Editor's Choice - Frailty and the management of patients with acute cardiovascular disease: A position paper from the Acute Cardiovascular Care Association

Impact of frailty on outcomes in elderly patients with acute myocardial infarction who undergo percutaneous coronary intervention

Aims 

Frailty is characterized by reduced biological reserves and weakened resistance to stressors, and is common in older adults. This study evaluated the prognostic implications of frailty at hospitalization in elderly patients with acute myocardial infarction (AMI) who undergo percutaneous coronary intervention (PCI).

Methods and results 

We prospectively analysed 546 AMI patients aged ≥80 years undergoing PCI from 2009 to 2017. Frailty was classified based on impairment in walking (unassisted, assisted, and wheelchair/non-ambulatory), cognition (normal, mildly impaired, moderately to severely impaired), and basic activities of daily living. Impairment in each domain was scored as 0, 1, or 2, and patients were categorized into the following three groups based on total score: no frailty (0), mild frailty (1–2), moderate-to-severe frailty (≥3). The median follow-up period was 589 days. Of the 546 patients, 27.8% were frail (mild or moderate-to-severe), and this proportion significantly increased to 35.5% at discharge (P < 0.001). Compared to non-frail patients, frail patients were older, less likely to be male, and had a higher rate of advanced Killip class. Major bleeding (no frailty, 9.6%; mild frailty, 16.9%; moderate-to-severe frailty, 31.8%; P < 0.001) and in-hospital mortality (no frailty, 8.4%; mild frailty, 15.4%; moderate-to-severe frailty, 27.3%; P < 0.001) increased as frailty worsened. After adjusting for confounders, frailty was independently associated with higher mid-term all-cause mortality (hazard ratio, 1.81; 95% confidence interval, 1.23–2.65; P = 0.002).

Conclusion 

Frailty in AMI patients aged ≥80 years undergoing PCI was associated with major bleeding, in-hospital death, and mid-term mortality.

[Cross-speciality geriatrics: A health-care challenge for the 21st century]

Increasing numbers of older persons are being treated by specialties other than Geriatric Medicine. Specialists turn to Geriatric Teams when they need to accurately stratify their patients' risk and prognosis, predict the potential impact of their, often, invasive interventions, optimise their clinical status, and contribute to discharge planning. Oncology and Haematology, Cardiology, General Surgery, and other surgical departments are examples where such collaborative working is already established, to a varying extent. The use of the term "Cross-speciality Geriatrics" is suggested when geriatric care is provided in clinical areas traditionally outside the reach of Geriatric Teams. The core principles of Geriatric Medicine (comprehensive geriatric assessment, patient-centred multidisciplinary targeted interventions, and input at point-of-care) are adapted to the specifics of each specialty and applied to frail older patients in order to deliver a holistic assessment/treatment, better patient/carer experience, and improved clinical outcomes. Using Comprehensive Geriatric Assessment methodology and Frailty scoring in such patients provides invaluable prognostic information, helps in decision making, and enables personalised treatment strategies. There is evidence that such an approach improves the efficiency of health care systems and patient outcomes. This article includes a review of these concepts, describes existing models of care, presents the most commonly used clinical tools, and offers examples of excellence in this new era of geriatric care. In an ever ageing population it is likely that teams will be asked to provide Cross-specialty Geriatrics across different Health Care systems. The fundamentals for its implementation are in place, but further evidence is required to guide future development and consolidation, making it one of the most important challenges for Geriatrics in the coming years.

Frailty to predict unplanned hospitalization, stroke, bleeding, and death in atrial fibrillation

Aims

Atrial fibrillation (AF) and frailty are common, and the prevalence is expected to rise further. We aimed to investigate the prevalence of frailty and the ability of a frailty index (FI) to predict unplanned hospitalizations, stroke, bleeding, and death in patients with AF.

Methods and results

Patients with known AF were enrolled in a prospective cohort study in Switzerland. Information on medical history, lifestyle factors, and clinical measurements were obtained. The primary outcome was unplanned hospitalization; secondary outcomes were all-cause mortality, bleeding, and stroke. The FI was measured using a cumulative deficit approach, constructed according to previously published criteria and divided into three groups (non-frail, pre-frail, and frail). The association between frailty and outcomes was assessed using multivariable-adjusted Cox regression models. Of the 2369 included patients, prevalence of pre-frailty and frailty was 60.7% and 10.6%, respectively. Pre-frailty and frailty were associated with a higher risk of unplanned hospitalizations [adjusted hazard ratio (aHR) 1.82, 95% confidence interval (CI) 1.49–2.22; P < 0.001; and aHR 3.59, 95% CI 2.78–4.63, P < 0.001], all-cause mortality (aHR 5.07, 95% CI 2.43–10.59; P < 0.001; and aHR 16.72, 95% CI 7.75–36.05; P < 0.001), and bleeding (aHR 1.53, 95% CI 1.11–2.13; P = 0.01; and aHR 2.46, 95% CI 1.61–3.77; P < 0.001). Frailty, but not pre-frailty, was associated with a higher risk of stroke (aHR 3.29, 95% CI 1.2–8.39; P = 0.01).

Conclusion

Over two-thirds of patients with AF are pre-frail or frail. These patients have a high risk for unplanned hospitalizations and other adverse events. These findings emphasize the need to carefully evaluate these patients. However, whether screening for pre-frailty and frailty and targeted prevention strategies improve outcomes needs to be shown in future studies.

Clinical trial registration

Clinicaltrials.gov identifier number: NCT02105844.

[Frailty syndrome. What physicians and cardiologists need to know?]

The review presents the data and evidences from recent clinical studies on the frailty syndrome - one of the most relevant clinical syndromes, though not studied well yet. The latest data on the prevalence of frailty and various factors contributing to its onset are reported. The presence of frailty is considered as an independent predictor of poor prognosis and high mortality rate. The role of frailty in the development of cardiovascular diseases, their progression and complicated course has been analyzed using the latest studies. In addition, the tendency towards higher incidence of frailty among the population of different countries and the poor prognosis of frail patients requires a series of clinical studies aimed at developing measures for primary and secondary prevention, as well as effective treatment strategies for frailty. The PubMed was used for a literature review.

Correlation between Frailty and Adverse Outcomes Among Older Community-Dwelling Chinese Adults: The China Health and Retirement Longitudinal Study

OBJECTIVES

Frailty is a state of decreased resilience when a person is exposed to an apparently innocuous stressor that is associated with numerous adverse outcomes. The aim is to examine frailty prevalence in China by demographic and investigate the correlation between frailty and outcome.

DESIGN

Cohort study.

SETTING

Community-dwelling adults from 28 Chinese provinces.

METHODS

The participants were 2,273 adults aged ≥65 years from the China Health and Retirement Longitudinal Study. Frailty was scored on the FRAIL scale. We estimated frailty prevalence in the overall sample. The association of frailty with adverse outcomes was evaluated by multinomial logistic regression analysis.

RESULTS

We found that 17.0% of adults aged ≥65 years were frail. Frail individuals had a higher prevalence of comorbidities, falls, and need for medical care than non-frail individuals. The multinomial logistic regression analysis demonstrated that frail status (OR = 2.061, 95% CI: 1.422-2.985) and pre-frail status (OR = 1.540, 95% CI: 1.135-2.089) were associated with falls in the previous two years. Serious falls in the previous two years were related to pre-frailty (OR = 1.815, 95% CI: 1.153-2.859) and frailty (OR = 2.797, 95% CI: 1.655-4.727). In addition, frail individuals were found to be at higher risk for outpatient visits over the previous month (OR = 2.091, 95% CI: 1.502-2.911) and readmission over the previous year (OR = 2.033, 95% CI: 1.480-2.792) in the analysis.

CONCLUSIONS

Pre-frailty and frailty were positively associated with major adverse outcomes, including falls and serious falls; they were also associated with more frequent outpatient visits and readmissions in the past.

Admission Braden Skin Score Independently Predicts Mortality in Cardiac Intensive Care Patients

OBJECTIVE

To determine whether a low Braden skin score (BSS), reflecting increased risk for skin pressure injury, would predict lower survival in cardiac intensive care unit (CICU) patients after adjustment for illness severity and comorbidities.

PATIENTS AND METHODS

This retrospective cohort study included consecutive unique adult patients admitted to a single tertiary care referral hospital CICU from January 1, 2007, through December 31, 2015, who had a BSS documented on CICU admission. The primary outcome was all-cause hospital mortality, using elastic net penalized logistic regression to determine predictors of hospital mortality. The secondary outcome was all-cause post-discharge mortality, using Cox proportional hazards models to determine predictors of post-discharge mortality.

RESULTS

The study included 9552 patients with a mean age of 67.4±15.2 years (3589 [37.6%] were females) and a hospital mortality rate of 8.3%. Admission BSS was inversely associated with hospital mortality (unadjusted odds ratio, 0.70; 95% CI, 0.68-0.72; P<.001; area under the receiver operator curve, 0.80; 95% CI, 0.78-0.82), with increased short-term mortality as a function of decreasing admission BSS. After adjustment for illness severity and comorbidities using multivariable analysis, admission BSS remained inversely associated with hospital mortality (adjusted odds ratio, 0.88; 95% CI, 0.85-0.92; P<.001). Among hospital survivors, admission BSS was inversely associated with post-discharge mortality after adjustment for illness severity and comorbidities (adjusted hazard ratio, 0.89; 95% CI, 0.88-0. 90; P<.001).

CONCLUSION

The admission BSS, a simple inexpensive bedside nursing assessment potentially reflecting frailty and overall illness acuity, was independently associated with hospital and post-discharge mortality when added to established multiparametric illness severity scores among contemporary CICU patients.

Relation of Frailty to Outcomes in Patients With Acute Coronary Syndromes

This study examines a national cohort of patients with a diagnosis of acute coronary syndrome (ACS) for the prevalence of frailty, temporal changes over time, and its association with treatments and clinical outcomes. The National Inpatient Sample database was used to identify US adults with a diagnosis of ACS between 2004 and 2014. Frailty risk was determined using a validated Hospital Frailty Risk Score based on ICD-9 codes using the cutoffs <5, 5 to 15, and >15 for low- (LRS), intermediate- (IRS), and high-risk (HRS) frailty scores, respectively. Logistic regression assessed associations of frailty with clinical outcomes, adjusted for patient co-morbidities and hospital characteristics. From 7,398,572 hospital admissions with ACS between 2004 and 2014, 86.5% of patients had LRS, 13.4% had an IRS, and 0.1% had an HRS. From 2004 to 2014, the prevalence of IRS and HRS patients increased from 8.1% to 18.2% and 0.03% to 0.18%, respectively (p <0.001 for both). The proportion of patients treated with percutaneous coronary intervention was greatest among patients with lowest frailty risk scores (LRS 42.9%, IRS 21.0%, and HRS 14.6%). Comparing HRS to LRS, there was a significant increase in bleeding complications (odds ratio [OR] 2.34, 95% confidence interval [CI] 2.03 to 2.69), vascular complications (OR 2.08, 95% CI 1.79 to 2.41), in-hospital stroke (OR 7.84, 95% CI 6.93 to 8.86), and in-hospital death (OR 2.57, 95% CI 2.18 to 3.04). Risk of frailty is common among patients with ACS, is increasing in prevalence, and is associated with differential management strategies, and outcomes during hospitalization. Increased awareness could facilitate frailty-tailored care to minimize the risk of adverse outcomes.

[Geriatric intensive care : Consensus paper of DGIIN, DIVI, DGAI, DGGG, ÖGGG, ÖGIAIN, DGP, DGEM, DGD, DGNI, DGIM, DGKliPha and DGG]

The proportion of elderly, frail, and multimorbid people has increased dramatically in recent decades resulting from demographic changes and will further increase, which will impact acute medical care. Prospective, randomized studies on geriatric intensive care are still lacking. There are also no international or national recommendations regarding the management of critically ill elderly patients. Based on an expert opinion, this consensus paper provides 16 statements that should be considered when dealing with geriatric critical care patients.

Derivation and Validation of a Novel Cardiac Intensive Care Unit Admission Risk Score for Mortality

Background

There are no risk scores designed specifically for mortality risk prediction in unselected cardiac intensive care unit (CICU) patients. We sought to develop a novel CICU‐specific risk score for prediction of hospital mortality using variables available at the time of CICU admission.

Methods and Results

A database of CICU patients admitted from January 1, 2007 to April 30, 2018 was divided into derivation and validation cohorts. The top 7 predictors of hospital mortality were identified using stepwise backward regression, then used to develop the Mayo CICU Admission Risk Score (M‐CARS), with integer scores ranging from 0 to 10. Discrimination was assessed using area under the receiver‐operator curve analysis. Calibration was assessed using the Hosmer–Lemeshow statistic. The derivation cohort included 10 004 patients and the validation cohort included 2634 patients (mean age 67.6 years, 37.7% females). Hospital mortality was 9.2%. Predictor variables included in the M‐CARS were cardiac arrest, shock, respiratory failure, Braden skin score, blood urea nitrogen, anion gap and red blood cell distribution width at the time of CICU admission. The M‐CARS showed a graded relationship with hospital mortality (odds ratio 1.84 for each 1‐point increase in M‐CARS, 95% CI 1.78–1.89). In the validation cohort, the M‐CARS had an area under the receiver‐operator curve of 0.86 for hospital mortality, with good calibration (P=0.21). The 47.1% of patients with M‐CARS <2 had hospital mortality of 0.8%, and the 5.2% of patients with M‐CARS >6 had hospital mortality of 51.6%.

Conclusions

Using 7 variables available at the time of CICU admission, the M‐CARS can predict hospital mortality in unselected CICU patients with excellent discrimination.

Incremental costs of high intensive care utilisation in patients hospitalised with heart failure

AIMS

Registries have reported large inter-hospital differences in intensive care unit admission rates for patients with acute heart failure, but little is known about the potential economic impact of over-admission of low-risk patients with heart failure to higher cost intensive care units. We described the variability in intensive care unit admission practices, the provision of critical care therapies, and estimated the potential national cost savings if all hospitals adopted low intensive care unit admission practices for patients admitted with heart failure.

METHODS

Using a national population health dataset, we identified 349,693 heart failure admission hospitalisations with a primary diagnosis of heart failure between 2007 and 2016. Hospitals were categorised as low (first quartile), medium (second and third quartile) and high (fourth quartiles) intensive care unit utilisation.

RESULTS

The mean intensive care unit admission rate was 16.4% (inter-hospital range 0.3-51%) including 5.4% in low, 14.5% in medium and 30% in high utilisation hospitals. Intensive care unit therapies in low, medium and high intensive care unit utilisation hospitals were 54.5%, 45.1% and 24.1% (P<0.001), respectively and the inhospital mortality rate was not significantly different. The proportion of hospital costs incurred by intensive care unit care was 7.8% in low, 19.8% in medium and 28.2% in high (P<0.001) admission hospitals. The potential cost savings of altering intensive care unit utilisation practices for patients with heart failure was CAN$234.8m over the study period.

CONCLUSIONS

In a national cohort of patients hospitalised with heart failure, we observed that low intensive care unit utilisation centres had lower hospital costs with no differences in mortality rates. The development of standardised admission criteria for high-cost and high acuity intensive care unit beds could reduce costs to the healthcare system.

Comparison of Mortality Risk Prediction Among Patients ≥70 Versus <70 Years of Age in a Cardiac Intensive Care Unit

Older adults account for an increasing number of cardiac intensive care unit (CICU) admissions. This study sought to determine the predictive value of illness severity scores for mortality in CICU patients ≥70 years of age. Adult patients admitted to the CICU from 2007 to 2015 at one tertiary care hospital were reviewed. Severity of illness scores were calculated on the first CICU day. Area under the receiver-operator characteristic curve (AUROC) values were used to assess discrimination for hospital mortality in patients ≥70 versus <70 years of age. We included 10,004 patients with a mean age of 67.4 ± 15.2 years (37.4% female); 4,771 patients (47.7%) were ≥70 years of age. Patients ≥70 years of age had greater illness severity and more extensive co-morbidities compared with patients <70 years of age. Patients ≥70 years of age had higher hospital mortality (11.6% vs 6.8%, odds ratio 1.80, 95% confidence interval 1.57 to 2.07, p <0.001), with a progressive increase in mortality as a function of decade. Severity of illness scores had lower AUROC values for hospital mortality in patients ≥70 years of age compared with patients <70 years of age (all p <0.05 by DeLong test). The Braden skin score on CICU admission predicted hospital mortality with an AUROC value only slightly lower than these scores. Increasing age decade was associated with decreased postdischarge survival by Kaplan-Meier analysis (p <0.001 by log-rank). In conclusion, contemporary CICU patients ≥70 years of age have greater illness severity, more co-morbidities and higher mortality than patients <70 years of age, yet severity of illness scores are less accurate for predicting mortality in CICU patients ≥70 years of age, emphasizing the need for more effective risk-stratification methods in this population.

Recommendations of the Geriatric Cardiology Section of the Spanish Society of Cardiology for the Assessment of Frailty in Elderly Patients With Heart Disease

Frailty is an age-associated clinical syndrome characterized by a decrease in physiological reserve in situations of stress, constituting a state of vulnerability that involves a higher risk of adverse events. Its prevalence in Spain is high, especially in elderly individuals with comorbidity and chronic diseases. In cardiovascular disease, frailty is associated worse clinical outcomes and higher morbidity and mortality in all scenarios, in both acute and chronic settings, and could consequently influence diagnosis and treatment. However, frailty is often not addressed or included when planning the management of elderly patients with heart disease. In this article, we review the available scientific evidence and highlight the most appropriate scales for the measurement and assessment of frailty, some of which are more useful and have better predictive capacity than others, depending on the clinical context. We also underline the importance of properly identifying and assessing frailty in order to include it in the treatment and care plan that best suits each patient.

Identification of Senior At Risk scale predicts 30-day mortality among older patients with acute heart failure

OBJECTIVE

To assess the value of frailty screening tool (Identification of Senior at Risk [ISAR]) in predicting 30-day mortality risk in older patients attended in emergency department (ED) for acute heart failure (AHF).

DESIGN

Observational multicenter cohort study.

SETTING

OAK-3 register.

SUBJECTS

Patients aged ≥65 years attended with ADHF in 16 Spanish EDs from January to February 2016.

INTERVENTION

No.

VARIABLES

Variable of study was ISAR scale. The outcome was all-cause 30-day mortality.

RESULTS

We included 1059 patients (mean age 85±5,9 years old). One hundred and sixty (15.1%) cases had 0-1 points, 278 (26.3%) 2 points, 260 (24.6%) 3 points, 209 (19.7%) 4 points, and 152 (14.3%) 5-6 points of ISAR scale. Ninety five (9.0%) patients died within 30 days. The percentage of mortality increased in relation to ISAR category (lineal trend P value <.001). The area under curve of ISAR scale was 0.703 (95%CI 0.655-0.751; P<.001). After adjusting for EFFECT risk categories, we observed a progressive increase in odds ratios of ISAR scale groups compared to reference (0-1 points).

CONCLUSIONS

scale is a brief and easy tool that should be considered for frailty screening during initial assessment of older patients attended with AHF for predicting 30-day mortality.

Frailty in Heart Failure: Implications for Management

Frailty is a complex clinical syndrome associated with ageing and chronic illness, resulting from multiple organ impairment; physiological reserves decrease and vulnerability to stressors increase. The role of frailty in cardiovascular disease has become increasingly recognised. Up to 79% of patients with heart failure are frail. Moreover, frailty is associated with a worse quality of life and poor prognosis. This review summarises the available literature on frailty in HF and highlights indications for its management.