Ordinal Prediction Model of 90-Day Modified Rankin Scale in Ischemic Stroke

Predictors of functional outcome after stroke: Sex differences in older individuals

BACKGROUND

Sex differences in stroke are well documented, with females being older at onset, with more severe strokes and worse outcomes than males. Females receive less comprehensive stroke unit treatment. Similarly, older individuals receive poorer quality care than younger ones. There is limited research on sex differences in factors that impact 3-month poststroke functional outcome in people older than 80 years.

METHODS

This register-based and cross-sectional study analyzed data from two stroke quality registers in Sweden from 2014 through 2019. The study included patients aged ≥80 with a diagnosis of ischemic or hemorrhagic stroke. Sociodemographic features, prestroke condition, stroke severity on admission (National Institutes of Health Stroke Scale [NIHSS]), stroke unit care, rehabilitation plans, and 3-month poststroke functional outcome measured with the modified Rankin Scale were analyzed. Ordinal regression analyses stratified by sex were conducted to assess sex differences in factors that impact poststroke functional outcome 3 months after the stroke.

RESULTS

A total of 2245 patients were studied with the majority (59.2%) being females. Females experienced more severe strokes (NIHSS median 4 vs. 3, p = 0.01) and were older at stroke onset than males (87.0 vs. 85.4, p < 0.001). Females were also less independent prestroke (69.9% vs. 77.4%, p < 0.001) and a higher proportion of females lived alone (78.2% vs. 44.2%, p < 0.001). Males received intravenous thrombolysis more often than females (16.3% vs. 12.0%, p = 0.005). Regarding 3-month functional outcome, males benefited more from thrombolysis (odds ratio [OR] 0.52, 95% confidence interval [CI] 0.30-0.83), whereas females benefited more from thrombectomy (OR 0.40, 95% CI 0.20-0.71).

CONCLUSION

Stroke care should be adapted to sex disparities in older individuals, while clinicians should be aware of these sex disparities. Further research could clarify the mechanisms behind these disparities and lead to a more personalized approach to stroke care of the older population.

Pre-morbid sleep disturbance and its association with stroke severity: results from the international INTERSTROKE study

BACKGROUND AND PURPOSE

Whilst sleep disturbances are associated with stroke, their association with stroke severity is less certain. In the INTERSTROKE study, the association of pre-morbid sleep disturbance with stroke severity and functional outcome following stroke was evaluated.

METHODS

INTERSTROKE is an international case-control study of first acute stroke. This analysis included cases who completed a standardized questionnaire concerning nine symptoms of sleep disturbance (sleep onset latency, duration, quality, nocturnal awakening, napping duration, whether a nap was planned, snoring, snorting and breathing cessation) in the month prior to stroke (n = 2361). Two indices were derived representing sleep disturbance (range 0-9) and obstructive sleep apnoea (range 0-3) symptoms. Logistic regression was used to estimate the magnitude of association between symptoms and stroke severity defined by the modified Rankin Score.

RESULTS

The mean age of participants was 62.9 years, and 42% were female. On multivariable analysis, there was a graded association between increasing number of sleep disturbance symptoms and initially severe stroke (2-3, odds ratio [OR] 1.44, 95% confidence interval [CI] 1.07-1.94; 4-5, OR 1.66, 95% CI 1.23-2.25; >5, OR 2.58, 95% CI 1.83-3.66). Having >5 sleep disturbance symptoms was associated with significantly increased odds of functional deterioration at 1 month (OR 1.54, 95% CI 1.01-2.34). A higher obstructive sleep apnoea score was also associated with significantly increased odds of initially severe stroke (2-3, OR 1.48; 95% CI 1.20-1.83) but not functional deterioration at 1 month (OR 1.19, 95% CI 0.93-1.52).

CONCLUSIONS

Sleep disturbance symptoms were common and associated with an increased odds of severe stroke and functional deterioration. Interventions to modify sleep disturbance may help prevent disabling stroke/improve functional outcomes and should be the subject of future research.

Artificial intelligence to predict individualized outcome of acute ischemic stroke patients: The SIBILLA project

INTRODUCTION

Formulating reliable prognosis for ischemic stroke patients remains a challenging task. We aimed to develop an artificial intelligence model able to formulate in the first 24 h after stroke an individualized prognosis in terms of NIHSS.

PATIENTS AND METHODS

Seven hundred ninety four acute ischemic stroke patients were divided into a training (597) and testing (197) cohort. Clinical and instrumental data were collected in the first 24 h. We evaluated the performance of four machine-learning models (Random Forest, K-Nearest Neighbors, Support Vector Machine, XGBoost) in predicting NIHSS at discharge both in terms of variation between discharge and admission (regressor approach) and in terms of severity class namely NIHSS 0-5, 6-10, 11-20, >20 (classifier approach). We used Shapley Additive exPlanations values to weight features impact on predictions.

RESULTS

XGBoost emerged as the best performing model. The classifier and regressor approaches perform similarly in terms of accuracy (80% vs 75%) and f1-score (79% vs 77%) respectively. However, the regressor has higher precision (85% vs 68%) in predicting prognosis of very severe stroke patients (NIHSS > 20). NIHSS at admission and 24 hours, GCS at 24 hours, heart rate, acute ischemic lesion on CT-scan and TICI score were the most impacting features on the prediction.

DISCUSSION

Our approach, which employs an artificial intelligence based-tool, inherently able to continuously learn and improve its performance, could improve care pathway and support stroke physicians in the communication with patients and caregivers.

CONCLUSION

XGBoost reliably predicts individualized outcome in terms of NIHSS at discharge in the first 24 hours after stroke.

Using 30-day modified rankin scale score to predict 90-day score in patients with intracranial hemorrhage: Derivation and validation of prediction model

Whether 30-day modified Rankin Scale (mRS) scores can predict 90-day scores is unclear. This study derived and validated a model to predict ordinal 90-day mRS score in an intracerebral hemorrhage (ICH) population using 30-day mRS values and routinely available baseline variables. Adults enrolled in the Antihypertensive Treatment of Acute Cerebral Hemorrhage-2 (ATACH-2) trial between May 2011 and September 2015 with acute ICH, who were alive at 30 days and had mRS scores reported at both 30 and 90 days were included in this post-hoc analysis. A proportional odds regression model for predicting ordinal 90-day mRS scores was developed and internally validated using bootstrapping. Variables in the model included: mRS score at 30 days, age (years), hematoma volume (cm3), hematoma location (deep [basal ganglia, thalamus], lobar, or infratentorial), presence of intraventricular hemorrhage (IVH), baseline Glasgow Coma Scale (GCS) score, and National Institutes of Health Stroke Scale (NIHSS) score at randomization. We assessed model fit, calibration, discrimination, and agreement (ordinal, dichotomized functional independence), and EuroQol-5D ([EQ-5D] utility weighted) between predicted and observed 90-day mRS. A total of 898/1000 participants were included. Following bootstrap internal validation, our model (calibration slope = 0.967) had an optimism-corrected c-index of 0.884 (95% CI = 0.873-0.896) and R2 = 0.712 for 90-day mRS score. The weighted ĸ for agreement between observed and predicted ordinal 90-day mRS score was 0.811 (95% CI = 0.787-0.834). Agreement between observed and predicted functional independence (mRS score of 0-2) at 90 days was 74.3% (95% CI = 69.9-78.7%). The mean ± SD absolute difference between predicted and observed EQ-5D-weighted mRS score was negligible (0.005 ± 0.145). This tool allows practitioners and researchers to utilize clinically available information along with the mRS score 30 days after ICH to reliably predict the mRS score at 90 days.

A Clinical and Imaging Fused Deep Learning Model Matches Expert Clinician Prediction of 90-Day Stroke Outcomes

BACKGROUND AND PURPOSE

Predicting long-term clinical outcome in acute ischemic stroke is beneficial for prognosis, clinical trial design, resource management, and patient expectations. This study used a deep learning-based predictive model (DLPD) to predict 90-day mRS outcomes and compared its predictions with those made by physicians.

MATERIALS AND METHODS

A previously developed DLPD that incorporated DWI and clinical data from the acute period was used to predict 90-day mRS outcomes in 80 consecutive patients with acute ischemic stroke from a single-center registry. We assessed the predictions of the model alongside those of 5 physicians (2 stroke neurologists and 3 neuroradiologists provided with the same imaging and clinical information). The primary analysis was the agreement between the ordinal mRS predictions of the model or physician and the ground truth using the Gwet Agreement Coefficient. We also evaluated the ability to identify unfavorable outcomes (mRS >2) using the area under the curve, sensitivity, and specificity. Noninferiority analyses were undertaken using limits of 0.1 for the Gwet Agreement Coefficient and 0.05 for the area under the curve analysis. The accuracy of prediction was also assessed using the mean absolute error for prediction, percentage of predictions ±1 categories away from the ground truth (±1 accuracy [ACC]), and percentage of exact predictions (ACC).

RESULTS

To predict the specific mRS score, the DLPD yielded a Gwet Agreement Coefficient score of 0.79 (95% CI, 0.71-0.86), surpassing the physicians' score of 0.76 (95% CI, 0.67-0.84), and was noninferior to the readers (P < .001). For identifying unfavorable outcome, the model achieved an area under the curve of 0.81 (95% CI, 0.72-0.89), again noninferior to the readers' area under the curve of 0.79 (95% CI, 0.69-0.87) (P < .005). The mean absolute error, ±1ACC, and ACC were 0.89, 81%, and 36% for the DLPD.

CONCLUSIONS

A deep learning method using acute clinical and imaging data for long-term functional outcome prediction in patients with acute ischemic stroke, the DLPD, was noninferior to that of clinical readers.

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.

Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model

BACKGROUND

Predicting long-term clinical outcome based on the early acute ischemic stroke information is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about which imaging features to assess and may require time-consuming postprocessing. This study's goal was to predict ordinal 90-day modified Rankin Scale (mRS) score in acute ischemic stroke patients by fusing a Deep Learning model of diffusion-weighted imaging images and clinical information from the acute period.

METHODS

A total of 640 acute ischemic stroke patients who underwent magnetic resonance imaging within 1 to 7 days poststroke and had 90-day mRS follow-up data were randomly divided into 70% (n=448) for model training, 15% (n=96) for validation, and 15% (n=96) for internal testing. Additionally, external testing on a cohort from Lausanne University Hospital (n=280) was performed to further evaluate model generalization. Accuracy for ordinal mRS, accuracy within ±1 mRS category, mean absolute prediction error, and determination of unfavorable outcome (mRS score >2) were evaluated for clinical only, imaging only, and 2 fused clinical-imaging models.

RESULTS

The fused models demonstrated superior performance in predicting ordinal mRS score and unfavorable outcome in both internal and external test cohorts when compared with the clinical and imaging models. For the internal test cohort, the top fused model had the highest area under the curve of 0.92 for unfavorable outcome prediction and the lowest mean absolute error (0.96 [95% CI, 0.77-1.16]), with the highest proportion of mRS score predictions within ±1 category (79% [95% CI, 71%-88%]). On the external Lausanne University Hospital cohort, the best fused model had an area under the curve of 0.90 for unfavorable outcome prediction and outperformed other models with an mean absolute error of 0.90 (95% CI, 0.79-1.01), and the highest percentage of mRS score predictions within ±1 category (83% [95% CI, 78%-87%]).

CONCLUSIONS

A Deep Learning-based imaging model fused with clinical variables can be used to predict 90-day stroke outcome with reduced subjectivity and user burden.

Prognostic Value of Combined Radiomic Features from Follow-Up DWI and T2-FLAIR in Acute Ischemic Stroke

The biological pathways involved in lesion formation after an acute ischemic stroke (AIS) are poorly understood. Despite successful reperfusion treatment, up to two thirds of patients with large vessel occlusion remain functionally dependent. Imaging characteristics extracted from DWI and T2-FLAIR follow-up MR sequences could aid in providing a better understanding of the lesion constituents. We built a fully automated pipeline based on a tree ensemble machine learning model to predict poor long-term functional outcome in patients from the MR CLEAN-NO IV trial. Several feature sets were compared, considering only imaging, only clinical, or both types of features. Nested cross-validation with grid search and a feature selection procedure based on SHapley Additive exPlanations (SHAP) was used to train and validate the models. Considering features from both imaging modalities in combination with clinical characteristics led to the best prognostic model (AUC = 0.85, 95%CI [0.81, 0.89]). Moreover, SHAP values showed that imaging features from both sequences have a relevant impact on the final classification, with texture heterogeneity being the most predictive imaging biomarker. This study suggests the prognostic value of both DWI and T2-FLAIR follow-up sequences for AIS patients. If combined with clinical characteristics, they could lead to better understanding of lesion pathophysiology and improved long-term functional outcome prediction.

National Institutes of Health Stroke Scale Score Less Than 10 at 24 hours After Stroke Onset Is a Strong Predictor of a Favorable Outcome After Mechanical Thrombectomy

BACKGROUND

There are a few accurate predictors of patient outcomes after mechanical thrombectomy (MT).

OBJECTIVE

To investigate whether the National Institutes of Health Stroke Scale (NIHSS) score 24 hours after stroke onset could predict favorable outcomes at 90 days in patients with acute stroke treated with MT.

METHODS

Patients from the SKIP study were enrolled in this study. Using receiver operating characteristic curves, the optimal cut-off NIHSS score 24 hours after stroke onset was calculated to distinguish between favorable (modified Rankin Scale score 0-2) and unfavorable (modified Rankin Scale score 3-6) outcomes at 90 days. These receiver operating characteristic curves were compared with those of previously reported predictors of favorable outcomes, such as the ΔNIHSS score (baseline NIHSS score-NIHSS score at 24 h), percent delta (ΔNIHSS score × 100/baseline NIHSS score), and early neurological improvement indices.

RESULTS

A total of 177 patients (median age, 72 years; female, 65 [37%]) were enrolled, and 109 (61.9%) had favorable outcomes. The respective sensitivity, specificity, and area under the curve values for an NIHSS of 10 were 92.6%, 80.7%, and .906; a ΔNIHSS score of 7 were 70.6%, 76.1%, and .797; and percent delta of 48.3% were 85.3%, 80.7%, and .890.

CONCLUSION

NIHSS score <10 at 24 hours after stroke onset is a strong predictor of favorable outcomes at 90 days in patients treated with MT.