Does stress hyperglycemia in diabetic and non-diabetic acute ischemic stroke patients predict unfavorable outcomes following endovascular treatment?

Wang, Zhengyang; Lin, Fan

doi:10.1007/s10072-023-06625-y

Cited by 3 publications

References 36 publications

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A Bayesian network approach for prognostic assessment in acute mild to moderate ischaemic stroke: Insights from a Multicentre Study (Preprint)

Liu,

Mo,

Liu

et al. 2024

Preprint

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BACKGROUND Mild to moderate ischaemic stroke accounts for one-half to two-thirds of all ischaemic stroke hospitalisations worldwide, yet prognostic models to assess functional outcome after stroke remain elusive. OBJECTIVE This study aimed to identify the risk factors and develop a prognostic assessment model for short-term functional outcome in patients with acute mild-to-moderate ischaemic stroke using the Bayesian Network (BN) approach. METHODS Patients participating in a multicentre randomised controlled trial were eligible for inclusion in this study. The least absolute shrinkage and selection operator (LASSO) regression was applied to reduce the dimensionality of the data, and synthetic minority oversampling technology (SMOTE) was used to correct data imbalances. The Bayesian network model was trained using a hill-climbing algorithm to determine the optimal network structure. The area under the receiver operating characteristic curve (AUROC) and the calibration plot were used to assess model performance. RESULTS A total of 2,432 patients were included for analysis, 424（17.43%）patients had unfavorable outcome in 3-month after AIS,. The BN model identified the NIH Stroke Scale (NIHSS) score, age, C-reactive protein (CRP) levels, fasting plasma glucose (FPG), and history of stroke as significant prognostic factors. The BN model outperformed the logistic regression model in accuracy, sensitivity, and F1 score in test sets, with significant differences in the AUROC as per the DeLong test (P < 0.05). The calibration plot showed good agreement between predictions and observations in both the training and test sets. CONCLUSIONS The NIHSS score, age, CRP levels, FPG, and history of stroke are significant predictors of functional outcome in AIS patients. The BN model demonstrates superior predictive performance compared to traditional logistic regression, suggesting its potential as a valuable tool for assessing short-term prognosis in mild-moderate ischaemic stroke patients. CLINICALTRIAL The trial is registered in the Chinese Clinical Trial Registry (http://www. chictr.org.cn/, ChiCTR1900026492) on 12 October 2019.

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A Bayesian network approach for prognostic assessment in acute mild to moderate ischaemic stroke: Insights from a Multicentre Study (Preprint)

Liu,

Mo,

Liu

et al. 2024

Preprint

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Premorbid frailty, stress hyperglycemia ratio, and functional outcome in patients with acute ischemic stroke

Zedde,

Lattanzi,

Pilotto

et al. 2024

Front. Neurol.

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BackgroundFrailty, defined as multidimensional prognostic index (MPI), has been recently identified as strong predictor of disability and mortality in the elderly with acute ischemic stroke (AIS). The stress hyperglycemia ratio (SHR) is a recently introduced biomarker significantly associated with poor outcome in AIS.ObjectivesThis study aimed to investigate in what extent frailty, measured by MPI, and SHR affects the 3-months outcome of patients > 65 years-old with AIS.MethodsConsecutive patients with AIS >65 years-old who underwent intravenous thrombolysis (IVT) from 2015 to 2019 were enrolled in a German and an Italian Stroke Unit. The SHR was calculated by dividing the fasting plasma glucose at admission with glycated hemoglobin. Demographics and clinical premorbid data, stroke-related variables, including baseline and post-treatment NIHSS score were included in a logistic regression model. The 3-months functional outcome was evaluated by using modified Rankin scale (mRS); good outcome was defined as mRS 0–2, poor as mRS ≥ 3.ResultsOne hundred and fifty-five AIS patients were enrolled in the study. Median MPI was 0.19 [0.13–0.31]; 118 (76.1%) patients were classified as “robust” and 37 (23.9%) as “frail.” In regression analysis, age, NIHSS, and MPI demonstrated as the most significant predictor of 3-months good outcome in the whole cohort. In robust patients, SHR values were significantly associated with the outcome.ConclusionsMPI is associated with the 3-months outcome in our cohort, in particular with good outcome. Conversely, SHR seems to be associated with a 3-months poor outcome in “robust” patients but not in frail patients.

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Introducing the Futile Recanalization Prediction Score (FRPS): A Novel Approach to Predict and Mitigate Ineffective Recanalization after Endovascular Treatment of Acute Ischemic Stroke

Shen,

Huasen,

Killingsworth

et al. 2024

Neurology International

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Objective: This study aims to develop and validate the Futile Recanalization Prediction Score (FRPS), a novel tool designed to predict the severity risk of FR and aid in pre- and post-EVT risk assessments. Methods: The FRPS was developed using a rigorous process involving the selection of predictor variables based on clinical relevance and potential impact. Initial equations were derived from previous meta-analyses and refined using various statistical techniques. We employed machine learning algorithms, specifically random forest regression, to capture nonlinear relationships and enhance model performance. Cross-validation with five folds was used to assess generalizability and model fit. Results: The final FRPS model included variables such as age, sex, atrial fibrillation (AF), hypertension (HTN), diabetes mellitus (DM), hyperlipidemia, cognitive impairment, pre-stroke modified Rankin Scale (mRS), systolic blood pressure (SBP), onset-to-puncture time, sICH, and NIHSS score. The random forest model achieved a mean R-squared value of approximately 0.992. Severity ranges for FRPS scores were defined as mild (FRPS < 66), moderate (FRPS 66–80), and severe (FRPS > 80). Conclusions: The FRPS provides valuable insights for treatment planning and patient management by predicting the severity risk of FR. This tool may improve the identification of candidates most likely to benefit from EVT and enhance prognostic accuracy post-EVT. Further clinical validation in diverse settings is warranted to assess its effectiveness and reliability.

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Does stress hyperglycemia in diabetic and non-diabetic acute ischemic stroke patients predict unfavorable outcomes following endovascular treatment?

Cited by 3 publications

References 36 publications

A Bayesian network approach for prognostic assessment in acute mild to moderate ischaemic stroke: Insights from a Multicentre Study (Preprint)

A Bayesian network approach for prognostic assessment in acute mild to moderate ischaemic stroke: Insights from a Multicentre Study (Preprint)

Premorbid frailty, stress hyperglycemia ratio, and functional outcome in patients with acute ischemic stroke

Introducing the Futile Recanalization Prediction Score (FRPS): A Novel Approach to Predict and Mitigate Ineffective Recanalization after Endovascular Treatment of Acute Ischemic Stroke

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