Geriatric Cardiology : JGC are provided here courtesy of Institute of Geriatric Cardiology, Chinese PLA General Hospital

RESOURCES