Time Is Brain: The Importance of an Accurate <i>SCN1A</i> Prediction Score in the Era of Precision Medicine
Bibliographic record
Abstract
Development and Validation of a Prediction Model for Early Diagnosis of SCN1A-Related EpilepsiesBrunklaus A, Pérez-Palma E, Ghanty I, et al. Neurology. 2022;98(11):e1163-e1174. doi:10.1212/WNL.0000000000200028.Background and objectives:Pathogenic variants in the neuronal sodium channel α1-subunit gene (SCN1A) are the most frequent monogenic cause of epilepsy. Phenotypes comprise a wide clinical spectrum including the severe childhood epilepsy, Dravet syndrome, characterized by drug-resistant seizures, intellectual disability and high mortality, and the milder genetic epilepsy with febrile seizures plus (GEFS+), characterized by normal cognition. Early recognition of a child’s risk for developing Dravet syndrome versus GEFS+ is key for implementing disease-modifying therapies when available before cognitive impairment emerges. Our objective was to develop and validate a prediction model using clinical and genetic biomarkers for early diagnosis of SCN1A-related epilepsies.Methods:Retrospective multicenter cohort study comprising data from SCN1A-positive Dravet syndrome and GEFS+ patients consecutively referred for genetic testing (March 2001-June 2020) including age of seizure onset and a newly-developed SCN1A genetic score. A training cohort was used to develop multiple prediction models that were validated using two independent blinded cohorts. Primary outcome was the discriminative accuracy of the model predicting Dravet syndrome versus other GEFS+ phenotypes.Results:1018 participants were included. The frequency of Dravet syndrome was 616/743 (83%) in the training cohort, 147/203 (72%) in validation cohort 1 and 60/72 (83%) in validation cohort 2. A high SCN1A genetic score 133.4 (SD, 78.5) versus 52.0 (SD, 57.5; p < 0.001) and young age of onset 6.0 (SD, 3.0) months versus 14.8 (SD, 11.8; p < 0.001) months, were each associated with Dravet syndrome versus GEFS+. A combined “SCN1A genetic score and seizure onset” model separated Dravet syndrome from GEFS+ more effectively (area under the curve [AUC], 0.89 [95% CI, 0.86-0.92]) and outperformed all other models (AUC, 0.79-0.85; p < 0.001). Model performance was replicated in both validation cohorts 1 (AUC, 0.94 [95% CI, 0.91-0.97]) and 2 (AUC, 0.92 [95% CI, 0.82-1.00]).Discussion:The prediction model allows objective estimation at disease onset whether a child will develop Dravet syndrome versus GEFS+, assisting clinicians with prognostic counseling and decisions on early institution of precision therapies (http://scn1a-prediction-model.broadinstitute.org/).Classification of evidence:This study provides Class II evidence that a combined “SCN1A genetic score and seizure onset” model distinguishes Dravet syndrome from other GEFS+ phenotypes.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.027 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".