Predicting Stroke in Heart Failure and Preserved Ejection Fraction Without Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: The rate of stroke in patients with heart failure (HF) and preserved ejection fraction but without atrial fibrillation (AF), is uncertain as is whether it is possible to reliably predict the risk of stroke in these patients. METHODS: We validated a previously developed simple risk model for stroke among patients enrolled in the I-Preserve trial (Irbesartan in Heart Failure With Preserved Systolic Function) and PARAGON-HF trial (Efficacy and Safety of LCZ696 Compared to Valsartan, on Morbidity and Mortality in Heart Failure Patients With Preserved Ejection Fraction). The risk model consisted of 3 variables: history of previous stroke, insulin-treated diabetes, and plasma N-terminal pro-B-type natriuretic peptide level. RESULTS: Of the 8924 patients included in the pooled trial dataset, 5126 patients did not have AF at baseline. Among patients without AF, 190 (3.7%) experienced a stroke over a median follow-up of 3.6 years (rate 10.5 per 1000 patient-years). The risk for stroke increased with increasing risk score: second tertile hazard ratio, 1.78 (95% CI, 1.17-2.71); third tertile hazard ratio, 3.03 (95% CI, 2.06-4.47), with the first tertile as reference. For patients in the third tertile, the occurrence rate of stroke was 17.7 per 1000 patient-years, similar to that in patients with AF not receiving anticoagulation (20.7 per 1000 patient-years), and those with AF who were receiving anticoagulation (14.5 per 1000 patient-years). Model discrimination was good with a C index of 0.81 (0.68-0.91) and a simple score could be created from the model. CONCLUSIONS: A simple risk model can detect a subset of HF and preserved ejection fraction patients without AF who have a higher risk for stroke. The balance of risk-to-benefit in these individuals may justify the use of prophylactic anticoagulation, but this hypothesis needs to be prospectively evaluated. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifiers: NCT00095238 and NCT01920711.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".