Patient and procedure selection for mechanical thrombectomy: Toward personalized medicine and the role of artificial intelligence

CT perfusion for lesion-symptom mapping in large vessel occlusion ischemic stroke

BACKGROUND

Identifying eloquent regions associated with poor outcomes based on CT perfusion (CTP) may help inform personalized decisions on selection for endovascular therapy (EVT) in patients with large vessel occlusion (LVO) ischemic stroke. This study aimed to characterize the relationship between CTP-defined hypoperfusion and National Institutes of Health Stroke Scale (NIHSS) subitem deficits.

METHODS

Patients with anterior circulation LVO, baseline CTP, itemized NIHSS at presentation and 24 hours were included. CTP was analyzed using e-CTP (Brainomix, UK). Time to maximal contrast (Tmax) prolongation was defined as >6 s, and penumbra as the difference between Tmax and ischemic core (relative cerebral blood flow<30%). Voxel-lesion-symptom mapping was performed using sparse canonical correlation analysis. For each NIHSS subitem, and total NIHSS, the associations were plotted between Tmax voxels with baseline NIHSS, and penumbra voxels with delta NIHSS (24 hours minus baseline).

RESULTS

This study included 171 patients. Total NIHSS was predicted by hypoperfusion in left frontal cortex and subcortical white matter tracts. Voxels associated with neurological recovery were symmetrical and subcortical.Limb deficits were associated with respective motor cortex regions and descending motor tracts, with negative correlation within the contralateral hemispheres. A similar but smaller cluster of voxels within the penumbra was associated with NIHSS improvement. Language impairment correlated with left frontal cortex and superior temporal gyrus voxels. With the exception of dysarthria, significant associations were observed and more diffusely distributed in all other NIHSS subitems.

CONCLUSIONS

These results demonstrate the feasibility of hypoperfusion-to-symptom mapping in LVO. Symptom-based mapping from presenting imaging could refine treatment decisions targeting specific neurological deficits.

The Artificial Intelligence Revolution in Stroke Care: A Decade of Scientific Evidence in Review

BACKGROUND

The emergence of artificial intelligence (AI) has significantly influenced the diagnostic evaluation of stroke and has revolutionized acute stroke care delivery. The scientific evidence evaluating the role of AI, especially in areas of stroke treatment and rehabilitation is limited but continues to accumulate. We performed a systemic review of current scientific evidence evaluating the use of AI in stroke evaluation and care and examined the publication trends during the past decade.

METHODS

A systematic search of electronic databases was conducted to identify all studies published from 2012 to 2022 that incorporated AI in any aspect of stroke care. Studies not directly relevant to stroke care in the context of AI and duplicate studies were excluded. The level of evidence and publication trends were examined.

RESULTS

A total of 623 studies were examined, including 101 reviews (16.2%), 9 meta-analyses (1.4%), 140 original articles on AI methodology (22.5%), 2 case reports (0.3%), 2 case series (0.3%), 31 case-control studies (5%), 277 cohort studies (44.5%), 16 cross-sectional studies (2.6%), and 45 experimental studies (7.2%). The highest published area of AI in stroke was diagnosis (44.1%) and the lowest was rehabilitation (12%). A 10-year trend analysis revealed a significant increase in AI literature in stroke care.

CONCLUSIONS

Most research on AI is in the diagnostic area of stroke care, with a recent noteworthy trend of increased research focus on stroke treatment and rehabilitation.

iSPAN: Explainable prediction of outcomes post thrombectomy with Machine Learning

PURPOSE

This study aimed to develop and evaluate a machine learning model and a novel clinical score for predicting outcomes in stroke patients undergoing endovascular thrombectomy.

MATERIALS AND METHODS

This retrospective study included all patients aged over 18 years with an anterior circulation stroke treated at a thrombectomy centre from 2010 to 2020 with external validation. The primary outcome was day 90 mRS ≥3. Existing clinical scores (SPAN and PRE) and Machine Learning (ML) models were compared. A novel clinical score (iSPAN) was derived by adding an optimised weighting of the most important ML features to the SPAN.

RESULTS

812 patients were initially included (397 female, average age 73), 63 for external validation. The best performing clinical score and ML model were SPAN and XGB (sensitivity, specificity and accuracy 0.290, 0.967, 0.628 and 0.693, 0.783, 0.738 respectively). A significant difference was found overall and our XGB model was more accurate than SPAN (p < 0.0018). The most important features were Age, mTICI and total number of passes. The addition of 11 points for mTICI of ≤2B and 3 points for ≥3 passes to the SPAN achieved the best accuracy and was used to create the iSPAN. iSPAN was not significantly less accurate than our XGB model (p > 0.5). In the external validation set, iSPAN and SPAN achieved sensitivity, specificity, and accuracy of (0.735, 0.862, 0.79) and (0.471, 0.897, 0.67) respectively.

CONCLUSION

iSPAN incorporates machine-derived features to achieve better predictions compared to existing clinical scores. It is not inferior to our XGB model and is externally generalisable.

Rescue Strategy for Hemorrhagic Complication During Mechanical Thrombectomy and the Clinical Outcome

PURPOSE

Hemorrhagic complications occasionally occur during mechanical thrombectomy and may lead to catastrophic outcomes. Therefore, remedial strategies require careful investigation. Herein, we aimed to evaluate a cohort of patients who experienced hemorrhage during endovascular procedures, the rescue methods used, and outcomes observed.

METHOD

This prospective study included patients who had hemorrhagic complications observed on digital subtraction angiography (DSA) during mechanical thrombectomy, between October 2017 and October 2022, at a high-volume stroke center. Functional outcomes were assessed using the modified Rankin scale (mRS) score at a 90-day follow-up. The primary outcomes were favorable outcomes (mRS score: 0-2 points) and mortality rates. The secondary outcomes were rescue therapy success rates, according to extravasation observed on the final DSA, recanalization status, and causes of hemorrhage.

RESULTS

From October 2017 to October 2022, 1537 patients with stroke received emergency endovascular therapy, and 1147 patients completed a 90-day follow-up. Hemorrhage was observed in 33 (2.1%) patients in the process of endovascular interventions. Eighteen (54.5%) cases of hemorrhage were caused by microwire or microcatheter perforation. Mechanical stretching of the vessel during stent retriever withdrawal resulted in 8 (24.2%) cases of hemorrhage. Nine (27.3%) instances of hemorrhage stopped after the reversal of heparin administration and introduction of blood pressure control measures. Further endovascular rescue treatment was performed in 11 patients. Intracranial inflation of the balloon for tamponade stopped 10 hemorrhages, and 1 patient underwent additional coil embolization. Fifteen (45.5%) patients died within 90 days after the procedure. Three (9.1%) patients recovered functional independence (mRS score: 0-2 points) within 90 days.

CONCLUSION

Hemorrhage during mechanical thrombectomy is a rare but severe complication of acute ischemic stroke with high mortality and disability rates. Intracranial inflation of a balloon for tamponade can effectively stop extravasation.

CLINICAL IMPACT

This paper described haemorrhagic events characterised by contrast extravasation in the procedure of mechanical thrombectomy due to various causes. Although this complication is rare, patients showed a high mortality and disability rate. There are limited reports available. We found self-limiting haemorrhage had a rather benign prognosis and balloon tamponade could effectively stop the extravasation and might reduce the death rate within 90d. The methods we adopted could be applied in the clinical practice and help neuro-interventionist cope with this complication more promptly and effectively.

Artificial Intelligence in Public Health: Current Trends and Future Possibilities

Artificial intelligence (AI) is a discipline that studies whether and how intelligent computer systems that can simulate the capacity and behaviour of human thought can be created [...]