A Predictive Model for Functional Outcome in Patients with Acute Ischemic Stroke Undergoing Endovascular Thrombectomy

An exploratory study of longitudinal trajectory of language, swallowing and cognition post endovascular clot retrieval

BACKGROUND

Endovascular clot retrieval (ECR) is known to reduce global disability at 3 months post stroke however limited research exists regarding the trajectory of specific clinical impairments including language, swallowing and cognitive deficits between onset and 3 months.

AIMS

To assess language, swallowing, and cognitive performance following ECR and explore whether impairment severity is correlated with modified Thrombolysis in Cerebral Infarction score (mTICI), stroke severity or quality of life (QoL).

METHODS

Assessment was completed within 7 days (T1), 1 month (T2) and 3 months (T3) post-stroke. Performance was measured with the Functional Oral Intake Scale (FOIS), Repeatable Battery for Assessment of Neuropsychological Status (RBANS), Trail Making Test (TMT A and B) and Brixton Spatial Awareness Test. The Western Aphasia Battery (WAB) was used for left hemisphere stroke. QoL was measured with the Stroke and Aphasia Quality of Life Scale.

RESULTS

Twenty-five participants (median 72 years; 64% male) were prospectively recruited following ECR. High reperfusion success (68% mTICI 3) and low stroke severity post ECR (median 24 h NIHSS = 3, IQR 7-18) were noted. At T1, 10 participants presented with aphasia, eight required a modified diet and 20 had impaired cognition. At T3 all had recovered to a normal oral diet, 39% had persistent cognitive impairment and 45% of patients with left hemisphere stroke remained aphasic. Performance on the WAB, FOIS, RBANS and TMT changed significantly over time (all p < 0.05). The severity score at T1 for all measures, excluding TMT B and Brixton, was significantly correlated with 24 h NIHSS. WAB scores at T3 were correlated with QoL (r = 0.618; p = 0.043).

CONCLUSION

This exploratory study found the longitudinal performance of language, swallowing and cognition significantly improved over time and severity in the first-week post-ECR was correlated with 24 h NIHSS rather than the degree of reperfusion.

WHAT THIS PAPER ADDS

What is already known on the subject Randomised control trials have demonstrated the benefit of ECR in patients with ischemic stroke using global measures of disability and function. Limited research exists regarding the trajectory of specific clinical impairments including language, swallowing and cognitive deficits. There is also a reliance on screening assessments and a lack of consideration of the influence of co-occurring impairments. What this paper adds to existing knowledge This prospective study is amongst the first to explore the longitudinal trajectory of language, swallowing and cognitive impairment using a standardised assessment battery. Twenty-four-hour NIHSS was significantly correlated with language, swallowing, global cognition and some measures of executive function. Language performance post ECR was correlated with domain-specific cognitive assessment of attention, immediate memory and delayed memory, which differed from swallowing performance post ECR that correlated with measures of executive function. What are the potential or actual clinical implications of this work? It is important for speech-language pathologists and the wider medical team to monitor language, swallowing and cognitive performance post ECR regardless of treatment success. Stroke severity at 24 h post-ECR influences the severity of language, swallowing and cognitive impairments.

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.

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.

Development and Validation of a Postprocedural Model to Predict Outcome After Endovascular Treatment for Ischemic Stroke

Importance

Outcome prediction after endovascular treatment (EVT) for ischemic stroke is important to patients, family members, and physicians.

Objective

To develop and validate a model based on preprocedural and postprocedural characteristics to predict functional outcome for individual patients after EVT.

Design, Setting, and Participants

A prediction model was developed using individual patient data from 7 randomized clinical trials, performed between December 2010 and December 2014. The model was developed within the Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials (HERMES) collaboration and external validation in data from the Dutch Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry of patients treated in clinical practice between March 2014 and November 2017. Participants included patients from multiple centers throughout different countries in Europe, North America, East Asia, and Oceania (derivation cohort), and multiple centers in the Netherlands (validation cohort). Included were adult patients with a history of ischemic stroke from an intracranial large vessel occlusion in the anterior circulation who underwent EVT within 12 hours of symptom onset or last seen well. Data were last analyzed in July 2022.

Main Outcome(s) and Measure(s)

A total of 19 variables were assessed by multivariable ordinal regression to predict functional outcome (modified Rankin Scale [mRS] score) 90 days after EVT. Variables were routinely available 1 day after EVT. Akaike information criterion (AIC) was used to optimize model fit vs model complexity. Probabilities for functional independence (mRS 0-2) and survival (mRS 0-5) were derived from the ordinal model. Model performance was expressed with discrimination (C statistic) and calibration.

Results

A total of 781 patients (median [IQR] age, 67 [57-76] years; 414 men [53%]) constituted the derivation cohort, and 3260 patients (median [IQR] age, 72 [61-80] years; 1684 men [52%]) composed the validation cohort. Nine variables were included in the model: age, baseline National Institutes of Health Stroke Scale (NIHSS) score, prestroke mRS score, history of diabetes, occlusion location, collateral score, reperfusion grade, NIHSS score at 24 hours, and symptomatic intracranial hemorrhage 24 hours after EVT. External validation in the MR CLEAN Registry showed excellent discriminative ability for functional independence (C statistic, 0.91; 95% CI, 0.90-0.92) and survival (0.89; 95% CI, 0.88-0.90). The proportion of functional independence in the MR CLEAN Registry was systematically higher than predicted by the model (41% vs 34%), whereas observed and predicted survival were similar (72% vs 75%). The model was updated and implemented for clinical use.

Conclusion and relevance

The prognostic tool MR PREDICTS@24H can be applied 1 day after EVT to accurately predict functional outcome for individual patients at 90 days and to provide reliable outcome expectations and personalize follow-up and rehabilitation plans. It will need further validation and updating for contemporary patients.

Association of clinical factors to functional outcomes in patients with stroke with large-vessel occlusion after endovascular thrombectomy

BACKGROUND/PURPOSE

Multiple clinical factors have been reported to be associated with functional outcomes in patients with stroke. However, little is known about prognostic predictors of functional independence in patients with stroke undergoing endovascular thrombectomy (EVT). Our study aimed to investigate the correlation between multiple prognostic variables (including EVT and rehabilitation-related parameters) and functional outcomes in patients post-EVT.

METHODS

This retrospective cohort study recruited patients hospitalized between December 2018 and March 2022. Patients with stroke with large-vessel occlusion who underwent EVT were eligible for inclusion in the study. Prognostic factors, including premorbid characteristics, laboratory data, EVT- and rehabilitation-related parameters, functional activity level, balance ability, swallowing, and sphincter function, were collected. Logistic regression and generalized linear models were used to analyze their correlations with functional outcomes.

RESULTS

A total of 148 patients were included. In the univariate logistic regression analysis, younger age, premorbid functional independence, higher hemoglobin (Hb) level, lower National Institute of Health Stroke Scale (NIHSS) score, absence of hemorrhagic transformation in 14 days, no nasogastric (NG) tube placement, earlier rehabilitation, frequent daily rehabilitation sessions, more out-of-bed rehabilitation, better ability of sitting up, better initial sitting balance, higher Barthel index (BI), absence of immobility, and neurological complications were associated with favorable outcomes at 3 months. In the stepwise regression model, the predictors of favorable function at 3 months included age, ability to sit up, and frequency of daily rehabilitation sessions; favorable outcomes at 6 months were associated with age, ability to sit up, and swallowing function.

CONCLUSION

In patients with stroke post-EVT, better functional outcomes were associated with prognostic variables, including younger age, better ability to sit up, normal swallowing function, and frequent daily rehabilitation sessions.