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76 The efficacy of frailty tools in detecting frailty and predicting mortality in patients with chronic heart failure
  1. Shirley Sze1,
  2. Pierpaolo Pellicori2,
  3. Jufen Zhang3,
  4. Joan Weston4,
  5. Andrew Clark5
  1. 1Leicester University Hospitals NHS Trust
  2. 2Robertson Centre for Biostatistics
  3. 3Anglia Ruskin University
  4. 4Castle Hill Hospital
  5. 5Department of Academic Cardiology, Hull York Medical School


Introduction Frailty is common in patients with chronic heart failure (CHF) and is associated with adverse outcome. Many frailty tools are available, however, there is standard way of evaluating frailty in patients with CHF. Our study aims to report the prevalence of frailty, agreement and prognostic significance amongst 3 frailty assessment tools and 3 screening tools in CHF patients.

Methods We comprehensively studied frailty using 6 frailty tools. Frailty screening tools include: Clinical frailty scale (CFS); Derby frailty index & Acute frailty network frailty criteria. Frailty assessment tools include: Fried criteria; Edmonton frailty score & Deficit index. Since there is no gold standard in evaluating frailty in CHF patients, for each of the frailty tools, we used the results of the other 5 tools to produce a combined frailty index which we used as a “standard” frailty tool. Subjects were defined as frail if so identified by at least 3 out of 5 tools.

Results 467 consecutive ambulatory CHF patients (67% male, median age 76 (IQR:69–82) years, median NTproBNP 1156 (IQR:469–2463) ng/L) and 87 controls (79% male, median age 73 (IQR:69–77 years) were studied.

Prevalence of frailty was much higher in CHF patients than in controls (30–52% vs 2–15%, respectively). Amongst the frailty screening tools, DFI scored the greatest proportion of patients as frail (48%) while CFS scored the lowest proportion as frail (44%). Amongst the assessment tools, Fried criteria scored the greatest proportion of patients as frail (52%) while EFS scored the lowest proportion as frail (30%). (figure 1)

The prevalence of frailty was higher in patients with HeFNEF than HeFREF. The prevalence of frailty was higher in patients with atrial fibrillation (AF) than in those in sinus rhythm. The prevalence of frailty increased with decreasing BMI and increasing NYHA class, age and NTproBNP.

Of the screening tools, CFS had the strongest agreement with assessment tools (kappa coefficient:0.65–0.72, all p<0.001). CFS had the highest sensitivity (87%) and specificity (89%) amongst screening tools and the lowest misclassification rate (12%) amongst all 6 frailty tools in identifying frailty according to the combined frailty index.

During a median follow-up of 559 days (IQR 512–629 days), 82(18%) patients died. 55% (N=45) of frail patients died of non-cardiovascular causes. Worsening frailty as detected by all 6 frailty tools was associated with worse outcome. A base model for mortality prediction including sex, NYHA class (III/IV vs I/II), BMI, log NTproBNP and haemoglobin had a C-statistics of 0.78. Amongst frailty tools: CFS and Fried criteria increased model performance most compared with base model (c-statistics: 0.80 for both). Patients who were frail according to CFS had a 9 times greater mortality risk than non-frail patients (figure 2).

Conclusion Frailty is common in CHF patients and is associated with worse outcome. CFS is a simple screening tool which identifies a similar group as lengthy assessment tools and has similar prognostic significance. Frailty screening should be incorporated into routine care of patients with CHF.

Conflict of Interest none

  • heart failure
  • frailty
  • mortality

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