Enhancing outcomes in large-infarct stroke: the critical role of thrombectomy
Bibliographic record
Abstract
Dear Editor, Stroke remains one of the leading causes of mortality and long-term disability worldwide1,2. The advent of thrombectomy has revolutionized the management of acute ischemic stroke, particularly for strokes due to large vessel occlusions in the anterior circulation3–5. However, the application of this procedure in patients with extensive ischemic damage, or large infarcts, has been a subject of considerable debate among neurosurgeons and neurologists. A recent study has provided critical insights into this issue and suggests a potential paradigm shift in our approach to these challenging cases6. The study meticulously assigned patients with proximal cerebral vessel occlusion and a large infarct [defined by an Alberta Stroke Program Early Computed Tomographic Score (ASPECTS) of ≤5] to either an endovascular thrombectomy plus medical care group or a medical care alone group. The primary outcome was the score on the modified Rankin scale (mRS) at 90 days. The median mRS score was significantly lower in the thrombectomy group compared to the control group, indicating a less severe degree of disability in patients who underwent thrombectomy, despite the initially large size of infarct. Also, there was a notable reduction in mortality at 90 days in the thrombectomy group (36.1%) compared to the control group (55.5%), underscoring the potential of thrombectomy to save lives in the context of large infarcts. However, the incidence of symptomatic intracerebral hemorrhage was higher in the thrombectomy group (9.6%) than in the control group (5.7%). While this raises safety concerns, the overall benefit in terms of survival and functional outcomes may justify the increased risk in selected patients. Because of the promising results of the trial and evidence from similar studies done in the past, the study was stopped prematurely, reflecting a growing consensus on the efficacy of thrombectomy in large-infarct strokes. Traditionally, the presence of a large infarct was considered a contraindication to thrombectomy due to the perceived risks of reperfusion injury and hemorrhagic transformation. However, this study challenges that notion by demonstrating clear benefits in terms of functional outcomes and mortality. Neurosurgeons should consider thrombectomy in patients with large infarcts, especially those who present early and have salvageable brain tissue indicated by imaging. Also, the success of thrombectomy in this study underscores the importance of technical skill and the need for rapid intervention. Neurosurgeons must be adept at quickly assessing the extent of infarct and vascular anatomy to make swift decisions about the feasibility of thrombectomy. The study’s findings should encourage the development of specialized training programs focused on endovascular techniques for large infarcts. Furthermore, effective management of large-infarct strokes requires a collaborative approach. Neurosurgeons, neurologists, radiologists, and stroke nurses must work together to optimize patient outcomes. This study reinforces the need for well-coordinated stroke teams that can perform rapid imaging assessments, provide comprehensive medical care, and execute thrombectomy procedures efficiently. While the study is groundbreaking, there are several limitations that need to be considered. First, the strict inclusion criteria (ASPECTS ≤5, proximal vessel occlusion) mean that the findings may not be generalizable to all stroke patients, particularly those with lower ASPECTS scores or those presenting later than 6.5 h after symptom onset. Second, the early termination of the trial could lead to an overestimation of the benefits of thrombectomy because it did not enroll as many patients as originally planned. Further studies are needed to confirm these results. Third, the increased risk of symptomatic intracerebral hemorrhage in the thrombectomy group is a concern. Neurosurgeons must weigh this risk against the potential benefits in terms of mortality and functional recovery. Patient-specific factors such as age, comorbidities, and the extent of collateral circulation should influence this risk assessment. Integration of thrombectomy into surgical practice can be facilitated by the revision of current stroke management guidelines to include the use of thrombectomy in patients with large infarcts. These guidelines should specify the imaging criteria, time windows, and clinical characteristics that optimize patient selection for this procedure. Also, hospitals should invest in training their neurosurgical teams in the latest thrombectomy techniques, especially for challenging cases with large infarcts. Additionally, ensuring the availability of around-the-clock imaging facilities and interventional neuroradiology services is crucial for implementing these findings in clinical practice. Furthermore, educating patients and their families about the risks and benefits of thrombectomy is vital. Clear communication can help manage expectations and foster informed decision-making, especially in emergency situations where time is of the essence. Further research is needed to explore the long-term outcomes of thrombectomy in large-infarct strokes and to identify additional biomarkers that can predict which patients are most likely to benefit from this intervention. Studies should also focus on optimizing post-thrombectomy care to enhance recovery and reduce complications. In conclusion, thrombectomy can be successfully employed for large infarcts. Stakeholders should integrate these findings into practice and work collaboratively with multidisciplinary teams to improve the outcomes in stroke patients. Ethical approval Ethical approval is not applicable for this correspondence article. Consent Informed consent is not applicable for this correspondence article. Source of funding Not applicable. Author contribution K.N. M.: conceptualization, project administration, supervision, validation, writing—original draft and writing—review and editing. A.A.: conceptualization, project administration, supervision, validation, writing—original draft and writing—review and editing. P.S.: writing—original draft and writing—review and editing. G.M.C.N.: supervision, validation, writing—review and editing. R.K.S.: writing—original draft and writing—review and editing. A.O.A.: supervision, validation, writing—review and editing. M.N.K.: writing—original draft and writing—review and editing. O.I.M.: writing—original draft and writing—review and editing. S.G.: 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. O.O.O.: supervision, validation, writing—review and editing. Conflicts of interest disclosure The authors declare no conflicts of interest. Research registration unique identifying number (UIN) Not applicable. Guarantor Olabisi Oluwagbemiga Ogunleye. Data availability statement Not applicable. Provenance and peer review Not commissioned, externally peer-reviewed.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".