OP168 Costs And Effectiveness Of Whole Exome Sequencing (WES) In Patients With Unsolved Rare Disease Through The Diagnostic Pathway
Bibliographic record
Abstract
Introduction Patients suspected of having a rare genetic disease often experience lengthy and costly diagnostic odysseys. The timing of whole exome sequencing (WES) in the testing sequence, its diagnostic yield and test costs in the sequence all factor into estimates of cost-effectiveness analysis for health technology assessment. Methods We modeled the diagnostic pathway using a discrete event simulation model, starting with the first test result. We defined and populated the simulation based on data from the electronic medical records of n=307 from the Care-for-Rare SOLVE multi-center Canadian observational cohort. Five alternative diagnostic pathways were modeled based on the observed data: no WES, and WES as the first, second, third or fourth test in the sequence. WES as the second test in the sequence is considered standard of care in medical genetic centers in Canada. We assessed effectiveness of WES in terms of diagnostic yield, time to diagnosis, and costs as patient-level overall test costs (2020 CAD/USD) across the diagnostic pathway. Results Compared to molecular and specialized diagnostic tests only (i.e., no WES), WES increased diagnostic yield from 5 percent to 40 percent. The shortest time to diagnosis for those with a diagnosis was 1.82 years in the diagnostic pathway with WES as the second test. Test costs for each pathway were CAD2,800 (USD2,087, no WES), CAD2,700 (USD2,013, WES as first test), CAD3,500 (USD2,609, WES as second test), CAD4,500 (USD3,354, WES as third test), and CAD5,300 (USD3,951, WES as fourth test). Conclusions Placing WES earlier in the diagnostic pathway for patients suspected of having a rare disease is associated with an increased diagnostic yield, reduced time to diagnosis and lower overall test costs with the benefits being greater the earlier in the pathway that WES is implemented.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".