In acute ischemic stroke patients with smoking incidence, are more women than men more likely to be included or excluded from thrombolysis therapy?

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.

Metabolomic Profiles of Men and Women Ischemic Stroke Patients

Background: Stroke is known to affect both men and women; however, incidence and outcomes differ between them. Therefore, the discovery of novel, sex-specific, blood-based biomarkers for acute ischemic stroke (AIS) patients has the potential to enhance the understanding of the etiology of this deadly disease in the content of sex. The objective of this study was to identify serum metabolites associated with male and female AIS patients. Methods: Metabolites were measured with the use of untargeted, reverse-phase ultra-performance liquid chromatography-tandem mass spectrometry quantification from blood specimens collected from AIS patients. Samples were collected from 36 patients comprising each of 18 men and women with matched controls. Metabolic pathway analysis and principal component analysis (PCA) was used to differentiate metabolite profiles for male and female AIS patients from the control, while logistic regression was used to determine differences in metabolites between male and female AIS patients. Results: In female AIS patients, 14 distinct altered metabolic pathways and 49 corresponding metabolites were identified, while 39 metabolites and 5 metabolic pathways were identified in male patients. Metabolites that are predictive of ischemic stroke in female patients were 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4) (AUC = 0.914, 0.765–1.000), 1-(1-enyl-palmitoyl)-2-palmitoyl-GPC (P-16:0/16:0) (AUC = 0.840, 0.656–1.000), and 5,6-dihydrouracil (P-16:0/20:2) (AUC = 0.815, 0.601–1.000). Significant metabolites that were predictive of stroke in male patients were 5alpha-androstan-3alpha,17beta-diol disulfate (AUC = 0.951, 0.857–1.000), alpha-hydroxyisocaproate (AUC = 0.938, 0.832–1.000), threonate (AUC = 0.877, 0.716–1.000), and bilirubin (AUC = 0.817, 0.746–1.000). Conclusions: In the current study, the untargeted serum metabolomics platform identified multiple pathways and metabolites associated with male and female AIS patients. Further research is necessary to characterize how these metabolites are associated with the pathophysiology in male and female AIS patients.

Obstructive sleep apnea and stroke severity: Impact of clinical risk factors

BACKGROUND

Specific clinical and demographic risk factors may be associated with improving or worsening neurologic outcomes within a population of acute ischemic stroke (AIS) patients with a history of obstructive sleep apnea (OSA). The objective of this study was to determine the changes in neurologic outcome during a 14-day recovery as it relates to initial stroke severity in AIS patients with OSA.

METHODS

This retrospective study analyzed baseline clinical risk factors and demographic data collected in a regional stroke center from January 2010 to June 2016. Our primary endpoint measure was the National Institutes of Health Stroke Scale (NIHSS) score and our secondary endpoint measures included the clinical factors associated with improving (NIHSS score ≤7) or worsening (NIHSS score >7) neurological outcome. The relative contribution of each variable to stroke severity and related outcome was determined using a logistic regression. The regression models were checked for the overall correct classification percentage using a Hosmer-Lemeshow test, and the sensitivity of our models was determined by the area under the receiver operating characteristic curve.

RESULTS

A total of 5469 AIS patients were identified. Of this, 96.89% did not present with OSA while 3.11% of AIS patients presented with OSA. Adjusted multivariate analysis demonstrated that in the AIS population with OSA, atrial fibrillation (AF) (odds ratio [OR] = 3.36, 95% confidence interval [CI], 1.289-8.762, P = 0.013) and changes in ambulatory status (OR = 2.813, 95% CI, 1.123-7.041, P = 0.027) showed an association with NIHSS score >7 while being Caucasian (OR = 0.214, 95% CI, 0.06-0.767, P = 0.018) was associated with NIHSS score ≤7.

CONCLUSION

In AIS patients with OSA, AF and changes in ambulatory status were associated with worsening neurological outcome while Caucasian patients were associated with improving neurological outcome. Our findings may have significant implications for patient stratification when determining treatment protocols with respect to neurologic outcomes in AIS patients with OSA.

Gender differences in comorbidities and risk factors in ischemic stroke patients with a history of atrial fibrillation

BACKGROUND

Atrial Fibrillation (AF) is a common cardiac arrhythmia and has been identified as a major risk factor for acute ischemic stroke (AIS). Gender differences in the disease process, causative mechanisms and outcomes of AF have been investigated. In the current study, we determined whether there is a gender-based disparity in AIS patients with baseline AF, and whether such a discrepancy is associated with specific risk factors and comorbidities.

METHODS

Baseline factors including comorbidities, risk and demographic factors associated with a gender difference were examined using retrospective data collected from a registry from January 2010 to June 2016 in a regional stroke center. Univariate analysis was used to differentiate between genders in terms of clinical risk factors and demographics. Variables in the univariate analysis were further analyzed using logistic regression. The adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for each factor were used to predict the increasing odds of an association of a specific comorbidity and risk factor with the male or female AIS with AF.

RESULTS

In the population of AIS patients with AF, a history of drug and alcohol use (OR = 0.250, 95% CI, 0.497-1.006, P = 0.016), sleep apnea (OR = 0.321, 95% CI, 0.133-0.777, P = 0.012), and higher serum creatinine (OR = 0.693, 95% CI, 0.542-0.886 P = 0.003) levels were found to be significantly associated with the male gender. Higher levels of HDL-cholesterol (OR = 1.035, 95% CI, 1.020-1.050, P < 0.001), LDL-cholesterol (OR = 1.006, 95% CI, 1.001-1.011, P = 0.012), and the inability to ambulate on admission to hospital (OR = 2.258, 95% CI, 1.368-3.727, P = 0.001) were associated with females.

CONCLUSION

Our findings reveal that in the AIS patients with atrial fibrillation, migraines, HDL, LDL and poor ambulation were associated with females, while drugs and alcohol, sleep apnea, and serum creatinine level were associated with male AIS patients with AF. Further studies are necessary to determine whether gender differences in risk factor profiles and commodities require consideration in clinical practice when it comes to AF as a risk factor management in AIS patients.