Skin Autofluorescence as a Predictor of First Heart Failure Hospitalization in Patients With Heart Failure With Preserved Ejection Fraction
Bibliographic record
Abstract
BACKGROUND: An autofluorescence (AF) reader can be used to diagnose skin AF non-invasively by measuring local accumulation of advanced glycation end-products. A number of studies have investigated the relationships between skin AF and cardiovascular disease. However, data regarding the usefulness of skin AF as a predictor of chronic heart failure remain limited. This prospective study aimed to elucidate the usefulness of skin AF as a predictor of first heart failure (HF) hospitalization in patients with HF with preserved ejection fraction (HFpEF). METHODS: A total of 412 outpatients with HFpEF with no history of HF hospitalization were enrolled. Patients were assigned to either the low (group L; skin AF ≤ 2.9 arbitrary units (AU); n = 303) or the high (group H; skin AF ≥ 3.0 AU; n = 109) group according to optimal skin AF cut-off levels determined using receiver operating characteristic curves. Clinical parameters and the usefulness of skin AF as a predictor of first HF hospitalization were evaluated. RESULTS: The E/e' ratio as a marker of left ventricular diastolic function was significantly higher in group H patients than in group L patients at baseline (group H, 11.8 ± 3.8; group L, 10.6 ± 3.3; P = 0.002). During the 72.7-month follow-up period, 43 HF cases were hospitalized (group L, 15 cases; group H, 28 cases; P < 0.001, log-rank test). Multivariate Cox regression analyses revealed that group H exhibited a significantly higher risk of first HF hospitalization than did group L (hazard ratio, 2.26; 95% confidence interval, 1.21 - 3.52; P = 0.014). CONCLUSIONS: The present study demonstrated that skin AF can predict the risk of first HF hospitalization in patients with HFpEF. Prospective studies, including intervention therapies, are required to validate our observations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".