Abstract T P309: Cost Effectiveness for Intra-arterial Stroke Therapy Achieved with Image-based Selection and not with Type of Device
Bibliographic record
Abstract
Background: Our aim was to determine if stentriever treatment results in cost effectiveness over Merci thrombectomy and to identify a cost-effective imaging threshold for intra-arterial treatment selection. Methods: With institutional approval, we retrospectively reviewed patients undergoing intra-arterial stroke therapy from March 2011 to March 2013 at our center. We collected the following data: stroke score, occlusion site, baseline Alberta Stroke Program Early CT Score (ASPECTS), device used, reperfusion, hemorrhage, 90-day modified Rankin Score (mRS), and procedure cost. Using published criteria, a quality-adjusted life year (QUALY) value of 0.74 and 0.4 was ascribed to a mRS outcome 2 respectively. Using the procedural mean cost, we calculated an incremental cost efficiency ratio (ICER) for stentriever versus Merci embolectomy and for interventions done for a baseline ASPECTS above and below the following thresholds: > 6, > 7, > 8, and > 9. Using established criteria, we identified a cost effective patient selection if the ICER was positive and less than $50,000/QUALY. Results: Our cohort included 122 patients, 45 treated with Merci in the first year and 78 with stentrievers in year two. Reperfusion occurred in 79% (87% in the stentriever and 64% with Merci groups, p=0.002). The good outcome rate for the entire cohort was 40% (43% good outcomes in the stentriever and 33% in the Merci groups, p=0.21) respectively. Stentriever interventions were not cost effective compared to Merci embolectomy (ICER > $500,000/QUALY). Using baseline ASPECTS > 6 and > 7 as a selection criteria for intervention, the good outcome rate was 42% and 44% respectively but with a negative ICER due to higher costs in treating those with lower scores. For those with an ASPECTS > 8 and > 9, the good outcome rate was 44% and 54% with an ICER of $40,000/QUALY and $24,000/QUALY respectively. Conclusions: At our institution, despite better outcome and reperfusion rates, stentriever interventions are yet to show a cost benefit. Optimizing patient selection by using the ASPECTS scoring system has led to improved clinical outcomes and cost effectiveness. Further prospective study may validate this technique for greater value to the individual patient and the health system at large.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".