A Prospective Evaluation of the Established Criteria for Heart Failure with Preserved Ejection Fraction Using the Alberta HEART Cohort
Bibliographic record
Abstract
AIMS: Heart failure with a preserved ejection fraction (HF-PEF) remains a difficult clinical diagnosis. The aim of this study was to test the utility of established criteria to classify patients with HF-PEF. We prospectively enrolled patients into one of five groups across a spectrum of cardiac disease and applied three different criteria for HF-PEF and calculated diagnostic metrics. METHODS AND RESULTS: A total of 565 patients were included in the analysis, including 170 patients with an adjudicated diagnosis of HF-PEF, 152 patients with heart failure with reduced ejection fraction, 152 patients at risk for heart failure, and 91 age-matched healthy controls. For the diagnosis of HF-PEF, the positive likelihood ratios were 6.1, 6.9, and 4.8 for the Zile, European Society of Cardiology (ESC) 2007, and ESC 2016 criteria, respectively. The negative likelihood ratios were 0.58, 0.60, and 0.42 for the Zile, ESC 2007, and ESC 2016 criteria, respectively. All three criteria lacked sensitivity to detect HF-PEF (46.5%, 44.1%, and 51.8%, respectively) but were highly specific (92.4%, 93.9%, and 89%, respectively). We further evaluated the criteria to distinguish HF-PEF from other diagnoses after excluding heart failure with reduced ejection fraction; the results were similar. CONCLUSIONS: In this community based cohort, the likelihood ratios of the existing criteria for HF-PEF were not at the level necessary to be considered diagnostic. Improved criteria for the diagnosis of patients with HF-PEF are needed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".