Re-evaluating the timing of mechanical thrombectomy in low ASPECTS stroke: insights from real-world data
Bibliographic record
Abstract
Dear Editor, Mechanical thrombectomy (MT) has transformed the management of acute ischemic stroke, particularly in patients with large vessel occlusion (LVO)1–3. The effectiveness of MT within 6 hours of symptom onset is well-documented, but recent studies have extended this window to 24 h under specific conditions1,4–6. A recent study by Elawady et al.7 with 10 229 participants provides valuable insights into the outcomes of MT in patients with low Alberta Stroke Program Early Computed Tomography Scores (ASPECTS) presenting in early (<6 h) and late (6–24 h) windows. Elawady et al.7 found no significant difference in acceptable outcomes [modified Rankin Scale (mRS) scores of 0–3] at 90 days between patients treated in the early and late windows. Also, the study reported significantly higher rates of sICH in the early window group (22.9%) compared to the late window group (10.8%)7. These findings have profound implications for neurosurgeons. First, the study challenges the traditional emphasis on the “golden hour” and suggests that MT can be beneficial beyond the 6-hour window, even in patients with low ASPECTS who typically present with extensive infarction. This implies that neurosurgeons may consider MT for a broader range of patients, including those presenting later. This is particularly relevant in settings where patients may experience delays in reaching thrombectomy-capable centers. The ability to extend the treatment window up to 24 h can significantly increase the number of eligible patients, potentially improving overall stroke outcomes. Second, an early intervention may carry increased risks, particularly in patients with extensive infarcts as indicated by low ASPECTS. Hence, neurosurgeons must weigh the benefits of early intervention against the increased risk of sICH. In patients with low ASPECTS, the decision to proceed with MT within the first 6 hours should be made cautiously, considering individual risk factors such as age, comorbidities, and extent of infarction. Third, to provide the best care, patient selection remains crucial. While the extended window offers more opportunities for intervention, identifying patients who are most likely to benefit is essential. Advanced imaging techniques to assess collateral circulation and tissue viability should be integrated into the decision-making process to optimize outcomes. Fourth, the choice of thrombectomy devices and techniques may influence the risk of sICH. Surgeons should consider using devices and approaches that minimize trauma to the vessel wall and reduce the likelihood of hemorrhagic transformation. Continuous advancements in thrombectomy technology and techniques should be evaluated and incorporated into practice to enhance safety. Fifth, given the heightened risk of sICH, intensive postoperative monitoring is essential for early window patients. Immediate post-procedure imaging and close neurological monitoring can help detect and manage hemorrhagic complications promptly, potentially mitigating their impact on patient outcomes2,8,9. Sixth, the study’s use of real-world data from the Stroke Thrombectomy and Aneurysm Registry highlights the variability in clinical practice across different centers. This variability can impact outcomes and underscores the importance of standardizing protocols. Hence, it is necessary to develop and adhere to standardized protocols for MT can reduce variability and improve outcomes. These protocols should include guidelines for patient selection, imaging criteria, procedural techniques, and postoperative care. Ongoing training and education for neurosurgeons are crucial to ensure consistency in technique and adherence to best practices. Simulation training, workshops, and peer review can help maintain high standards of care across different centers. Moreover, collaboration among stroke centers and sharing of data and experiences can facilitate continuous improvement in practice. Multi-center registries and collaborative research efforts can identify best practices and areas for improvement, driving advancements in stroke care. While the study by Elawady and colleagues provides important insights, several limitations should be acknowledged. The retrospective design introduces potential biases, and the lack of core volume and collateral score data limits the understanding of these critical factors. Moreover, the high mortality rates reported in both groups highlight the severe nature of stroke in low ASPECTS patients. To address these limitations various aspects can be considered. First, prospective, randomized controlled trials are needed to validate the findings of this study and provide more definitive evidence on the optimal timing of MT in low ASPECTS patients. Second, incorporating advanced imaging techniques, such as perfusion imaging and collateral assessment, into future studies can provide a more comprehensive understanding of which patients benefit most from MT and why. Third, research should focus on identifying strategies to optimize outcomes in low ASPECTS patients, including the development of new devices, techniques, and adjunctive therapies that can improve recanalization rates while minimizing complications. In conclusion, demonstrating that functional outcomes are comparable between early and late intervention windows in patients with anterior circulation large vessel occlusion and low ASPECTS. However, the increased risk of sICH in the early window and the high overall mortality rates underscore the need for careful patient selection and vigilant postoperative care. As surgical practice evolves, incorporating these findings into clinical protocols, enhancing standardization, and continuing to pursue research and innovation will be crucial in improving outcomes for stroke patients. Ethical approval Ethical approval is not applicable for this correspondence article. Consent Informed consent is not applicable for this correspondence article. Sources of funding None. Author contribution K.M.A.: conceptualization, project administration, supervision, validation, writing—original draft and writing—review and editing. A.O.A.: conceptualization, project administration, supervision, validation, writing—original draft and writing—review and editing. S.A.G.: supervision, validation, writing—original draft and writing—review and editing. M.P.S.: writing—original draft and writing—review and editing. M.N.K.: writing—original draft and writing—review and editing. R.K.S.: writing—original draft and writing—review and editing. Q.S.Z.: writing—original draft and writing—review and editing. S.R.: writing—original draft and writing—review and editing. S.G.: writing—original draft and writing—review and editing. A.A.: supervision, validation, and writing—review and editing. Conflicts of interest disclosure No conflict of interest to declare. Research registration unique identifying number (UIN) None. Guarantor Kelechi Michael Azode and Abass Oluwaseyi Ajayi. Data availability statement None. Provenance and peer review Not commissioned, externally peer-reviewed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".