Abstract 272: Essential Requirements to Develop Stroke Thrombectomy Program in Limited Resource Settings: A Modified Delphi Survey
Bibliographic record
Abstract
Introduction Mechanical thrombectomy (MT) has revolutionized care for patients with ischemic strokes from large vessel occlusion (LVO) leading to adoption as the standard of care. However, the Mechanical Thrombectomy Global Access For Stroke (MT‐GLASS) study, demonstrated that global access to mechanical thrombectomy is <3% of the demand. One possible reason for these shortcomings are the significant resources required to develop a sustainable MT service. Our aim is to understand what infrastructure, equipment and staff are essential to perform safe and effective MT for LVO‐stroke in resource limited settings. Methods Using a modified Delphi method iterative rounds of surveys were administered to an international panel of stroke leaders. Initial survey questions covered optimal and minimal requirements for diagnosis, imaging, equipment, and staff to perform MT. Results from the initial survey underwent qualitative analysis to create statements identifying essential requirements for a stroke thrombectomy program in resource‐limited setting. These statements were distributed in follow up surveys with participants rating their level of agreement with each statement using a 5‐point Likert scale. Consensus was defined as 70‐100% of respondents agreeing (Likert scale levels strongly agree and agree) or disagreeing (Likert scale levels disagree and strongly disagree) with the statement. Results A total of 27 experts answered the initial 40‐question survey. Experts from various specialties including neurosurgery, neurointervention, and vascular neurology, with majority (63%) from an academic center (63%) across 18 countries participated. Experts uniformly agreed that it is possible to develop a stroke thrombectomy program in a resource‐limited setting though barriers would include cost, public awareness of stroke symptoms, and developing an emergency triage protocol to efficiently identify and image potential MT candidates. 86% of respondents agreed that interventionalists are the appropriate provider to identify candidates for MT. There was disagreement on minimum necessary imaging required to identify a candidate for MT with the most common answer of CT head and CTA (54%) not meeting consensus definition. Essential equipment to perform MT that achieved consensus included (% agreement) access sheath (92%), guide catheter (100%), glidewire (100%), aspiration catheter (100%), aspiration syringe (92%), microcatheter and microwire (85%). Other listed equipment did not achieve consensus as essential equipment including ultrasound for access (8%), diagnostic catheter (69%), aspiration pump (23%), stent retriever (69%), extracranial stent (69%), intracranial stent (54%), and coils (54%). Experts agreed that access to common femoral artery can be safely obtained through palpation alone (76% agree) and that closure devices are not necessary as manual pressure will suffice (76% agree). Example of area lacking consensus is the necessity of invasive blood pressure monitoring follow MT (54% agree it is necessary, 30% disagree, 16% indifferent). Conclusions We have demonstrated that a strong consensus exists among experts that it is possible to create a MT program in a limited resource setting. While country specific barriers need to be addressed, the essential requirements for development of these programs were identified. We believe that these results can serve as a blueprint for development of national stroke programs which include MT.
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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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".