A Validated Model for Sudden Cardiac Death Risk Prediction in Pediatric Hypertrophic Cardiomyopathy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Hypertrophic cardiomyopathy is the leading cause of sudden cardiac death (SCD) in children and young adults. Our objective was to develop and validate a SCD risk prediction model in pediatric hypertrophic cardiomyopathy to guide SCD prevention strategies. METHODS: In an international multicenter observational cohort study, phenotype-positive patients with isolated hypertrophic cardiomyopathy <18 years of age at diagnosis were eligible. The primary outcome variable was the time from diagnosis to a composite of SCD events at 5-year follow-up: SCD, resuscitated sudden cardiac arrest, and aborted SCD, that is, appropriate shock following primary prevention implantable cardioverter defibrillators. Competing risk models with cause-specific hazard regression were used to identify and quantify clinical and genetic factors associated with SCD. The cause-specific regression model was implemented using boosting, and tuned with 10 repeated 4-fold cross-validations. The final model was fitted using all data with the tuned hyperparameter value that maximizes the c-statistic, and its performance was characterized by using the c-statistic for competing risk models. The final model was validated in an independent external cohort (SHaRe [Sarcomeric Human Cardiomyopathy Registry], n=285). RESULTS: score, peak left ventricular outflow tract gradient, and presence of a pathogenic variant. Unlike in adults, left ventricular outflow tract gradient had an inverse association, and family history of SCD had no association with SCD. Clinical and clinical/genetic models were developed to predict 5-year freedom from SCD. Both models adequately discriminated between patients with and without SCD events with a c-statistic of 0.75 and 0.76, respectively, and demonstrated good agreement between predicted and observed events in the primary and validation cohorts (validation c-statistic 0.71 and 0.72, respectively). CONCLUSION: Our study provides a validated SCD risk prediction model with >70% prediction accuracy and incorporates risk factors that are unique to pediatric hypertrophic cardiomyopathy. An individualized risk prediction model has the potential to improve the application of clinical practice guidelines and shared decision making for implantable cardioverter defibrillator insertion. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT0403679.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it