Predictors of Infarct Growth Measured by Apparent Diffusion Coefficient Quantification in Patients with Acute Ischemic Stroke

Association of Carotid Artery Disease with Collateralization and Infarct Growth in Patients with Acute Middle Cerebral Artery Occlusion

BACKGROUND AND PURPOSE

Collaterals are important in large vessel occlusions (LVO), but the role of carotid artery disease (CAD) in this context remains unclear. This study aimed to investigate the impact of CAD on intracranial collateralization and infarct growth after thrombectomy in LVO.

MATERIALS AND METHODS

All patients who underwent thrombectomy due to M1 segment occlusion from 01/2015 to 12/2021 were retrospectively included. Internal carotid artery stenosis according to NASCET was assessed on the affected and nonaffected sides. Collaterals were assessed according to the Tan score. Infarct growth was quantified by comparing ASPECTS on follow-up imaging with baseline ASPECTS.

RESULTS

In total, 709 patients were included, 118 (16.6%) of whom presented with CAD (defined as severe stenosis ≥70% or occlusion ipsilaterally), with 42 cases (5.9%) being contralateral. Good collateralization (Tan 3) was present in 56.5% of the patients with ipsilateral CAD and 69.1% of the patients with contralateral CAD. The ipsilateral stenosis grade was an independent predictor of good collateral supply (adjusted OR: 1.01; NASCET point, 95% CI: 1.00-1.01; P = .009), whereas the contralateral stenosis grade was not (P = .34). Patients with ipsilateral stenosis of ≥70% showed less infarct growth (median ASPECTS decay: 1; IQR: 0-2) compared with patients with 0%-69% stenosis (median: 2; IQR: 1-3) (P = .005). However, baseline ASPECTS was significantly lower in patients with stenosis of 70%-100% (P < .001). The results of a multivariate analysis revealed that increasing ipsilateral stenosis grade (adjusted OR: 1.0; 95% CI: 0.99-1.00; P = .004) and good collateralization (adjusted OR: 0.5; 95% CI: 0.4-0.62; P < .001) were associated with less infarct growth.

CONCLUSIONS

CAD of the ipsilateral ICA is an independent predictor of good collateral supply. Patients with CAD tend to have larger baseline infarct size but less infarct growth.

Machine learning-based prognostication of mortality in stroke patients

Objectives

Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke.

Materials and methods

Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors.

Results

The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes.

Conclusions

This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.

Predicting 90-day prognosis for patients with stroke: a machine learning approach

Background

Stroke is a significant global health burden and ranks as the second leading cause of death worldwide.

Objective

This study aims to develop and evaluate a machine learning-based predictive tool for forecasting the 90-day prognosis of stroke patients after discharge as measured by the modified Rankin Score.

Methods

The study utilized data from a large national multiethnic stroke registry comprising 15,859 adult patients diagnosed with ischemic or hemorrhagic stroke. Of these, 7,452 patients satisfied the study's inclusion criteria. Feature selection was performed using the correlation and permutation importance methods. Six classifiers, including Random Forest (RF), Classification and Regression Tree, Linear Discriminant Analysis, Support Vector Machine, and k-Nearest Neighbors, were employed for prediction.

Results

The RF model demonstrated superior performance, achieving the highest accuracy (0.823) and excellent discrimination power (AUC 0.893). Notably, stroke type, hospital acquired infections, admission location, and hospital length of stay emerged as the top-ranked predictors.

Conclusion

The RF model shows promise in predicting stroke prognosis, enabling personalized care plans and enhanced preventive measures for stroke patients. Prospective validation is essential to assess its real-world clinical performance and ensure successful implementation across diverse healthcare settings.

Role of intravenous alteplase on late lesion growth and clinical outcome after stroke treatment

Several acute ischemic stroke mechanisms that cause lesion growth continue after treatment which is detrimental to long-term clinical outcome. The potential role of intravenous alteplase treatment (IVT), a standard in stroke care, in cessing the physiological processes causing post-treatment lesion development is understudied. We analyzed patients from the MR CLEAN-NO IV trial with good quality 24-hour and 1-week follow-up Non-Contrast CT scans. We delineated hypo- and hyper-dense regions on the scans as lesion. We performed univariable logistic and linear regression to estimate the influence of IVT on the presence (growth > 0 ml) and extent of late lesion growth. The association between late lesion growth and mRS was assessed using ordinal logistic regression. Interaction analysis was performed to evaluate the influence of IVT on this association. Of the 63/116 were randomized to included patients, IVT. Median growth was 8.4(-0.88-26) ml. IVT was not significantly associated with the presence (OR: 1.24 (0.57-2.74, p = 0.59) or extent (β = 5.1(-8.8-19), p = 0.47) of growth. Late lesion growth was associated with worse clinical outcome (aOR: 0.85(0.76-0.95), p < 0.01; per 10 ml). IVT did not influence this association (p = 0.18). We did not find evidence that IVT influences late lesion growth or the relationship between growth and worse clinical outcome. Therapies to reduce lesion development are necessary.

Perfusion Collateral Index versus Hypoperfusion Intensity Ratio in Assessment of Collaterals in Patients with Acute Ischemic Stroke

BACKGROUND AND PURPOSE

Perfusion-based collateral indices such as the perfusion collateral index and the hypoperfusion intensity ratio have shown promise in the assessment of collaterals in patients with acute ischemic stroke. We aimed to compare the diagnostic performance of the perfusion collateral index and the hypoperfusion intensity ratio in collateral assessment compared with angiographic collaterals and outcome measures, including final infarct volume, infarct growth, and functional independence.

MATERIALS AND METHODS

Consecutive patients with acute ischemic stroke with anterior circulation proximal arterial occlusion who underwent endovascular thrombectomy and had pre- and posttreatment MRI were included. Using pretreatment MR perfusion, we calculated the perfusion collateral index and the hypoperfusion intensity ratio for each patient. The angiographic collaterals obtained from DSA were dichotomized to sufficient (American Society of Interventional and Therapeutic Neuroradiology [ASITN] scale 3-4) versus insufficient (ASITN scale 0-2). The association of collateral status determined by the perfusion collateral index and the hypoperfusion intensity ratio was assessed against angiographic collaterals and outcome measures.

RESULTS

A total of 98 patients met the inclusion criteria. Perfusion collateral index values were significantly higher in patients with sufficient angiographic collaterals (P < .001), while there was no significant (P = .46) difference in hypoperfusion intensity ratio values. Among patients with good (mRS 0-2) versus poor (mRS 3-6) functional outcome, the perfusion collateral index of ≥ 62 was present in 72% versus 31% (P = .003), while the hypoperfusion intensity ratio of ≤0.4 was present in 69% versus 56% (P = .52). The perfusion collateral index and the hypoperfusion intensity ratio were both significantly predictive of final infarct volume, but only the perfusion collateral index was significantly (P = .03) associated with infarct growth.

CONCLUSIONS

Results show that the perfusion collateral index outperforms the hypoperfusion intensity ratio in the assessment of collateral status, infarct growth, and determination of functional outcomes.

Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning

(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar's stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors.

Risk factors of late lesion growth after acute ischemic stroke treatment

Background

Even days after treatment of acute ischemic stroke due to a large vessel occlusion, the infarct lesion continues to grow. This late, subacute growth is associated with unfavorable functional outcome. In this study, we aim to identify patient characteristics that are risk factors of late, subacute lesion growth.

Methods

Patients from the MR CLEAN trial cohort with good quality 24 h and 1-week follow up non-contrast CT scans were included. Late Lesion growth was defined as the difference between the ischemic lesion volume assessed after 1-week and 24-h. To identify risk factors, patient characteristics associated with lesion growth (categorized in quartiles) in univariable ordinal analysis (p < 0.1) were included in a multivariable ordinal regression model.

Results

In the 226 patients that were included, the median lesion growth was 22 (IQR 10–45) ml. In the multivariable model, lower collateral capacity [aOR: 0.62 (95% CI: 0.44–0.87); p = 0.01], longer time to treatment [aOR: 1.04 (1–1.08); p = 0.04], unsuccessful recanalization [aOR: 0.57 (95% CI: 0.34–0.97); p = 0.04], and larger midline shift [aOR: 1.18 (95% CI: 1.02–1.36); p = 0.02] were associated with late lesion growth.

Conclusion

Late, subacute, lesion growth occurring between 1 day and 1 week after ischemic stroke treatment is influenced by lower collateral capacity, longer time to treatment, unsuccessful recanalization, and larger midline shift. Notably, these risk factors are similar to the risk factors of acute lesion growth, suggesting that understanding and minimizing the effects of the predictors for late lesion growth could be beneficial to mitigate the effects of ischemia.

Posttreatment Ischemic Lesion Evolution Is Associated With Reduced Favorable Functional Outcome in Patients With Stroke

BACKGROUND AND PURPOSE

Ischemic lesion volume can increase even 24 hours after onset of an acute ischemic stroke. In this study, we investigated the association of lesion evolution with functional outcome and the influence of successful recanalization on this association.

METHODS

We included patients from the MR CLEAN trial (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) who received good quality noncontrast CT images 24 hours and 1 week after stroke onset. The ischemic lesion delineations included infarct, edema, and hemorrhagic transformation. Lesion evolution was defined as the difference between the volumes measured on the 1-week and 24-hour noncontrast CTs. The association of lesion evolution with functional outcome was evaluated using unadjusted and adjusted logistic regression. Adjustments were made for baseline, clinical, and imaging parameters that were associated P<0.10) in univariate analysis with favorable functional outcome, defined as modified Rankin Scale score of ≤2. Interaction analysis was performed to evaluate the influence of successful recanalization, defined as modified Arterial Occlusion Lesion score of 3 points, on this association.

RESULTS

Of the 226 patients who were included, 69 (31%) patients achieved the favorable functional outcome. Median lesion evolution was 22 (interquartile range, 10-45) mL. Lesion evolution was significantly inversely correlated with favourable functional outcome: unadjusted odds ratio, 0.76 (95% CI, 0.66-0.86; per 10 mL of lesion evolution; P<0.01) and adjusted odds ratio: 0.85 (95% CI, 0.72-0.97; per 10 mL of lesion evolution; P=0.03). There was no significant interaction of successful recanalization on the association of lesion evolution and favorable functional outcome (odds ratio, 1.01 [95% CI, 0.77-1.36]; P=0.94).

CONCLUSIONS

In our population, subacute ischemic lesion evolution is associated with unfavorable functional outcome. This study suggests that even 24 hours after onset of stroke, deterioration of the brain continues, which has a negative effect on functional outcome. This finding may warrant additional treatment in the subacute phase.

Baseline ASPECTS and hypoperfusion intensity ratio influence the impact of first pass reperfusion on functional outcomes

BACKGROUND

First pass reperfusion (FPR) has been established as a key performance metric in mechanical thrombectomy (MT). The impact of FPR may be more relevant in fast progressors. We aim to study the impact of baseline Alberta Stroke Program Early CT Score (ASPECTS) on non-contrast CT and hypoperfusion intensity ratio (HIR) on CT perfusion on clinical outcomes after FPR.

METHODS

A prospective MT database was reviewed for patients with isolated occlusion of the intracranial internal carotid artery and/or middle cerebral artery M1 segment who underwent MT with complete reperfusion (modified Thrombolyis in Cerebral Infarction score 2c-3) from January 2012 to May 2019. The overall population was divided into ASPECTS >7 versus ≤7 and the subgroup of patients with baseline CT perfusion was divided into HIR <0.3 versus ≥0.3. Univariable and multivariable analyses were performed to establish the predictors of 90-day functional independence (modified Rankin Scale (mRS) ≤2) in each subgroup.

RESULTS

A total of 436 patients were included in the analyses. FPR was achieved in 254 (58.3%) patients. ASPECTS modified the effect of FPR on clinical outcomes, with FPR predicting good outcomes in patients with ASPECTS ≤7 (46% vs 29%, adjusted OR 3.748; 95% CI 1.590 to 8.838, p=0.003) while no significant effect was detected in those with ASPECTS >7 (62.3% vs 53.1%, adjusted OR 1.372; 95% CI 0.798 to 2.358, p=0.25). Similarly, FPR predicted good outcomes in patients with HIR ≥0.3 (54.8% vs 41.9%, adjusted OR 2.204; 95% CI 1.148 to 4.233, p=0.01) but not in those with HIR <0.3 (62.9% vs 52.8%, adjusted OR 1.524; 95% CI 0.592 to 3.920, p=0.38).

CONCLUSIONS

The impact of FPR on functional outcomes is highly dependent on baseline imaging characteristics, with a more prominent influence in patients presenting with lower ASPECTS and/or higher HIR.