Health Economic Impact of Incomplete Reperfusion Patterns After Endovascular Thrombectomy in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Incomplete reperfusion in endovascular thrombectomy (EVT) impacts patients' outcomes. Different incomplete reperfusion patterns may benefit from targeted therapeutic strategies, e.g. EVT-accessible incomplete reperfusion patterns could improve by performing additional EVT attempts, while EVT-non-accessible incomplete patterns might benefit from pharmacological therapies. The health-economic implications of these therapies are uncertain. This study aims to assess the potential economic benefits of improving incomplete reperfusion patterns after EVT. MATERIALS AND METHODS: Retrospective Data analysis from the ESCAPE-NA1 trial, which included patients with large vessel occlusion strokes undergoing EVT. Reperfusion patterns were classified as near-/complete (eTICI 2c3), EVT-accessible incomplete (eTICI 2b), or EVT-non-accessible incomplete (eTICI 2b) and we compared multiple attempts to achieve eTICI 2c3 vs. first-pass eTICI 2c3. A Markov-Model was built to compare lifetime costs and quality adjusted life-years (QALY) for each reperfusion pattern over a lifetime horizon, considering both healthcare and societal perspectives. RESULTS: A total of 1105 of patients were enrolled in the ESCAPE-NA1 trial of which 949 with eTICI 2b, 2c and 3 were further analyized (mean age 70.7 ± 13.6 [SD]; 463 female). Near-Complete reperfusion (eTICI 2c3) was achieved in 506/1105 patients (45.8%). Incomplete reperfusion patterns (eTICI 2b) were found in 450/1105 (40.7%) patients. Angiography imaging could be further investigated in 443/450 (98.4%) cases with 147/443(33.2%) EVT-accessible and 296/443(66.8%) EVT-non-accessible incomplete reperfusion patterns. Compared to EVT-accessible and EVT-non-accssible incomplete reperfusion, achieving complete (eTICI 2c3) reperfusion resulted in lower costs and an additional 1.14/0.45 QALYs, making it the dominant strategy from a health-economic perspective. In the complete reperfusion (eTICI 2c3) group, cumulative lifetime QALYs were similar with 5.25 for single-pass eTICI 2c3 and 5.19 for multi-pass eTICI 2c3. CONCLUSIONS: Improving incomplete reperfusion patterns after EVT has considerable potential health economic benefits, both in the presence and absence of a target occlusion that is amenable to EVT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".