TH64. NEXT GENERATION GENE PANEL SEQUENCING: LESSONS FROM OUR MEDICAL NEUROPSYCHIATRIC GENETICS CLINIC EXPERIENCE
Bibliographic record
Abstract
Genetics of sudden cardiac deaths (SCD) remains frequently undetected. Genetic analysis is recommended in undefined selected cases in the 2021 ERC-guideline. The emergency medical service and physicians (EMS) may play a pivotal role for unraveling SCD by saving biomaterial for later molecular autopsy. Since for high-throughput DNA-sequencing (NGS) high quality genomic DNA is needed.We investigated in a prospective proof-of-concept study the role of the EMS for the identification of genetic forms of SCDs in the young.We included patients aged 1–50 years with need for cardiopulmonary resuscitation attempts (CPR). Cases with non-natural deaths were excluded. In two German counties with 562,904 residents 39,506 services were analysed. Paired end panel-sequencing was performed, and variants were classified according to guidelines of the American College of Medical Genetics (ACMG).769 CPR-attempts were recorded (1.95% of all EMS-services; CPR-incidence 68/100,000). In 103 cases CPR were performed in patients < 50y. 58% died on scene, 26% were discharged from hospital. 24 subjects were included for genotyping. Of these 33% died on scene, 37.5% were discharged from hospital. 25% of the genotyped patients were carriers of (likely) pathogenic (ACMG-4/-5) variants. 67% carried variants with unknown significance (ACMG-3). 2 of them had familial history for arrhythmogenic cardiomyopathy or had to be re-classified as ACMG-4 carriers due to whole exome sequencing.The EMS contributes especially in fatal OHCA-cases to increase the yield of identified genetic conditions by collecting a blood sample on scene. Thus, the EMS can contribute significantly to primary and secondary prophylaxis in affected families.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".