Infarct volume and outcome of cerebral ischaemia, a systematic review and meta‐analysis

Meng, Xianbing; Ji, Jianwen

doi:10.1111/ijcp.14773

Cited by 12 publications

(6 citation statements)

References 69 publications

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“…Our study showed that FIV was associated with poor clinical outcome and is an independent predictor of futile recanalization, which is consistent with previous studies 33,34,35,36 . However, the optimal cutoff FIV value is controversial.…”

Section: Discussionsupporting

confidence: 93%

Predictors of futile recanalization in patients with large infarct: a post hoc analysis of the ANGEL-ASPECT trial

Yi,

Huo,

Lin

et al. 2023

Preprint

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Background: Few studies have focused on factors associated with futile recanalization in acute anterior circulation stroke patients with large infarct cores who were treated with modern endovascular therapy (EVT). The aim of this study was to explore the factors associated with futile recanalization in patients with large ischemic strokes. Methods: This is a post hoc analysis of the ANGEL-ASPECT trial. Demographic and clinical characteristics, acute stroke workflow interval times, and imaging characteristics were compared between the futile and meaningful recanalization groups. A favorable outcome was defined as a 90-day mRS score 0-3, successful reperfusion was defined as eTICI 2b, 2c and 3 on final angiogram, and futile recanalization was defined as failure to achieve a favorable outcome despite successful reperfusion. Multivariate analysis was performed to identify the predictors of futile recanalization. Results: One hundred eighty-three patients were included in the final analysis; 91 (49.7%) patients had futile recanalization, and 92 (51.3%) patients had meaningful recanalization. In multivariable logistic regression analysis, older age (age ≥68, OR=3.29, P=0.004), higher NIHSS score (NIHSS ≥16, OR=3.33, P=0.003), diabetes (OR=3.23, P=0.017), larger final volume (FIV ≥174.7, OR=6.79, P<0.001), postoperative respiratory failure (OR=14.56, P=0.01), and female sex (OR=2.78, P=0.01) were independent predictors of futile recanalization. Conclusions: Futile recanalization occurred in approximately half of acute stroke patients with a large infarct core following endovascular treatment. Old age, high baseline NIHSS score, diabetes mellitus, large FIV and respiratory failure were independent predictors of futile recanalization after endovascular therapy for large ischemic strokes. Stroke-related pneumonia control may improve prognosis.

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Section: Discussionsupporting

confidence: 93%

Predictors of futile recanalization in patients with large infarct: a post hoc analysis of the ANGEL-ASPECT trial

Yi,

Huo,

Lin

et al. 2023

Preprint

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“…Patients were divided into two groups based on infarct volume (≤20 cm 3 vs. >20 cm 3 ) ( Meng and Ji, 2021 ) and time since stroke onset (≤14 days vs. >14 days) ( Kanekar et al, 2012 ). According to a meta-analysis of research on predictive factors in ischemic stroke, the infarct volume cut-point of 50 ml is sensitive in differentiating favorable and unfavorable outcomes ( Meng and Ji, 2021 ). On the other hand, restoration of blood–brain barrier, resolution of vasogenic edema, and cleaning up of necrotic tissue begin after 14 days ( Kanekar et al, 2012 ).…”

Section: Methodsmentioning

confidence: 99%

Glymphatic Dysfunction in Patients With Ischemic Stroke

Toh

Siow

2021

Front. Aging Neurosci.

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Objectives: Rodent experiments have provided some insight into the changes of glymphatic function in ischemic stroke. The diffusion tensor image analysis along the perivascular space (DTI-ALPS) method offers an opportunity for the noninvasive investigation of the glymphatic system in patients with ischemic stroke. We aimed to investigate the changes of glymphatic function in ischemic stroke and the factors associated with the changes.Materials and Methods: A total of 50 patients (mean age 56.7 years; 30 men) and 44 normal subjects (mean age 53.3 years; 23 men) who had preoperative diffusion-tensor imaging for calculation of the analysis along the perivascular space (ALPS) index were retrospectively included. Information collected from each patient included sex, age, time since stroke onset, infarct location, hemorrhagic change, infarct volume, infarct apparent diffusion coefficient (ADC), infarct fractional anisotropy (FA), and ALPS index of both hemispheres. Interhemispheric differences in ALPS index (infarct side vs. contralateral normal side) were assessed with a paired t-test in all patients. ALPS index was normalized by calculating ALPS ratios (right-to-left and left-to-right) for comparisons between patients and normal subjects. Comparisons of ALPS ratios between patients and normal subjects were performed using analysis of covariance with adjustments for age and sex. Linear regression analyses were performed to identify factors associated with the ALPS index.Results: In patients, the mean ALPS index ipsilateral to infarct was 1.162 ± 0.126, significantly lower (P < 0.001) than that of the contralateral side (1.335 ± 0.160). The right-to-left ALPS index ratio of patients with right cerebral infarct was 0.84 ± 0.08, significantly lower (P < 0.001) than that of normal subjects (0.95 ± 0.07). The left-to-right ALPS ratio of patients with left cerebral infarct was 0.92 ± 0.09, significantly (P < 0.001) lower than that of normal subjects (1.05 ± 0.08). On multiple linear regression analysis, time since stroke onset (β = 0.794, P < 0.001) was the only factor associated with the ALPS index.Conclusion: The ALPS index showed lower values in ischemic stroke suggesting impaired glymphatic function. Following initial impairment, the ALPS index increased with the time since stroke onset, which is suggestive of glymphatic function recovery.

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“…[98][99][100] Final infarct volume post-AIS is a critical radiographic measure linked to functional outcomes and complications like malignant cerebral edema and herniation. 101 Although imaging in the hyperacute/acute setting may not accurately reflect final infract volume, ML applications on acute MRI sequences have predicted it with high accuracy (AUC: 0.88). 102 ML has also been applied to predict the progression of cerebral edema through automated volumetric analysis of cerebrospinal fluid, with a recent study achieving a 90% accuracy in forecasting malignant cerebral edema requiring surgical intervention or resulting in death.…”

Section: Ischemic Strokementioning

confidence: 99%

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients

Vitt,

Mainali

2024

Semin Neurol

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The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the “black box” nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.

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Infarct volume and outcome of cerebral ischaemia, a systematic review and meta‐analysis

Cited by 12 publications

References 69 publications

Predictors of futile recanalization in patients with large infarct: a post hoc analysis of the ANGEL-ASPECT trial

Predictors of futile recanalization in patients with large infarct: a post hoc analysis of the ANGEL-ASPECT trial

Glymphatic Dysfunction in Patients With Ischemic Stroke

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients

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