Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

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.

Download full-text PDF

Source
http://dx.doi.org/10.1093/ehjqcco/qcaa002DOI Listing

Publication Analysis

Top Keywords

frailty predict
8
predict unplanned
8
unplanned hospitalization
8
stroke bleeding
8
bleeding death
8
atrial fibrillation
8
unplanned hospitalizations
8
pre-frailty frailty
8
frailty
7
unplanned
4

Similar Publications

To evaluate a simplified version of the Clinical Frailty Scale (SCFS) among older adults presenting to the emergency department (ED) with acute dyspnea. In this retrospective single-center cohort study, we included patients from the Acute Dyspnea Study (ADYS) cohort. Severity of illness was assessed using the Medical Emergency Triage and Treatment System (METTS).

View Article and Find Full Text PDF

Background: Frailty is a common issue among hospitalized older adult patients and is associated with numerous adverse health outcomes. Assessing frailty facilitates better decision-making for treatment plans, patient placement, and discharge planning. Approximately a decade ago, the frailty index based on laboratory tests (FI-Lab) metric was introduced.

View Article and Find Full Text PDF

Predicting the future risk and outcomes of severe heart failure and coronary artery disease with machine learning in the UK Biobank Cohort.

PLoS One

September 2025

Department of Medicine, The Red Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada.

Background: In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by traditional statistical methods that have historically yielded only modest prediction accuracy.

Methods: This study uses machine learning algorithms to generate predictions models for the development and progression of severe HF and CAD.

View Article and Find Full Text PDF

Background: The Hospital Frailty Risk Score (HFRS) has been widely used to identify patients at high risk of poor outcomes and to predict poor outcomes for older people. Although poor health outcomes are associated more with frailty than age, HFRS has been validated only for older people. This study aimed to explore for the first time whether age influences the predictive power of Hospital Frailty Risk Score to predict a long length of stay.

View Article and Find Full Text PDF