Abstract 4367364: A Multicenter Friedreich Ataxia Registry Identifies Posterior Wall Thickness as a Predictor of Major Adverse Cardiac Events
Bibliographic record
Abstract
Background: Cardiac disease is the leading cause of premature mortality in Friedreich Ataxia (FA). However, validated prognostic indicators or early imaging biomarkers that can reliably predict cardiac events are lacking. To address this gap, we created a multicenter FA cardiac registry to characterize disease progression and identify early echocardiographic (echo) predictors of major adverse cardiac events (MACE). Methods: FA subjects from 6 North American centers with at least two echos performed ≥5 years apart were included. Demographics, FA-specific disease characteristics, and MACE (a composite of death, heart failure hospitalization, aborted sudden cardiac death, and/or life-threatening arrhythmia) were abstracted. Digital echo images were re-read at a centralized core lab. Paired T tests were utilized for comparison of the normally distributed first and second echo parameters, and logistic regression models were utilized to evaluate for predictors of MACE. Results: Of 115 subjects, 52 (45%) were female. Mean age at diagnosis was 10 ± 3 years, median GAA1 repeat length (a marker of genetic severity) was 700 (range 66–1200), and median age at first analyzed echo was 12 years (range 4-36). MACE occurred in 35 (30%) of the cohort. Serial echocardiograms demonstrated progressive worsening of multiple cardiac parameters over time, including markers of ventricular thickness and hypertrophy, chamber size, systolic function, and novel markers of atrial and ventricular strain (Table 1). On univariate analysis, several markers of hypertrophy and systolic function from the earliest analyzed echo predicted MACE. However, after adjusting for age of diagnosis, GAA1 repeat length, and collinearity among variables, only posterior wall thickness in diastole (PWTd) remained an independent predictor of MACE (odds ratio 1.8 per mm increase, 95% CI 1.1–3.3, p = 0.028). Conclusions: This multicenter FA registry identifies early posterior wall thickness as a significant and independent predictor of major adverse cardiac events in FA. Each millimeter increase in PWTd on early echo is associated with nearly double the odds of MACE. This commonly acquired parameter is often overlooked and may serve as a powerful early marker of cardiac risk. Further investigation of PWTd as a potential surrogate endpoint for therapeutic trials in FA is warranted.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".