153 Agreement among frailty assessment tools and their association with quality of life in older patients with heart failure: a comparison of four instruments
Bibliographic record
Abstract
<h3>Introduction</h3> Frailty is highly prevalent in older patients with heart failure (HF) and is associated with poor clinical outcomes. Many tools are available for assessing frailty, however, very few studies have simultaneously evaluated different frailty tools and their relation to quality of life (QoL) within the same cohort of patients. We studied the agreement between 4 commonly used frailty tools and their association with QoL and functional dependence amongst older patients with HF. <h3>Methods</h3> We recruited older adults (aged ≥ 65) with HF from a hospital-based HF clinic and assessed their frailty using 4 tools: Clinical Frailty Scale (CFS), Fried Frailty Phenotype (FP), Short Physical Performance Battery (SPPB) and Edmonton Frail Scale (EFS). Patients were classified as frail and non-frail according to cut-offs specified by each instrument. Functional dependence and QoL were assessed using Barthel Index (BI) and Kansas City Cardiomyopathy Questionnaire (KCCQ-12), respectively. Kappa statistics was used to assess the level of agreement between the frailty tools. <h3>Results</h3> We studied 150 participants [median age: 80 (range 65–94) years, 65% male]. Thirty-eight percent had HF with reduced ejection fraction; One fifth had NYHA III/IV symptoms; median NT-proBNP 2137 ng/L (IQR = 1120–5028). The SPPB model identified the highest proportion of patients as frail (69%), followed by CFS (67%), FP (57%) and EFS (47%) (table 1). Forty-five patients (30%) were classified as frail by all 4 tools (figure 1). There was moderate agreement between SPPB vs CFS (&kgreen; = 0.49) and SPPB vs FP (&kgreen; = 0.43) and fair agreement between FP vs CFS (&kgreen; = 0.29), FP vs EFS (&kgreen; = 0.25), CFS vs EFS (&kgreen; = 0.27) and SPPB vs EFS (&kgreen; = 0.220), all p < 0.001 (table 2). Of the frailty tools, CFS had the strongest correlation with BI (r = -0.703, p < 0.001), whilst EFS had the strongest correlation with KCCQ-12 (r = -0.627, p < 0.001). Worse frailty status determined by any frailty tool was significantly associated with poorer QoL and functional dependence after adjusting for age and NYHA class. <h3>Conclusion</h3> SPPB had moderate agreement with FP and CFS while EFS had low agreement with other frailty tools. Frailty was significantly associated with functional dependence and QoL in older patients with HF. Our observations have implications for the application of clinical frailty tools in clinical practice and in research. <h3>Conflict of Interest</h3> None
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".