Development and Validation of a Clinical Predictive Model for Identifying Hypertrophic Cardiomyopathy Patients at Risk for Atrial Fibrillation: The HCM-AF Score
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is the most common sustained arrhythmia in hypertrophic cardiomyopathy (HCM), associated with impaired quality of life, risk for embolic stroke, and unpredictable onset. We sought to create a predictive model to identify risk for AF development in HCM. Methods: A cohort of 1900 patients with HCM followed for newly diagnosed AF in the Tufts HCM center was used for model development. A cohort of 387 patients from Toronto General Hospital was used for external validation. Data in the development cohort generated the HCM-AF score, a point score to predict AF probability at 2 and 5 years: left atrial dimension (+2 points per 6 mm increase), age at clinical evaluation (+3 points per 10-year increase), age at initial HCM diagnosis (−2 points per 10-year increase), and heart failure symptoms (+3 points if symptomatic). Results: The HCM-AF score stratifies risk as low (<1.0%/y; score ≤17), intermediate (1.0-2.0%/y; score 18 to 21), and high risk (>2.0%/y; score ≥22) for AF development for individual patients. Concordance of the HCM-AF score was 0.70 in the development cohort and 0.68 in the external validation cohort. In the development cohort, 17.2% of high-risk patients developed AF (rate 3.4%/y), while only 3.3% of low-risk patients developed AF (rate 0.7%/y) at 5 years ( P <0.001). Similarly, in the external validation cohort, 13.3% of high-risk patients developed AF (rate 2.7%/y), whereas only 1.1% of low-risk patients developed AF (rate 0.2%/y). The HCM-AF score provided greater predictive power for future AF risk than left atrial dimension alone (concordance of 0.58) and outperformed other non–HCM risk models. Conclusions: The HCM-AF score is a novel externally validated predictive tool to identify AF risk in HCM. This score can reliably stratify patients with HCM to risk of newly diagnosed AF and offers the opportunity to inform expectations regarding future clinical course.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".