Thinking About the Future: A Review of Prognostic Scales Used in Acute Stroke

Stroke Severity, Caregiver Feedback, and Cognition in the REGARDS-CARES Study

BACKGROUND

Cognitive impairment after stroke is common and is present in up to 60% of survivors. Stroke severity, indicated by both volume and location, is the most consequential predictor of cognitive impairment, with severe strokes predicting higher chances of cognitive impairment. The current investigation examines the associations of 2 stroke severity ratings and a caregiver-report of poststroke functioning with longitudinal cognitive outcomes.

METHODS AND RESULTS

One hundred fifty-seven caregivers and stroke survivor dyads participated in the CARES (Caring for Adults Recovering From the Effects of Stroke) project, an ancillary study of the REGARDS (Reasons for Geographic and Racial Differences in Stroke) national cohort study. The Glasgow Outcome Scale and modified Rankin Scale scores collected at hospitalization discharge were included as 2 primary predictors of cognitive impairment. The number of caregiver-reported problems and impairments at 9 months following stroke were included as a third predictor. Cognition was measured using a biennial telephone battery and included the domains of learning, memory, and executive functioning. Multiple cognitive assessments were analyzed up to 5 years poststroke, controlling for prestroke cognition and demographic variables of the stroke survivor. Separate mixed models showed significant main effects of the Glasgow Outcome Scale (b=0.3380 [95% CI, 0.14-0.5]; P=0.0009), modified Rankin Scale (b=-0.2119 [95% CI, -0.32 to -0.10]; P=0.0002), and caregiver-reported problems (b=-0.0671 [95% CI, -0.09 to -0.04]; P<0.0001) on longitudinal cognitive scores. In a combined model including all 3 predictors, only caregiver-reported problems significantly predicted cognition (b=-0.0480 [95% CI, -0.08 to -0.03]; P<0.0001).

CONCLUSIONS

These findings emphasize the importance of caregiver feedback in predicting cognitive consequences of stroke.

Predicting ischemic stroke patients' prognosis changes using machine learning in a nationwide stroke registry

Accurately predicting the prognosis of ischemic stroke patients after discharge is crucial for physicians to plan for long-term health care. Although previous studies have demonstrated that machine learning (ML) shows reasonably accurate stroke outcome predictions with limited datasets, to identify specific clinical features associated with prognosis changes after stroke that could aid physicians and patients in devising improved recovery care plans have been challenging. This study aimed to overcome these gaps by utilizing a large national stroke registry database to assess various prediction models that estimate how patients' prognosis changes over time with associated clinical factors. To properly evaluate the best predictive approaches currently available and avoid prejudice, this study employed three different prognosis prediction models including a statistical logistic regression model, commonly used clinical-based scores, and a latest high-performance ML-based XGBoost model. The study revealed that the XGBoost model outperformed other two traditional models, achieving an AUROC of 0.929 in predicting the prognosis changes of stroke patients followed for 3 months. In addition, the XGBoost model maintained remarkably high precision even when using only selected 20 most relevant clinical features compared to full clinical datasets used in the study. These selected features closely correlated with significant changes in clinical outcomes for stroke patients and showed to be effective for predicting prognosis changes after discharge, allowing physicians to make optimal decisions regarding their patients' recovery.

Machine learning decision support model for discharge planning in stroke patients

BACKGROUND/AIM

Efficient discharge for stroke patients is crucial but challenging. The study aimed to develop early predictive models to explore which patient characteristics and variables significantly influence the discharge planning of patients, based on the data available within 24 h of admission.

DESIGN

Prospective observational study.

METHODS

A prospective cohort was conducted at a university hospital with 523 patients hospitalised for stroke. We built and trained six different machine learning (ML) models, followed by testing and tuning those models to find the best-suited predictor for discharge disposition, dichotomized into home and non-home. To evaluate the accuracy, reliability and interpretability of the best-performing models, we identified and analysed the features that had the greatest impact on the predictions.

RESULTS

In total, 523 patients met the inclusion criteria, with a mean age of 61 years. Of the patients with stroke, 30.01% had non-home discharge. Our model predicting non-home discharge achieved an area under the receiver operating characteristic curve of 0.95 and a precision of 0.776. After threshold was moved, the model had a recall of 0.809. Top 10 variables by importance were National Institutes of Health Stroke Scale (NIHSS) score, family income, Barthel index (BI) score, FRAIL score, fall risk, pressure injury risk, feeding method, depression, age and dysphagia.

CONCLUSION

The ML model identified higher NIHSS, BI, and FRAIL, family income, higher fall risk, pressure injury risk, older age, tube feeding, depression and dysphagia as the top 10 strongest risk predictors in identifying patients who required non-home discharge to higher levels of care. Modern ML techniques can support timely and appropriate clinical decision-making.

RELEVANCE TO CLINICAL PRACTICE

This study illustrates the characteristics and risk factors of non-home discharge in patients with stroke, potentially contributing to the improvement of the discharge process.

REPORTING METHOD

STROBE guidelines.

Machine learning is an effective method to predict the 3-month prognosis of patients with acute ischemic stroke

Background and objectives

Upwards of 50% of acute ischemic stroke (AIS) survivors endure varying degrees of disability, with a recurrence rate of 17.7%. Thus, the prediction of outcomes in AIS may be useful for treatment decisions. This study aimed to determine the applicability of a machine learning approach for forecasting early outcomes in AIS patients.

Methods

A total of 659 patients with new-onset AIS admitted to the Department of Neurology of both the First and Second Affiliated Hospitals of Bengbu Medical University from January 2020 to October 2022 included in the study. The patient' demographic information, medical history, Trial of Org 10,172 in Acute Stroke Treatment (TOAST), National Institute of Health Stroke Scale (NIHSS) and laboratory indicators at 24 h of admission data were collected. The Modified Rankine Scale (mRS) was used to assess the 3-mouth outcome of participants' prognosis. We constructed nine machine learning models based on 18 parameters and compared their accuracies for outcome variables.

Results

Feature selection through the Least Absolute Shrinkage and Selection Operator cross-validation (Lasso CV) method identified the most critical predictors for early prognosis in AIS patients as white blood cell (WBC), homocysteine (HCY), D-Dimer, baseline NIHSS, fibrinogen degradation product (FDP), and glucose (GLU). Among the nine machine learning models evaluated, the Random Forest model exhibited superior performance in the test set, achieving an Area Under the Curve (AUC) of 0.852, an accuracy rate of 0.818, a sensitivity of 0.654, a specificity of 0.945, and a recall rate of 0.900.

Conclusion

These findings indicate that RF models utilizing general clinical and laboratory data from the initial 24 h of admission can effectively predict the early prognosis of AIS patients.

Derivation and Validation of a Scoring System for Predicting Poor Outcome After Posterior Circulation Ischemic Stroke in China

BACKGROUND AND OBJECTIVES

Guidelines for posterior circulation ischemic stroke (PCIS) treatment are lacking and outcome prediction is crucial for patients and clinicians. We aimed to develop and validate a prognostic score to predict the poor outcome for patients with PCIS.

METHODS

The score was developed from a prospective derivation cohort named the Third China National Stroke Registry (August 2015-March 2018) and validated in a spatiotemporal independent validation cohort (December 2017-March 2023) in China. Patients with PCIS with acute infarctions defined as hyperintense lesions on diffusion-weighted imaging were included in this study. The poor outcome was measured as modified Rankin scale (mRS) score 3-6 at 3 months after PCIS. Multivariable logistic regression analysis was used to identify predictors for poor outcome. The prognostic score, namely PCIS Outcome Score (PCISOS), was developed by assigning points to variables based on their relative β-coefficients in the logistic model.

RESULTS

The PCISOS was derived from 3,294 patients (median age 62 [interquartile range (IQR) 55-70] years; 2,250 [68.3%] men) and validated in 501 patients (median age 61 [IQR 53-68] years; 404 [80.6%] men). Among them, 384 (11.7%) and 64 (12.8%) had poor outcome 3 months after stroke in respective cohorts. Age, mRS before admission, NIH Stroke Scale on admission, ischemic stroke history, infarction distribution, basilar artery, and posterior cerebral artery stenosis or occlusion were identified as independent predictors for poor outcome and included in PCISOS. This easy-to-use integer scoring system identified a marked risk gradient between 4 risk groups. PCISOS performed better than previous scores, with an excellent discrimination (C statistic) of 0.80 (95% CI 0.77-0.83) in the derivation cohort and 0.81 (95% CI 0.77-0.84) in the validation cohort. Calibration test showed high agreement between the predicted and observed outcomes in both cohorts.

DISCUSSION

PCISOS can be applied for patients with PCIS with acute infarctions to predict functional outcome at 3 months post-PCIS. This simple tool helps clinicians to identify patients with PCIS with higher risk of poor outcome and provides reliable outcome expectations for patients. This information might be used for personalized rehabilitation plan and patient selection for future clinical trials to reduce disability and mortality.

Magnetic resonance imaging-based deep learning imaging biomarker for predicting functional outcomes after acute ischemic stroke

PURPOSE

Clinical risk scores are essential for predicting outcomes in stroke patients. The advancements in deep learning (DL) techniques provide opportunities to develop prediction applications using magnetic resonance (MR) images. We aimed to develop an MR-based DL imaging biomarker for predicting outcomes in acute ischemic stroke (AIS) and evaluate its additional benefit to current risk scores.

METHOD

This study included 3338 AIS patients. We trained a DL model using deep neural network architectures on MR images and radiomics to predict poor functional outcomes at three months post-stroke. The DL model generated a DL score, which served as the DL imaging biomarker. We compared the predictive performance of this biomarker to five risk scores on a holdout test set. Additionally, we assessed whether incorporating the imaging biomarker into the risk scores improved the predictive performance.

RESULTS

The DL imaging biomarker achieved an area under the receiver operating characteristic curve (AUC) of 0.788. The AUCs of the five studied risk scores were 0.789, 0.793, 0.804, 0.810, and 0.826, respectively. The imaging biomarker's predictive performance was comparable to four of the risk scores but inferior to one (p = 0.038). Adding the imaging biomarker to the risk scores improved the AUCs (p-values) to 0.831 (0.003), 0.825 (0.001), 0.834 (0.003), 0.836 (0.003), and 0.839 (0.177), respectively. The net reclassification improvement and integrated discrimination improvement indices also showed significant improvements (all p < 0.001).

CONCLUSIONS

Using DL techniques to create an MR-based imaging biomarker is feasible and enhances the predictive ability of current risk scores.

Association of multimorbidity with mortality after stroke stratified by age, severity, etiology, and prior disability

BACKGROUND

Multimorbidity is common in patients with stroke and is associated with increased medium- to long-term mortality, but its value for clinical decision-making and case-mix adjustment will depend on other factors, such as age, stroke severity, etiological subtype, prior disability, and vascular risk factors.

AIMS

In the absence of previous studies, we related multimorbidity to long-term post-stroke mortality with stratification by these factors.

METHODS

In patients ascertained in a population-based stroke incidence study (Oxford Vascular Study; 2002-2017), we related pre-stroke multimorbidity (weighted/unweighted Charlson comorbidity index (CCI)) to all-cause/vascular/non-vascular mortality (1/5/10 years) using regression models adjusted/stratified by age, sex, predicted early outcome (THRIVE score), stroke severity (NIH stroke scale (NIHSS)), etiology (Trial of Org 10172 in Acute Stroke Treatment (TOAST)), premorbid disability (modified Rankin Scale (mRS)), and non-CCI risk factors (hypertension, hyperlipidemia, atrial fibrillation, smoking, deprivation, anxiety/depression).

RESULTS

Among 2454 stroke patients (M/SD age: 74.1/13.9 years; 48.9% male; M/SD NIHSS: 5.7/7.0), 1375/56.0% had ⩾ 1 CCI comorbidity and 685/27.9% had ⩾ 2. After age/sex adjustment, multimorbidity (unweighted CCI ⩾ 2 vs 0) predicted (all ps < 0.001) mortality at 1 year (aHR = 1.57, 95% CI = 1.38-1.78), 5 years (aHR = 1.73, 95% CI = 1.53-1.96), and 10 years (aHR = 1.79, 95% CI = 1.58-2.03). Although multimorbidity was independently associated with premorbid disability (mRS > 2: aOR = 2.76, 2.13-3.60) and non-CCI risk factors (hypertension: 1.56, 1.25-1.95; hyperlipidemia: 2.58, 2.03-3.28; atrial fibrillation: 2.31; 1.78-2.98; smoking: 1.37, 1.01-1.86), it predicted death after adjustment for all measured confounders (10-year-aHR = 1.56, 1.37-1.78, p < 0.001), driven mainly by non-vascular death (aHR = 1.89, 1.55-2.29). Predictive value for 10-year all-cause death was greatest in patients with lower expected early mortality: lower THRIVE score (pint < 0.001), age < 75 years (aHR = 2.27, 1.71-3.00), NIHSS < 5 (1.84, 1.53-2.21), and lacunar stroke (3.56, 2.14-5.91). Results were similar using the weighted CCI.

CONCLUSION

Pre-stroke multimorbidity is highly prevalent and is an independent predictor of death after stroke, supporting its inclusion in case-mix adjustment models and in informing decision-making by patients, families, and carers. Prediction in younger patients and after minor stroke, particularly for non-vascular death, suggests potential clinical utility in targeting interventions that require survival for 5-10 years to achieve a favorable risk/benefit ratio.

DATA ACCESS STATEMENT

Data requests will be considered by the Oxford Vascular Study (OXVASC) Study Director (P.M.R.-peter.rothwell@ndcn.ox.ac.uk).

Predicting 30-day mortality in intensive care unit patients with ischaemic stroke or intracerebral haemorrhage

BACKGROUND

Stroke patients admitted to an intensive care unit (ICU) follow a particular survival pattern with a high short-term mortality, but if they survive the first 30 days, a relatively favourable subsequent survival is observed.

OBJECTIVES

The development and validation of two prognostic models predicting 30-day mortality for ICU patients with ischaemic stroke and for ICU patients with intracerebral haemorrhage (ICH), analysed separately, based on parameters readily available within 24 h after ICU admission, and with comparison with the existing Acute Physiology and Chronic Health Evaluation IV (APACHE-IV) model.

DESIGN

Observational cohort study.

SETTING

All 85 ICUs participating in the Dutch National Intensive Care Evaluation database.

PATIENTS

All adult patients with ischaemic stroke or ICH admitted to these ICUs between 2010 and 2019.

MAIN OUTCOME MEASURES

Models were developed using logistic regressions and compared with the existing APACHE-IV model. Predictive performance was assessed using ROC curves, calibration plots and Brier scores.

RESULTS

We enrolled 14 303 patients with stroke admitted to ICU: 8422 with ischaemic stroke and 5881 with ICH. Thirty-day mortality was 27% in patients with ischaemic stroke and 41% in patients with ICH. Important factors predicting 30-day mortality in both ischaemic stroke and ICH were age, lowest Glasgow Coma Scale (GCS) score in the first 24 h, acute physiological disturbance (measured using the Acute Physiology Score) and the application of mechanical ventilation. Both prognostic models showed high discrimination with an AUC 0.85 [95% confidence interval (CI), 0.84 to 0.87] for patients with ischaemic stroke and 0.85 (0.83 to 0.86) in ICH. Calibration plots and Brier scores indicated an overall good fit and good predictive performance. The APACHE-IV model predicting 30-day mortality showed similar performance with an AUC of 0.86 (95% CI, 0.85 to 0.87) in ischaemic stroke and 0.87 (0.86 to 0.89) in ICH.

CONCLUSION

We developed and validated two prognostic models for patients with ischaemic stroke and ICH separately with a high discrimination and good calibration to predict 30-day mortality within 24 h after ICU admission.

TRIAL REGISTRATION

Trial registration: Dutch Trial Registry ( https://www.trialregister.nl/ ); identifier: NTR7438.

Shared decision making in older people after severe stroke

Stroke is a major cause of death and lifelong disability. Although stroke treatments have improved, many patients are left with life-changing deficits. Shared decision making and consent are fundamental to good medical practice. This is challenging because stroke often causes mental incapacity, prior views might not be known and prognosis early after stroke is often uncertain. There are no large trials of shared decision making after severe stroke, so we need to rely on observational data to inform practice. Core ethical principles of autonomy, beneficence, non-maleficence and justice must underpin our decision making. 'Surrogate' decision makers will need to be involved if a patient lacks capacity, and prior expressed views and values and beliefs need to be taken into account in decision making. Patients and surrogates often feel shocked at the sudden nature of stroke, and experience grief including anticipatory grief. Health care professionals need to acknowledge these feelings and provide support, be clear about what decisions need to be made and provide sufficient information about the stroke, and the risks and benefits of treatments being considered. Shared decision making can be emotionally difficult for health care professionals and so working in a supportive environment with compassionate leadership is important. Further research is needed to better understand the nature of grief and what sort of psychological support would be most helpful. Large randomised trials of shared decision making are also needed.

Aging-related genes revealed Neuroinflammatory mechanisms in ischemic stroke by bioinformatics

Ischemic stroke (IS) is a leading cause of disability, morbidity, and mortality globally. Aging affects immune function and contributes to poor outcomes of IS in elderly individuals. However, little is known about how aging-related genes (ARGs) are involved in IS. In this study, the relationship between ARGs and IS immune microenvironment biomarkers was explored by bioinformatics. Two IS microarray datasets (GSE22255, GSE16561) from human blood samples were analyzed and 502 ARGs were identified, from which 29 differentially expressed ARGs were selected. Functional analysis revealed that 7 of these ARGs (IL1B, FOS, JUN, CXCL5, PTGS2, TNFAIP3 and TLR4) were involved in five top enriched pathways (IL-17 signaling pathway, TNF signaling pathway, Rheumatoid arthritis, NF-kappa B signaling pathway and Pertussis) related to immune responses and inflammation. Five hub DE-ARGs (IL2RB, FOS, IL7R, ALDH2 and BIRC2) were identified using machine learning algorithms, and their association with immune-related characteristics was confirmed by additional tests. Single-cell sequencing dataset GSE129788 was retrieved to analyze aging molecular-related features, which was in accordance with microarray datasets. Clustering analysis revealed two subtypes of IS, which were distinguished by their differential expression of genes related to the NF-kappa B signaling pathway. These findings highlight the importance of ARGs in regulating immune responses in IS and suggest potential prevention and treatment strategies as well as guidelines for future research.

Developing and Validating a New Model to Predict the Risk of Poor Neurological Status of Acute Ischemic Stroke After Intravenous Thrombolysis

OBJECTIVES

The objective of this study was to develop and validate a predictive model for the risk of poor neurological status in in-hospital patients with acute ischemic stroke (AIS) after intravenous thrombolysis.

METHODS

This 2-center retrospective study included patients with AIS treated at the Advanced Stroke Center of the Second Hospital of Hebei Medical University and Baoding No.1 Central Hospital between January 2018 and January 2020). The neurological function status at day 7 of AIS onset was used as the endpoint of the study, which was evaluated using the National Institute of Health Stroke Scale (NIHSS) score.

RESULTS

A total of 878 patients were included in the study and divided into training (n=652) and validation (n=226) sets. Seven variables were selected as predictors to establish the risk model: age, NIHSS before thrombolysis (NIHSS1), NIHSS 24 hours after thrombolysis (NIHSS3), high-density lipoprotein, antiplatelet, cerebral computed tomography after thrombolysis (CT2), and lower extremity venous color Doppler ultrasound. The risk prediction model achieved good discrimination (the areas under the Receiver Operating Characteristic curve in the training and validation sets were 0.9626 and 0.9413, respectively) and calibration (in the training set Emax=0.072, Eavg=0.01, P =0.528, and in the validation set Emax=0.123, Eavg=0.019, P =0.594, respectively). The decision curve analysis showed that the model could achieve a good net benefit.

CONCLUSIONS

The prediction model obtained in this study showed good discrimination, calibration, and clinical efficacy. This new nomogram can provide a reference for predicting the risk of poor neurological status in patients with acute ischemic stroke after intravenous thrombolysis.

Prognostic estimation for acute ischemic stroke patients undergoing mechanical thrombectomy within an extended therapeutic window using an interpretable machine learning model

Background

Mechanical thrombectomy (MT) is effective for acute ischemic stroke with large vessel occlusion (AIS-LVO) within an extended therapeutic window. However, successful reperfusion does not guarantee positive prognosis, with around 40-50% of cases yielding favorable outcomes. Preoperative prediction of patient outcomes is essential to identify those who may benefit from MT. Although machine learning (ML) has shown promise in handling variables with non-linear relationships in prediction models, its "black box" nature and the absence of ML models for extended-window MT prognosis remain limitations.

Objective

This study aimed to establish and select the optimal model for predicting extended-window MT outcomes, with the Shapley additive explanation (SHAP) approach used to enhance the interpretability of the selected model.

Methods

A retrospective analysis was conducted on 260 AIS-LVO patients undergoing extended-window MT. Selected patients were allocated into training and test sets at a 3:1 ratio following inclusion and exclusion criteria. Four ML classifiers and one logistic regression (Logit) model were constructed using pre-treatment variables from the training set. The optimal model was selected through comparative validation, with key features interpreted using the SHAP approach. The effectiveness of the chosen model was further evaluated using the test set.

Results

Of the 212 selected patients, 159 comprised the training and 53 the test sets. Extreme gradient boosting (XGBoost) showed the highest discrimination with an area under the curve (AUC) of 0.93 during validation, and maintained an AUC of 0.77 during testing. SHAP analysis identified ischemic core volume, baseline NHISS score, ischemic penumbra volume, ASPECTS, and patient age as the top five determinants of outcome prediction.

Conclusion

XGBoost emerged as the most effective for predicting the prognosis of AIS-LVO patients undergoing MT within the extended therapeutic window. SHAP interpretation improved its clinical confidence, paving the way for ML in clinical decision-making.

Non-Excitatory Amino Acids, Melatonin, and Free Radicals: Examining the Role in Stroke and Aging

The aim of this review is to explore the relationship between melatonin, free radicals, and non-excitatory amino acids, and their role in stroke and aging. Melatonin has garnered significant attention in recent years due to its diverse physiological functions and potential therapeutic benefits by reducing oxidative stress, inflammation, and apoptosis. Melatonin has been found to mitigate ischemic brain damage caused by stroke. By scavenging free radicals and reducing oxidative damage, melatonin may help slow down the aging process and protect against age-related cognitive decline. Additionally, non-excitatory amino acids have been shown to possess neuroprotective properties, including antioxidant and anti-inflammatory in stroke and aging-related conditions. They can attenuate oxidative stress, modulate calcium homeostasis, and inhibit apoptosis, thereby safeguarding neurons against damage induced by stroke and aging processes. The intracellular accumulation of certain non-excitatory amino acids could promote harmful effects during hypoxia-ischemia episodes and thus, the blockade of the amino acid transporters involved in the process could be an alternative therapeutic strategy to reduce ischemic damage. On the other hand, the accumulation of free radicals, specifically mitochondrial reactive oxygen and nitrogen species, accelerates cellular senescence and contributes to age-related decline. Recent research suggests a complex interplay between melatonin, free radicals, and non-excitatory amino acids in stroke and aging. The neuroprotective actions of melatonin and non-excitatory amino acids converge on multiple pathways, including the regulation of calcium homeostasis, modulation of apoptosis, and reduction of inflammation. These mechanisms collectively contribute to the preservation of neuronal integrity and functions, making them promising targets for therapeutic interventions in stroke and age-related disorders.

From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning

As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disability after a stroke. Initial stroke severity is a significant predictor of functional dependence and mortality following an acute stroke. The current study aims to collect and analyze data from the hyperacute and acute phases of stroke, as well as from the medical history of the patients, in order to develop an explainable machine learning model for predicting stroke-related neurological deficits at discharge, as measured by the National Institutes of Health Stroke Scale (NIHSS). More specifically, we approached the data as a binary task problem: improvement of NIHSS progression vs. worsening of NIHSS progression at discharge, using baseline data within the first 72 h. For feature selection, a genetic algorithm was applied. Using various classifiers, we found that the best scores were achieved from the Random Forest (RF) classifier at the 15 most informative biomarkers and parameters for the binary task of the prediction of NIHSS score progression. RF achieved 91.13% accuracy, 91.13% recall, 90.89% precision, 91.00% f1-score, 8.87% FNrate and 4.59% FPrate. Those biomarkers are: age, gender, NIHSS upon admission, intubation, history of hypertension and smoking, the initial diagnosis of hypertension, diabetes, dyslipidemia and atrial fibrillation, high-density lipoprotein (HDL) levels, stroke localization, systolic blood pressure levels, as well as erythrocyte sedimentation rate (ESR) levels upon admission and the onset of respiratory infection. The SHapley Additive exPlanations (SHAP) model interpreted the impact of the selected features on the model output. Our findings suggest that the aforementioned variables may play a significant role in determining stroke patients' NIHSS progression from the time of admission until their discharge.

Differences in the post-stroke innate immune response between young and old

Aging is associated to progressive changes impairing fundamental cellular and tissue functions, and the relationships amongst them through the vascular and immune systems. Aging factors are key to understanding the pathophysiology of stroke since they increase its risk and worsen its functional outcome. Most currently recognised hallmarks of aging are also involved in the cerebral responses to stroke. Notably, age-associated chronic low-grade inflammation is related to innate immune responses highlighted by induction of type-I interferon. The interferon program is prominent in microglia where it interrelates cell damage, danger signals, and phagocytosis with immunometabolic disturbances and inflammation. Microglia engulfment of damaged myelin and cell debris may overwhelm the cellular capacity for waste removal inducing intracellular lipid accumulation. Acute inflammation and interferon-stimulated gene expression are also typical features of acute stroke, where danger signal recognition by microglia trigger immunometabolic alterations underscored by lipid droplet biogenesis. Aging reduces the capacity to control these responses causing increased and persistent inflammation, metabolic dysregulation, and impaired cellular waste disposal. In turn, chronic peripheral inflammation during aging induces immunosenescence further worsening stroke-induced immunodepression, thus increasing the risk of post-stroke infection. Aging also alters gut microbiota composition inducing dysbiosis. These changes are enhanced by age-related diseases, such as atherosclerosis and type-II diabetes, that further promote vascular aging, predispose to stroke, and exacerbate brain inflammation after stroke. Current advances in aging research suggest that some age-associated alterations may be reversed. Future work will unravel whether such evolving anti-aging research may enable designing strategies to improve stroke outcome in the elderly.

Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke

BACKGROUND AND OBJECTIVES

While chronological age is one of the most influential determinants of poststroke outcomes, little is known of the impact of neuroimaging-derived biological "brain age." We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of patients with stroke will be associated with cardiovascular risk factors and worse functional outcomes.

METHODS

We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison with chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs and a logistic regression model of favorable functional outcomes taking RBA as input.

RESULTS

We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age = 62.8 years, 42.0% female patients). T2-FLAIR radiomics predicted chronological ages (mean absolute error = 6.9 years, r = 0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted odds ratios: 0.58, 0.76, 0.48, 0.55; all p-values < 0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes.

DISCUSSION

T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older-appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.

Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors' Functional Outcome upon Discharge Utilizing Explainable Machine Learning

Despite therapeutic advancements, stroke remains a leading cause of death and long-term disability. The quality of current stroke prognostic models varies considerably, whereas prediction models of post-stroke disability and mortality are restricted by the sample size, the range of clinical and risk factors and the clinical applicability in general. Accurate prognostication can ease post-stroke discharge planning and help healthcare practitioners individualize aggressive treatment or palliative care, based on projected life expectancy and clinical course. In this study, we aimed to develop an explainable machine learning methodology to predict functional outcomes of stroke patients at discharge, using the Modified Rankin Scale (mRS) as a binary classification problem. We identified 35 parameters from the admission, the first 72 h, as well as the medical history of stroke patients, and used them to train the model. We divided the patients into two classes in two approaches: "Independent" vs. "Non-Independent" and "Non-Disability" vs. "Disability". Using various classifiers, we found that the best models in both approaches had an upward trend, with respect to the selected biomarkers, and achieved a maximum accuracy of 88.57% and 89.29%, respectively. The common features in both approaches included: age, hemispheric stroke localization, stroke localization based on blood supply, development of respiratory infection, National Institutes of Health Stroke Scale (NIHSS) upon admission and systolic blood pressure levels upon admission. Intubation and C-reactive protein (CRP) levels upon admission are additional features for the first approach and Erythrocyte Sedimentation Rate (ESR) levels upon admission for the second. Our results suggest that the said factors may be important predictors of functional outcomes in stroke patients.

Palliative care approaches to acute stroke in the hospital setting

Stroke is a prevalent neurologic condition that portends a high risk of morbidity and mortality such that patients impacted by stroke and their caregivers can benefit from palliative care at the time of diagnosis and throughout the disease trajectory. Clinicians who care for stroke patients should be adept at establishing rapport with patients and caregivers, delivering serious news, responding to emotions, discussing prognosis, and establishing goals of care efficiently in an acute stroke setting. Aggressive stroke care can be integrated with a palliative approach to care that involves aligning the available treatment options with a patient's values and goals of care. Reassessing the goals throughout the hospitalization provides an opportunity for continued shared decision-making about the intensity of poststroke interventions. The palliative needs for stroke patients may increase over time depending on the severity of disease, poststroke complications, stroke-related symptoms, and treatment intensity preferences. If the decision is made to transition the focus of care to comfort, the support of an interdisciplinary palliative care or hospice team can be beneficial to the patient, family members, and surrogate decision makers.

Prognosis of Individual-Level Mobility and Self-Care Stroke Recovery During Inpatient Rehabilitation, Part 1: A Proof-of-Concept Single Group Retrospective Cohort Study

OBJECTIVE

To demonstrate feasibility of generating predictive short-term individual trajectory recovery models after acute stroke by extracting clinical data from an electronic medical record (EMR) system.

DESIGN

Single-group retrospective patient cohort design.

SETTING

Stroke rehabilitation unit at an independent inpatient rehabilitation facility (IRF).

PARTICIPANTS

Cohort of 1408 inpatients with acute ischemic or hemorrhagic stroke with a mean ± SD age of 66 (14.5) years admitted between April 2014 and October 2019 (N=1408).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

0-100 Rasch-scaled Functional Independence Measure (FIM) Mobility and Self-Care subscales.

RESULTS

Unconditional models were best-fit on FIM Mobility and Self-Care subscales by spline fixed-effect functions with knots at weeks 1 and 2, and random effects on the baseline (FIM 0-100 Rasch score at IRF admission), initial rate (slope at time zero), and second knot (change in slope pre-to-post week 2) parameters. The final Mobility multivariable model had intercept associations with Private/Other Insurance, Ischemic Stroke, Serum Albumin, Motricity Index Lower Extremity, and FIM Cognition; and initial slope associations with Ischemic Stroke, Private/Other and Medicaid Insurance, and FIM Cognition. The final Self-Care multivariable model had intercept associations with Private/Other Insurance, Ischemic Stroke, Living with One or More persons, Serum Albumin, and FIM Cognition; and initial slope associations with Ischemic Stroke, Private/Other and Medicaid Insurance, and FIM Cognition. Final models explained 52% and 27% of the variance compared with unconditional Mobility and Self-Care models. However, some EMR data elements had apparent coding errors or missing data, and desired elements from acute care were not available. Also, unbalanced outcome data may have biased trajectories.

CONCLUSIONS

We demonstrate the feasibility of developing individual-level prognostic models from EMR data; however, some data elements were poorly defined, subject to error, or missing for some or all cases. Development of prognostic models from EMR will require improvements in EMR data collection and standardization.

Multicentric validation of a reduced features case-mix set for predicting functional outcome after ischemic stroke in Belgium

INTRODUCTION

Ischemic stroke is the second cause of death and leading cause of severe disability worldwide. A reduced features set of CT-DRAGON (age, NIHSS on admission and pre-stroke mRS) predicts 90-day functional outcome after stroke in a single center. The current study was designed to validate this adapted CT-DRAGON score in three major Belgian hospitals, in the framework of future case-mix adjustment.

METHODS

This retrospective study included stroke patients, treated by thrombolysis, thrombectomy, a combination of both or neither thrombolysis or thrombectomy (conservative treatment) in 2019. Patient characteristics and 90-day mRS were collected. Multivariable logistic regression analysis of 90-day mRS 0-2 vs. 3-6 and 0-5 vs. 6 with the reduced features set was performed. Discriminative performance was assessed by the area under the receiver operating characteristic curve (AUROC).

RESULTS

Thirty-three percent of patients (413/1243) underwent treatment. Majority of strokes was treated conservatively (n = 830, 67%), 18% (n = 225) was treated by thrombolysis, 7% (n = 88) by thrombectomy and 8% (n = 100) by thrombolysis and thrombectomy. Age, NIHSS and pre-stroke mRS were independently associated with 90-day mRS 0-2 (all p ≤ 0.0001, AUROC 0.88). When treatment modality was added in the model, age, NIHSS, pre-stroke mRS and treatment modality were independently associated with 90-day mRS 0-2 (p < 0.0001, p < 0.0001, p < 0.0001 and p = 0.0001) AUROC 0.89). Age, NIHSS, pre-stroke mRS and treatment modality were independently associated with 90-day survival (p = 0.0001, p < 0.0001, p < 0.0001 and p = 0.008, AUROC 0.86).

DISCUSSION

The reduced features set (age, NIHSS and pre-mRS) was independently associated with long-term functional outcome in a Belgian multicentric cohort, making it useful for case-mix adjustments in Belgian stroke centers. Treatment modality was associated with long-term outcome.

‘We Have Guidelines, but We Can Also Be Artists’: Neurologists Discuss Prognostic Uncertainty, Cognitive Biases, and Scoring Tools

Introduction: Ischemic stroke is a leading cause of disability and mortality worldwide. As acute stroke patients often lose decision-making capacity, acute management is fraught with complicated decisions regarding life-sustaining treatment (LST). We aimed to explore (1) the perspectives and experiences of clinicians regarding the use of predictive scores for LST decision making in severe acute stroke, and (2) clinicians’ awareness of their own cognitive biases in this context. Methods: Four focus groups (FGs) were conducted with 21 physicians (13 residents and 8 attending physicians); two FGs in a university hospital and two in a regional hospital in French-speaking Switzerland. Discussions were audio-recorded and transcribed verbatim. Transcripts were analyzed thematically. Two of the four transcripts were double coded to establish coding framework consistency. Results: Participants reported that predictive tools were not routinely used after severe stroke, although most knew about such scores. Scores were reported as being useful in quantifying prognosis, advancing scientific evidence, and minimizing potential biases in decisions. Their use is, however, limited by the following barriers: perception of inaccuracy, general disbelief in scoring, fear of self-fulfilling prophecy, and preference for clinical judgement. Emotional and cognitive biases were common. Emotional biases distort clinicians’ knowledge and are notably: bias of personal values, negative experience, and cultural bias. Cognitive biases, such as availability, confirmation, and anchoring biases, that produce systematic deviations from rational thinking, were also identified. Conclusions: The results highlight opportunities to improve decision making in severe stroke through the promotion of predictive tools, strategies for communicating prognostic uncertainty, and minimizing cognitive biases among clinicians, in order to promote goal-concordant care.

Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing

Background

Timely detection of atrial fibrillation (AF) after stroke is highly clinically relevant, aiding decisions on the optimal strategies for secondary prevention of stroke. In the context of limited medical resources, it is crucial to set the right priorities of extended heart rhythm monitoring by stratifying patients into different risk groups likely to have newly detected AF (NDAF). This study aimed to develop an electronic health record (EHR)-based machine learning model to assess the risk of NDAF in an early stage after stroke.

Methods

Linked data between a hospital stroke registry and a deidentified research-based database including EHRs and administrative claims data was used. Demographic features, physiological measurements, routine laboratory results, and clinical free text were extracted from EHRs. The extreme gradient boosting algorithm was used to build the prediction model. The prediction performance was evaluated by the C-index and was compared to that of the AS5F and CHASE-LESS scores.

Results

The study population consisted of a training set of 4,064 and a temporal test set of 1,492 patients. During a median follow-up of 10.2 months, the incidence rate of NDAF was 87.0 per 1,000 person-year in the test set. On the test set, the model based on both structured and unstructured data achieved a C-index of 0.840, which was significantly higher than those of the AS5F (0.779, p = 0.023) and CHASE-LESS (0.768, p = 0.005) scores.

Conclusions

It is feasible to build a machine learning model to assess the risk of NDAF based on EHR data available at the time of hospital admission. Inclusion of information derived from clinical free text can significantly improve the model performance and may outperform risk scores developed using traditional statistical methods. Further studies are needed to assess the clinical usefulness of the prediction model.

Dynamic change of neutrophil-to-lymphocyte ratio and symptomatic intracerebral hemorrhage after endovascular recanalization therapy

OBJECTIVES

The neutrophil-to-lymphocyte ratio (NLR) is a known marker of systemic inflammation. Recent studies demonstrated its applicability as a marker of poor prognosis for stroke patients. In this study, we evaluated the relationship between dynamic changes in the NLR and sICH in patients with successful recanalization following ERT.

MATERIALS AND METHODS

This study included 128 patients with acute ischemic stroke who underwent successful ERT between January 2013 and November 2019. We evaluated the NLR pre-ERT (at admission) and post-ERT (at 24-36 h after ERT). The symptomatic ICH and miserable outcomes at 3 months after ERT were analyzed as outcomes. sICH was defined as type-2 parenchymal hematoma with neurological deterioration (defined as National Institute of Health Stroke Scale score ≥4). Moreover, a modified Rankin Scale score of 5-6 at 3 months was considered a miserable outcome.

RESULTS

Among the included patients, sICH occurred in 12 (9.4%). The sICH group had significantly higher post-ERT NLR (P < 0.001) and ∆NLR (calculated as the difference between pre-ERT NLR and post-ERT NLR) (P = 0.004). In the multivariate analysis, the post-ERT NLR was independently associated with sICH (odds ratio [OR], 1.166; 95% confidence interval [CI], 1.041-1.306; P = 0.008) and miserable outcome at 3 months (OR, 1.101; 95% CI, 1.002-1.210; P = 0.045).

CONCLUSIONS

This study demonstrated that temporal elevation of the NLR is associated with sICH events after successful ERT in patients with acute ischemic stroke. The temporal variation in NLR may help to identify high-risk patients with sICH after ERT.

A Presurgical Unfavorable Prediction Scale of Endovascular Treatment for Acute Ischemic Stroke

Objective

To develop a prognostic prediction model of endovascular treatment (EVT) for acute ischemic stroke (AIS) induced by large-vessel occlusion (LVO), this study applied machine learning classification model light gradient boosting machine (LightGBM) to construct a unique prediction model.

Methods

A total of 973 patients were enrolled, primary outcome was assessed with modified Rankin scale (mRS) at 90 days, and favorable outcome was defined using mRS 0–2 scores. Besides, LightGBM algorithm and logistic regression (LR) were used to construct a prediction model. Then, a prediction scale was further established and verified by both internal data and other external data.

Results

A total of 20 presurgical variables were analyzed using LR and LightGBM. The results of LightGBM algorithm indicated that the accuracy and precision of the prediction model were 73.77 and 73.16%, respectively. The area under the curve (AUC) was 0.824. Furthermore, the top 5 variables suggesting unfavorable outcomes were namely admitting blood glucose levels, age, onset to EVT time, onset to hospital time, and National Institutes of Health Stroke Scale (NIHSS) scores (importance = 130.9, 102.6, 96.5, 89.5 and 84.4, respectively). According to AUC, we established the key cutoff points and constructed prediction scale based on their respective weightings. Then, the established prediction scale was verified in raw and external data and the sensitivity was 80.4 and 83.5%, respectively. Finally, scores &gt;3 demonstrated better accuracy in predicting unfavorable outcomes.

Conclusion

Presurgical prediction scale is feasible and accurate in identifying unfavorable outcomes of AIS after EVT.

Predicting 10-year stroke mortality: development and validation of a nomogram

Predicting long-term stroke mortality is a clinically important and unmet need. We aimed to develop and internally validate a 10-year ischaemic stroke mortality prediction score. In this UK cohort study, 10,366 patients with first-ever ischaemic stroke between January 2003 and December 2016 were followed up for a median (interquartile range) of 5.47 (2.96-9.15) years. A Cox proportional-hazards model was used to predict 10-year post-admission mortality. The predictors associated with 10-year mortality included age, sex, Oxfordshire Community Stroke Project classification, estimated glomerular filtration rate (eGFR), pre-stroke modified Rankin Score, admission haemoglobin, sodium, white blood cell count and comorbidities (atrial fibrillation, coronary heart disease, heart failure, cancer, hypertension, chronic obstructive pulmonary disease, liver disease and peripheral vascular disease). The model was internally validated using bootstrap resampling to assess optimism in discrimination and calibration. A nomogram was created to facilitate application of the score at the point of care. Mean age (SD) was 78.5 ± 10.9 years, 52% female. Most strokes were partial anterior circulation syndromes (38%). 10-year mortality predictors were: total anterior circulation stroke (hazard ratio, 95% confidence intervals) (2.87, 2.62-3.14), eGFR < 15 (1.97, 1.55-2.52), 1-year increment in age (1.04, 1.04-1.05), liver disease (1.50, 1.20-1.87), peripheral vascular disease (1.39, 1.23-1.57), cancers (1.37, 1.27-1.47), heart failure (1.24, 1.15-1.34), 1-point increment in pre-stroke mRS (1.20, 1.17-1.22), atrial fibrillation (1.17, 1.10-1.24), coronary heart disease (1.09, 1.02-1.16), chronic obstructive pulmonary disease (1.13, 1.03-1.25) and hypertension (0.77, 0.72-0.82). Upon internal validation, the optimism-adjusted c-statistic was 0.76 and calibration slope was 0.98. Our 10-year mortality model uses routinely collected point-of-care information. It is the first 10-year mortality score in stroke. While the model was internally validated, further external validation is also warranted.

Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction

Background and Purpose

Mechanical thrombectomy greatly improves stroke outcomes. Nonetheless, some patients fall short of full recovery despite good reperfusion. The purpose of this study was to develop machine learning (ML) models for the pre-interventional prediction of functional outcome at 3 months of thrombectomy in acute ischemic stroke (AIS), using clinical and auto-extractable radiological information consistently available upon first emergency evaluation.

Materials and Methods

A two-center retrospective cohort of 293 patients with AIS who underwent thrombectomy was analyzed. ML models were developed to predict dichotomized modified Rankin score at 90 days (mRS-90) using clinical and imaging features, both separately and combined. Conventional and experimental imaging biomarkers were quantified using automated image-processing software from non-contract computed tomography (CT) and computed tomography angiography (CTA). Shapley Additive Explanation (SHAP) was applied for model interpretability and predictor importance analysis of the optimal model.

Results

Merging clinical and imaging features returned the best results for mRS-90 prediction. The best performing classifier was Extreme Gradient Boosting (XGB) with an area under the receiver operating characteristic curve (AUC) = 84% using selected features. The most important classifying features were age, baseline National Institutes of Health Stroke Scale (NIHSS), occlusion side, degree of brain atrophy [primarily represented by cortical cerebrospinal fluid (CSF) volume and lateral ventricle volume], early ischemic core [primarily represented by e-Alberta Stroke Program Early CT Score (ASPECTS)], and collateral circulation deficit volume on CTA.

Conclusion

Machine learning that is applied to quantifiable image features from CT and CTA alongside basic clinical characteristics constitutes a promising automated method in the pre-interventional prediction of stroke prognosis. Interpretable models allow for exploring which initial features contribute the most to post-thrombectomy outcome prediction overall and for each individual patient outcome.

A Dynamic Nomogram to Predict the 3-Month Unfavorable Outcome of Patients with Acute Ischemic Stroke

Purpose

Despite receiving standard-of-care treatments, a significant proportion of patients with acute ischemic stroke (AIS) are left with long-term functional impairment. Therefore, an easy-to-use tool for predicting of unfavorable outcome following AIS plays an important role in clinical practice. This study was aimed to develop a dynamic nomogram to predict the 3-month unfavorable outcome for AIS patients.

Methods

This was a prospective observational study conducted in consecutive patients with AIS admitted to our stroke center between September 2019 and June 2020. Baseline demographic, clinical, and laboratory information were obtained. The primary outcome was evaluated with modified Rankin Scale (mRS) scores at 3 months. Least absolute shrinkage and selection operator regression was used to select the optimal predictive factors. Multiple logistics regression was performed to establish the nomogram. Decision curve analysis (DCA) was applied to assess the clinical utility of the nomogram. The calibration and discrimination property of the nomogram was validated by calibration plots and concordance index.

Results

A total of 93 eligible patients were enrolled: 28 (30.1%) patients had unfavorable outcome (mRS >2). Glycosylated hemoglobin (OR, 1.541; 95% CI, 1.051–2.261), the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) (OR, 0.635; 95% CI, 0.463–0.871), and National Institute of Health Stroke Scale (NIHSS) (OR 1.484; 95% CI, 1.155–1.907) were significant predictors of the poor outcome of patients with AIS and included into the nomogram model. The nomogram showed good calibration and discrimination. C-index was 0.891 (95% CI, 0.854–0.928). DCA confirmed the clinical usefulness of the model. The dynamic nomogram can be obtained at the website: https://odywong.shinyapps.io/DBT_21/.

Conclusion

The dynamic nomogram, comprised of glycosylated hemoglobin, ASPECTS, and NIHSS score at day 14, may be able to predict the 3-month unfavorable outcome for AIS patients.

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence

Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke.

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature

PURPOSE OF REVIEW

To critically appraise literature on recent advances and methods using "big data" to evaluate stroke outcomes and associated factors.

RECENT FINDINGS

Recent big data studies provided new evidence on the incidence of stroke outcomes, and important emerging predictors of these outcomes. Main highlights included the identification of COVID-19 infection and exposure to a low-dose particulate matter as emerging predictors of mortality post-stroke. Demographic (age, sex) and geographical (rural vs. urban) disparities in outcomes were also identified. There was a surge in methodological (e.g., machine learning and validation) studies aimed at maximizing the efficiency of big data for improving the prediction of stroke outcomes. However, considerable delays remain between data generation and publication. Big data are driving rapid innovations in research of stroke outcomes, generating novel evidence for bridging practice gaps. Opportunity exists to harness big data to drive real-time improvements in stroke outcomes.

Early Prediction of Functional Outcomes After Acute Ischemic Stroke Using Unstructured Clinical Text: Retrospective Cohort Study

Background

Several prognostic scores have been proposed to predict functional outcomes after an acute ischemic stroke (AIS). Most of these scores are based on structured information and have been used to develop prediction models via the logistic regression method. With the increased use of electronic health records and the progress in computational power, data-driven predictive modeling by using machine learning techniques is gaining popularity in clinical decision-making.

Objective

We aimed to investigate whether machine learning models created by using unstructured text could improve the prediction of functional outcomes at an early stage after AIS.

Methods

We identified all consecutive patients who were hospitalized for the first time for AIS from October 2007 to December 2019 by using a hospital stroke registry. The study population was randomly split into a training (n=2885) and test set (n=962). Free text in histories of present illness and computed tomography reports was transformed into input variables via natural language processing. Models were trained by using the extreme gradient boosting technique to predict a poor functional outcome at 90 days poststroke. Model performance on the test set was evaluated by using the area under the receiver operating characteristic curve (AUC).

Results

The AUCs of text-only models ranged from 0.768 to 0.807 and were comparable to that of the model using National Institutes of Health Stroke Scale (NIHSS) scores (0.811). Models using both patient age and text achieved AUCs of 0.823 and 0.825, which were similar to those of the model containing age and NIHSS scores (0.841); the model containing preadmission comorbidities, level of consciousness, age, and neurological deficit (PLAN) scores (0.837); and the model containing Acute Stroke Registry and Analysis of Lausanne (ASTRAL) scores (0.840). Adding variables from clinical text improved the predictive performance of the model containing age and NIHSS scores, the model containing PLAN scores, and the model containing ASTRAL scores (the AUC increased from 0.841 to 0.861, from 0.837 to 0.856, and from 0.840 to 0.860, respectively).

Conclusions

Unstructured clinical text can be used to improve the performance of existing models for predicting poststroke functional outcomes. However, considering the different terminologies that are used across health systems, each individual health system may consider using the proposed methods to develop and validate its own models.

Interaction between stroke severity and quality indicators of acute stroke care: a single-center retrospective analysis

Ischemic stroke leads to substantial mortality and morbidity worldwide. Door-to-CT time, door-to-needle time (DNT), and door-to-groin time (DGT) are important quality indicators of stroke care. However, patient characteristics remain important determinants of outcome as well. In this single-center study, we investigated the interaction between these quality indicators and stroke severity regarding long-term functional outcome. All consecutive stroke patients treated at the ZOL stroke center, Genk, Belgium, between 2017 and 2020 were included in this retrospective observational study. Stroke severity was graded as "mild" if National Institutes of Health Stroke Scale (NIHSS) was equal to or lower than 8, "moderate" if NIHSS was between 9 and 15, and "severe" if NIHSS was higher than 16. Modified Rankin Scale (mRS) scores were collected before and 3 months after stroke. Ordinal regression analysis with correction for patient characteristics of functional outcome was done. A total of 1255 patients were included, of which 84% suffered an ischemic CVA (n = 1052) and 16% a TIA (n = 203). The proportion of patients treated conservatively or with thrombolysis, thrombectomy, or the combination of both differed according to stroke severity (p < 0.0001). Door-to-CT time was longer in mild and moderate stroke (p < 0.0001). Median DNT also differed between stroke categories: 46 (IQR 31-70) min for mild vs. 36 (25-56) min for moderate vs. 30 (21-45) min for severe stroke (p = 0.0002). Median DGT did not differ between stroke severity categories (p = 0.15). NIHSS on admission and pre-stroke mRS were independently associated with mRS at 90 days. Operational performance, reflected in door-to-CT time and DNT, was worse in patients with mild and moderate stroke severity. DNT was also associated with functional outcome in our center, along with pre-stroke mRS, NIHSS on admission and age.

Natural Language Processing Enhances Prediction of Functional Outcome After Acute Ischemic Stroke

Background

Conventional prognostic scores usually require predefined clinical variables to predict outcome. The advancement of natural language processing has made it feasible to derive meaning from unstructured data. We aimed to test whether using unstructured text in electronic health records can improve the prediction of functional outcome after acute ischemic stroke.

Methods and Results

Patients hospitalized for acute ischemic stroke were identified from 2 hospital stroke registries (3847 and 2668 patients, respectively). Prediction models developed using the first cohort were externally validated using the second cohort, and vice versa. Free text in the history of present illness and computed tomography reports was used to build machine learning models using natural language processing to predict poor functional outcome at 90 days poststroke. Four conventional prognostic models were used as baseline models. The area under the receiver operating characteristic curves of the model using history of present illness in the internal and external validation sets were 0.820 and 0.792, respectively, which were comparable to the National Institutes of Health Stroke Scale score (0.811 and 0.807). The model using computed tomography reports achieved area under the receiver operating characteristic curves of 0.758 and 0.658. Adding information from clinical text significantly improved the predictive performance of each baseline model in terms of area under the receiver operating characteristic curves, net reclassification improvement, and integrated discrimination improvement indices (all P<0.001). Swapping the study cohorts led to similar results.

Conclusions

By using natural language processing, unstructured text in electronic health records can provide an alternative tool for stroke prognostication, and even enhance the performance of existing prognostic scores.

Prognostic Scores for Large Vessel Occlusion Strokes

PURPOSE OF THE REVIEW

Endovascular thrombectomy (EVT) for large vessel occlusion strokes (LVOS) presents several treatment challenges. We provide a summary of existing tools for patient selection (pre-EVT tools) and for prognostication of long-term outcomes following reperfusion therapy (post-EVT tools).

RECENT FINDINGS

Recently published randomized trials demonstrated superiority of EVT over medical therapy alone for LVOS. Uniform patient selection paradigms based on demographic, clinical, and radiographic variables are not completely standardized, leading to variability in patient selection for EVT for LVOS. Post-EVT, an accurate assessment of long-term prognosis is critical in the decision-making process.

SUMMARY

Prognostic scores can serve as useful adjuncts to facilitate clinical decision-making during early management of patients with ischemic stroke, particularly those with LVOS. The acute management of LVOS comprises rapid clinical assessment, triage, and cerebrovascular imaging, followed by evaluation for candidacy for thrombolysis and EVT. Pre-EVT prognostic tools that accurately predict the likelihood of benefit from EVT may guide reliable, efficient, and cost-effective patient selection. Following EVT, severe stroke deficits and subacute poststroke complications that portend a poor prognosis may warrant invasive therapies. Clinical decisions regarding these treatment options involve careful discussions between providers and patient families, and are also based on prognosis provided by the treating clinician. Reliable post-EVT prognostic tools can facilitate this by providing accurate and objective prognostic information. Several prognostic tools have been developed and validated in the literature, some of which may be applicable in the pre-EVT and post-EVT settings, although clinical utility and application varies. Validation in contemporary datasets as well as implementation and impact studies are needed before these scales can be used to guide clinical decisions for individual patients.

Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

End-of-Life Care Decision-Making in Stroke

Stroke is one of the leading causes of death and long-term disability in the United States. Though advances in interventions have improved patient survival after stroke, prognostication of long-term functional outcomes remains challenging, thereby complicating discussions of treatment goals. Stroke patients who require intensive care unit care often do not have the capacity themselves to participate in decision making processes, a fact that further complicates potential end-of-life care discussions after the immediate post-stroke period. Establishing clear, consistent communication with surrogates through shared decision-making represents best practice, as these surrogates face decisions regarding artificial nutrition, tracheostomy, code status changes, and withdrawal or withholding of life-sustaining therapies. Throughout decision-making, clinicians must be aware of a myriad of factors affecting both provider recommendations and surrogate concerns, such as cognitive biases. While decision aids have the potential to better frame these conversations within intensive care units, aids specific to goals-of-care decisions for stroke patients are currently lacking. This mini review highlights the difficulties in decision-making for critically ill ischemic stroke and intracerebral hemorrhage patients, beginning with limitations in current validated clinical scales and clinician subjectivity in prognostication. We outline processes for identifying patient preferences when possible and make recommendations for collaborating closely with surrogate decision-makers on end-of-life care decisions.

European Stroke Organisation and European Academy of Neurology joint guidelines on post-stroke cognitive impairment

BACKGROUND AND PURPOSE

The optimal management of post-stroke cognitive impairment (PSCI) remains controversial. These joint European Stroke Organisation (ESO) and European Academy of Neurology (EAN) guidelines provide evidence-based recommendations to assist clinicians in decision making regarding prevention, diagnosis, treatment and prognosis.

METHODS

Guidelines were developed according to the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The working group identified relevant clinical questions, performed systematic reviews, assessed the quality of the available evidence, and made specific recommendations. Expert consensus statements were provided where insufficient evidence was available to provide recommendations.

RESULTS

There was limited randomized controlled trial (RCT) evidence regarding single or multicomponent interventions to prevent post-stroke cognitive decline. Lifestyle interventions and treating vascular risk factors have many health benefits, but a cognitive effect is not proven. We found no evidence regarding routine cognitive screening following stroke, but recognize the importance of targeted cognitive assessment. We describe the accuracy of various cognitive screening tests, but found no clearly superior approach to testing. There was insufficient evidence to make a recommendation for use of cholinesterase inhibitors, memantine nootropics or cognitive rehabilitation. There was limited evidence on the use of prediction tools for post-stroke cognition. The association between PSCI and acute structural brain imaging features was unclear, although the presence of substantial white matter hyperintensities of presumed vascular origin on brain magnetic resonance imaging may help predict cognitive outcomes.

CONCLUSIONS

These guidelines highlight fundamental areas where robust evidence is lacking. Further definitive RCTs are needed, and we suggest priority areas for future research.

European Stroke Organisation and European Academy of Neurology joint guidelines on post-stroke cognitive impairment

The optimal management of post-stroke cognitive impairment remains controversial. These joint European Stroke Organisation (ESO) and European Academy of Neurology (EAN) guidelines provide evidence-based recommendations to assist clinicians in decision making around prevention, diagnosis, treatment and prognosis. These guidelines were developed according to ESO standard operating procedure and the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The working group identified relevant clinical questions, performed systematic reviews and, where possible, meta-analyses of the literature, assessed the quality of the available evidence and made specific recommendations. Expert consensus statements were provided where insufficient evidence was available to provide recommendations based on the GRADE approach. There was limited randomised controlled trial evidence regarding single or multicomponent interventions to prevent post-stroke cognitive decline. Interventions to improve lifestyle and treat vascular risk factors may have many health benefits but a beneficial effect on cognition is not proven. We found no evidence around routine cognitive screening following stroke but recognise the importance of targeted cognitive assessment. We described the accuracy of various cognitive screening tests but found no clearly superior approach to testing. There was insufficient evidence to make a recommendation for use of cholinesterase inhibitors, memantine nootropics or cognitive rehabilitation. There was limited evidence on the use of prediction tools for post-stroke cognitive syndromes (cognitive impairment, dementia and delirium). The association between post-stroke cognitive impairment and most acute structural brain imaging features was unclear, although the presence of substantial white matter hyperintensities of presumed vascular origin on acute MRI brain may help predict cognitive outcomes. These guidelines have highlighted fundamental areas where robust evidence is lacking. Further, definitive randomised controlled trials are needed, and we suggest priority areas for future research.

Actigraphic Measurement of the Upper Limbs for the Prediction of Ischemic Stroke Prognosis: An Observational Study

Background: It is often challenging to formulate a reliable prognosis for patients with acute ischemic stroke. The most accepted prognostic factors may not be sufficient to predict the recovery process. In this view, describing the evolution of motor deficits over time via sensors might be useful for strengthening the prognostic model. Our aim was to assess whether an actigraphic-based parameter (Asymmetry Rate Index for the 24 h period (AR2_24 h)) obtained in the acute stroke phase could be a predictor of a 90 d prognosis. Methods: In this observational study, we recorded and analyzed the 24 h upper limb movement asymmetry of 20 consecutive patients with acute ischemic stroke during their stay in a stroke unit. We recorded the motor activity of both arms using two programmable actigraphic systems positioned on patients’ wrists. We clinically evaluated the stroke patients by NIHSS in the acute phase and then assessed them across 90 days using the modified Rankin Scale (mRS). Results: We found that the AR2_24 h parameter positively correlates with the 90 d mRS (r = 0.69, p < 0.001). Moreover, we found that an AR2_24 h > 32% predicts a poorer outcome (90 d mRS > 2), with sensitivity = 100% and specificity = 89%. Conclusions: Sensor-based parameters might provide useful information for predicting ischemic stroke prognosis in the acute phase.

Aging Immune System in Acute Ischemic Stroke: A Transcriptomic Analysis

[Figure: see text].

Different Stroke Scales; Which Scale or Scales Should Be Used?

BACKGROUND

There has been a considerable development in the clinometric of stroke. But researchers are concerned that some scales are too generic, inherently and the insight may not be provided. The current study was conducted to determine which scale or scales should be used in stroke survivors.

METHODS

We selected 67 studies which were published between January 2010 and December 2018 from Up to date, CINAHL, ProQuest, Scopus, PubMed, Embase, Medline, Elsevier and Web of Science with MeSH terms. Inclusion criteria were: clinical trials, prospective studies, retrospective cohort studies, or cross-sectional studies; original research in adult human stroke survivors. We excluded the following articles: non-adult population; highly selected studies or treatment studies without incidence data; commentaries, single case reports, review article, editorials and non-English articles or articles without full text available.

RESULTS

Face Arm Speech Test and Cincinnati Pre-Hospital Stroke Scale scales because it was easy to learn and rapidly administer the recommended dose to use in pre-hospital, but there are not gold standard in stroke diagnosis in Pre-Hospital. National Institutes of Health Stroke Scale valuable in the acute stage for middle cerebral artery, not chronic or long term post stroke outcome. The Barthel Index scores for approximately three weeks could predict activities of daily living disabilities in 6 months.

CONCLUSION

Every scale has an advantage and a disadvantage and we were not able to introduce the gold standard for each item, but some special scales were used more in the studies, preferred for comparing with other studies to match the research results.

Towards biomarkers for outcomes after pancreatic ductal adenocarcinoma and ischaemic stroke, with focus on (co)-morbidity and ageing/cellular senescence (SASKit): protocol for a prospective cohort study

INTRODUCTION

Ageing-related processes such as cellular senescence are believed to underlie the accumulation of diseases in time, causing (co)morbidity, including cancer, thromboembolism and stroke. Interfering with these processes may delay, stop or reverse morbidity. The aim of this study is to investigate the link between (co)morbidity and ageing by exploring biomarkers and molecular mechanisms of disease-triggered deterioration in patients with pancreatic ductal adenocarcinoma (PDAC) and (thromboembolic) ischaemic stroke (IS).

METHODS AND ANALYSIS

We will recruit 50 patients with PDAC, 50 patients with (thromboembolic) IS and 50 controls at Rostock University Medical Center, Germany. We will gather routine blood data, clinical performance measurements and patient-reported outcomes at up to seven points in time, alongside in-depth transcriptomics and proteomics at two of the early time points. Aiming for clinically relevant biomarkers, the primary outcome is a composite of probable sarcopenia, clinical performance (described by ECOG Performance Status for patients with PDAC and the Modified Rankin Scale for patients with stroke) and quality of life. Further outcomes cover other aspects of morbidity such as cognitive decline and of comorbidity such as vascular or cancerous events. The data analysis is comprehensive in that it includes biostatistics and machine learning, both following standard role models and additional explorative approaches. Prognostic and predictive biomarkers for interventions addressing senescence may become available if the biomarkers that we find are specifically related to ageing/cellular senescence. Similarly, diagnostic biomarkers will be explored. Our findings will require validation in independent studies, and our dataset shall be useful to validate the findings of other studies. In some of the explorative analyses, we shall include insights from systems biology modelling as well as insights from preclinical animal models. We anticipate that our detailed study protocol and data analysis plan may also guide other biomarker exploration trials.

ETHICS AND DISSEMINATION

The study was approved by the local ethics committee (Ethikkommission an der Medizinischen Fakultät der Universität Rostock, A2019-0174), registered at the German Clinical Trials Register (DRKS00021184), and results will be published following standard guidelines.

Transforming self-reported outcomes from a stroke register to the modified Rankin Scale: a cross-sectional, explorative study

The aim was to create an algorithm to transform self-reported outcomes from a stroke register to the modified Rankin Scale (mRS). Two stroke registers were used: the Väststroke, a local register in Gothenburg, Sweden, and the Riksstroke, a Swedish national register. The reference variable, mRS (from Väststroke), was mapped with seven self-reported questions from Riksstroke. The transformation algorithm was created as a result of manual mapping performed by healthcare professionals. A supervised machine learning method—decision tree—was used to further evaluate the transformation algorithm. Of 1145 patients, 54% were male, the mean age was 71 y. The mRS grades 0, 1 and 2 could not be distinguished as a result of manual mapping or by using the decision tree analysis. Thus, these grades were merged. With manual mapping, 78% of the patients were correctly classified, and the level of agreement was almost perfect, weighted Kappa (K_w) was 0.81. With the decision tree, 80% of the patients were correctly classified, and substantial agreement was achieved, K_w = 0.67. The self-reported outcomes from a stroke register can be transformed to the mRS. A mRS algorithm based on manual mapping might be useful for researchers using self-reported questionnaire data.

Prediction of Functional Outcome After Acute Ischemic Stroke: Comparison of the CT-DRAGON Score and a Reduced Features Set

Background and Purpose: The CT-DRAGON score was developed to predict long-term functional outcome after acute stroke in the anterior circulation treated by thrombolysis. Its implementation in clinical practice may be hampered by its plethora of variables. The current study was designed to develop and evaluate an alternative score, as a reduced set of features, derived from the original CT-DRAGON score.

Methods: This single-center retrospective study included 564 patients treated for stroke, in the anterior and the posterior circulation. At 90 days, favorable [modified Rankin Scale score (mRS) of 0–2] and miserable outcome (mRS of 5–6) were predicted by the CT-DRAGON in 427 patients. Bootstrap forests selected the most relevant parameters of the CT-DRAGON, in order to develop a reduced set of features. Discrimination, calibration and misclassification of both models were tested.

Results: The area under the receiver operating characteristic curve (AUROC) for the CT-DRAGON was 0.78 (95% CI 0.74–0.81) for favorable and 0.78 (95% CI 0.72-0.83) for miserable outcome. Misclassification was 29% for favorable and 13.5% for miserable outcome, with a 100% specificity for the latter. National Institutes of Health Stroke Scale (NIHSS), pre-stroke mRS and age were identified as the strongest contributors to favorable and miserable outcome and named the reduced features set. While CT-DRAGON was only available in 323 patients (57%), the reduced features set could be calculated in 515 patients (91%) (p < 0.001). Misclassification was 25.8% for favorable and 14.4% for miserable outcome, with a 97% specificity for miserable outcome. The reduced features set had better discriminative power than CT-DRAGON for both outcomes (both p < 0.005), with an AUROC of 0.82 (95% CI 0.79–0.86) and 0.83 (95% CI 0.77–0.87) for favorable and miserable outcome, respectively.

Conclusions: The CT-DRAGON score revealed acceptable discrimination in our cohort of both anterior and posterior circulation strokes, receiving all treatment modalities. The reduced features set could be measured in a larger cohort and with better discrimination. However, the reduced features set needs further validation in a prospective, multicentre study.

Clinical Trial Registration: http://www.clinicaltrials.gov. Identifiers: NCT03355690, NCT04092543.

Predictors of urinary tract infection in acute stroke patients: A cohort study

Patients with stroke have a high risk of infection which may be predicted by age, procalcitonin, interleukin-6, C-reactive protein, National Institute of Health stroke scale (NHSS) score, diabetes, etc. These prediction methods can reduce unfavourable outcome by preventing the occurrence of infection.We aim to identify early predictors for urinary tract infection in patients after stroke.In 186 collected acute stroke patients, we divided them into urinary tract infection group, other infection type groups, and non-infected group. Data were recorded at admission. Independent risk factors and infection prediction model were determined using Logistic regression analyses. Likelihood ratio test was used to detect the prediction effect of the model. Receiver operating characteristic curve and the corresponding area under the curve were used to measure the predictive accuracy of indicators for urinary tract infection.Of the 186 subjects, there were 35 cases of urinary tract infection. Elevated interleukin-6, higher NIHSS, and decreased hemoglobin may be used to predict urinary tract infection. And the predictive model for urinary tract infection (including sex, NIHSS, interleukin-6, and hemoglobin) have the best predictive effect.This study is the first to discover that decreased hemoglobin at admission may predict urinary tract infection. The prediction model shows the best accuracy.

Predicting ambulatory recovery in acute ischemic stroke patients with thrombolytic therapy

Aim: The aim of this study was to determine the predictive value of clinical presentations on functional ambulation following thrombolytic therapy. Materials & methods: Logistic regression analysis was used to determine associations between functional ambulation and thrombolytic therapy. Results & conclusion: In the results, Hispanic ethnicity (odds ratio (OR): 2.808; p = 0.034; 95% CI: 1.08–7.30), high National Institute of Health Stroke Scale (NIHSS) (OR: 1.112; p ≤ 0.001; 95% CI: 1.06–1.17), weakness/paresis (OR: 1.796; p = 0.005; 95% CI: 1.19–2.71), Broca’s aphasia (OR: 1.571; p = 0.003; 95% CI = 1.16–2.12) and antihypertensive medication (OR: 1.530; p = 0.034; 95% CI: 1.03–2.26) were associated with an improved ambulation in patients without thrombolytic therapy. In thrombolytic treated patients, Broca’s aphasia was associated with improved functional outcome.

Quantitative and Qualitative EEG as a Prediction Tool for Outcome and Complications in Acute Stroke Patients

Currently, the relevance of EEG measurements in acute stroke patients is considered low in clinical practice. However, recent studies on the predictive value of EEG measurements after stroke for various outcomes may increase the role of EEG in patients with stroke. We aimed to review the current literature on the utility of EEG measurements after stroke as a tool to predict outcome and complications, focusing on studies in which the EEG measurement was performed in the acute phase after the event and in which long-term outcome measures were reported. In our literature review, we identified 4 different outcome measures (functional outcome, mortality, development of post-stroke cognitive decline, and development of post-stroke epilepsy) where studies on the utility of acute EEG measurements exist. There is a large body of evidence for the prediction of functional outcome, in which a multitude of associated quantitative and qualitative EEG parameters are described. In contrast, only few studies focus on mortality as outcome parameter. We found studies of high methodical quality on the prediction of post-stroke cognitive decline, though the number of patients in these studies often was small. The role of EEG as a prediction tool for seizures and epilepsy after stroke could increase after a recently published study, especially if its result can be incorporated into already existing post-stroke epilepsy prediction tools. In summary, EEG is useful for the prediction of functional outcome, mortality, development of post-stroke cognitive decline and epilepsy, even though there is a discrepancy between the large amount of studies on EEG in acute stroke patients and its underuse in clinical practice.

The Association Between an Antecedent of Transient Ischemic Attack Prior to Onset of Stroke and Functional Ambulatory Outcome

BACKGROUND

Specific clinical risk factors linked to transient ischemic attack (TIA) could affect functional ambulatory outcome following thrombolytic therapy in patients having ischemic stroke with a prior TIA (TIA-ischemic stroke). This issue was investigated in this study.

METHODS

We retrospectively analyzed data from 6379 ischemic stroke patients of which 1387 presented with an antecedent TIA prior to onset of stroke. We used logistic regression model to identify demographic and clinical risk factors that are associated with functional ambulatory outcome in patients with TIA-ischemic stroke treated with thrombolytic therapy.

RESULTS

In a population of TIA-ischemic stroke who received recombinant tissue plasminogen activator, patients with a history of stroke (odds ratio [OR] = 3.229, 95% confidence interval [CI] = 1.494-6.98, P = .003) were associated with increasing odds of improvement in functional ambulation, while the female gender (OR = 0.462, 95% CI = 0.223-0.956, P = .037) was associated with reducing odds of improvement. In the non-TIA group, dyslipidemia (OR = 1.351, 95% CI = 1.026-1.781, P = .032) and blood glucose (OR = 1.003, 95% CI = 1.0-1.005, P = .041) were associated with the increasing odds of improvement while older patients (OR = 0.989, 95% CI = 0.98-0.999, P = .029) with heart failure (OR = 0.513, 95% CI = 0.326-0.808, P = .004) and higher lipid level (OR = 0.834, 95% CI = 0.728-0.955, P = .009) were associated with reducing odds of improvement in ambulation.

CONCLUSION

In a population of TIA-ischemic stroke with thrombolytic therapy and a clearly defined TIA without focal ischemic injury, regardless of associated clinical risk factors, a TIA prior to a stroke is not associated with reducing odds of improved ambulatory outcome, except in female patients with TIA-ischemic stroke.