Predictors of poor cerebral collaterals and cerebrovascular reserve in patients with chronic total carotid occlusion

Rizk, Haytham; Allam, Mahmoud; Hegazy, Ahmed; Khalil, Haytham; Helmy, Hanan; Hashem, Hebatalla S.; Abd‐Allah, Foad

doi:10.1080/00207454.2018.1538990

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…1). Although it was possible to model a number of different interaction terms between included predictors, we focused on three interactions most consistently demonstrated by existing evidence [22,[25][26][27][28][29][30][31].…”

Section: Discussionmentioning

confidence: 99%

“…To add further complexity, cardiovascular risk factors may not only co-occur but also interact, affecting the manifestation of these putative protective mechanisms. Findings from clinical and preclinical models suggest that both hypertension and diabetes may impair the development of collaterals [22,[25][26][27][28][29], while diabetes also prevents ischaemic tolerance [30,31].…”

Section: Introductionmentioning

confidence: 99%

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Cardiovascular risk factors indirectly affect acute post-stroke cognition through stroke severity and prior cognitive impairment: a moderated mediation analysis

et al. 2020

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Background: Cognitive impairment is an important consequence of stroke and transient ischaemic attack, but its determinants are not fully understood. Simple univariable or multivariable models have not shown clinical utility for predicting cognitive impairment. Cardiovascular risk factors may influence cognition through multiple, direct, and indirect pathways, including effects on prior cognition and stroke severity. Understanding these complex relationships may help clinical teams plan intervention and follow-up strategies. Methods: We analysed clinical and demographic data from consecutive patients admitted to an acute stroke ward. Cognitive assessment comprised Abbreviated Mental Test and mini-Montreal Cognitive Assessment. We constructed bias-corrected confidence intervals to test indirect effects of cardiovascular risk factors (hypertension, vascular disease, atrial fibrillation, diabetes mellitus, previous stroke) on cognitive function, mediated through stroke severity and history of dementia, and we assessed moderation effects due to comorbidity. Results: From 594 eligible patients, we included 587 in the final analysis (age range 26-100; 45% female). Our model explained R 2 = 62.10% of variance in cognitive test scores. We found evidence for an indirect effect of previous stroke that was associated with increased risk of prevalent dementia and in turn predicted poorer cognitive score (estimate = − 0.39; 95% bias-corrected CI, − 0.75 to − 0.13; p = 0.02). Atrial fibrillation was associated with greater stroke severity and in turn with a poorer cognitive score (estimate = − 0.27; 95% bias-corrected CI, − 0.49 to − 0.05; p = 0.02). Conversely, previous TIA predicted decreased stroke severity and, through that, lesser cognitive impairment (estimate = 0.38; 95% bias-corrected CI, 0.08 to 0.75; p = 0.02). Through an association with reduced stroke severity, vascular disease was associated with lesser cognitive impairment, conditional on presence of hypertension and absence of diabetes mellitus (estimate = 0.36; 95% bias-corrected CI, 0.03 to 0.68; p = 0.02), although the modelled interaction effects did not reach statistical significance.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cardiovascular risk factors indirectly affect acute post-stroke cognition through stroke severity and prior cognitive impairment: a moderated mediation analysis

et al. 2020

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“…Thus, many of the MRI and CT techniques employed in stroke assessment are not available elsewhere. Perfusion SPECT remains a valuable tool for assessing acute strokes prior to administering intravenous tissue plasminogen activator (IV tPA) ( 129 , 130 ), assessing subacute strokes for viability and size of the penumbra ( 131 , 132 ), and assessing persistent stroke-related symptoms ( 133 ). Perfusion SPECT may also contribute to stroke risk assessment ( 134 , 135 ).…”

Section: Assessment Of the State Of The Art—spect In Brain Disordersmentioning

confidence: 99%

The Legacy of the TTASAAN Report—Premature Conclusions and Forgotten Promises: A Review of Policy and Practice Part I

2022

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Brain perfusion single photon emission computed tomography (SPECT) scans were initially developed in 1970's. A key radiopharmaceutical, hexamethylpropyleneamine oxime (HMPAO), was originally approved in 1988, but was unstable. As a result, the quality of SPECT images varied greatly based on technique until 1993, when a method of stabilizing HMPAO was developed. In addition, most SPECT perfusion studies pre-1996 were performed on single-head gamma cameras. In 1996, the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology (TTASAAN) issued a report regarding the use of SPECT in the evaluation of neurological disorders. Although the TTASAAN report was published in January 1996, it was approved for publication in October 1994. Consequently, the reported brain SPECT studies relied upon to derive the conclusions of the TTASAAN report largely pre-date the introduction of stabilized HMPAO. While only 12% of the studies on traumatic brain injury (TBI) in the TTASAAN report utilized stable tracers and multi-head cameras, 69 subsequent studies with more than 23,000 subjects describe the utility of perfusion SPECT scans in the evaluation of TBI. Similarly, dementia SPECT imaging has improved. Modern SPECT utilizing multi-headed gamma cameras and quantitative analysis has a sensitivity of 86% and a specificity of 89% for the diagnosis of mild to moderate Alzheimer's disease—comparable to fluorodeoxyglucose positron emission tomography. Advances also have occurred in seizure neuroimaging. Lastly, developments in SPECT imaging of neurotoxicity and neuropsychiatric disorders have been striking. At the 25-year anniversary of the publication of the TTASAAN report, it is time to re-examine the utility of perfusion SPECT brain imaging. Herein, we review studies cited by the TTASAAN report vs. current brain SPECT imaging research literature for the major indications addressed in the report, as well as for emerging indications. In Part II, we elaborate technical aspects of SPECT neuroimaging and discuss scan interpretation for the clinician.

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“…Interestingly, the model considered that the history of smoke is a protective factor against MCE and CH after MT, which was contrary to our common sense and some previous research. Smoking severely affects the cerebrovascular reserve and induces intracranial atherosclerotic changes, and it may impair cerebrovascular reactivity and lead to poor collateral circulation ( 44 , 45 ). However, a meta-analysis based on 45,826 AIS patients showed a similar result that smoking was a protective factor against MCE ( 46 ).…”

Section: Discussionmentioning

confidence: 99%

Predicting futile recanalization, malignant cerebral edema, and cerebral herniation using intelligible ensemble machine learning following mechanical thrombectomy for acute ischemic stroke

Zeng

Li²,

Huang³

et al. 2022

Front. Neurol.

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PurposeTo establish an ensemble machine learning (ML) model for predicting the risk of futile recanalization, malignant cerebral edema (MCE), and cerebral herniation (CH) in patients with acute ischemic stroke (AIS) who underwent mechanical thrombectomy (MT) and recanalization.MethodsThis prospective study included 110 patients with premorbid mRS ≤ 2 who met the inclusion criteria. Futile recanalization was defined as a 90-day modified Rankin Scale score >2. Clinical and imaging data were used to construct five ML models that were fused into a logistic regression algorithm using the stacking method (LR-Stacking). We added the Shapley Additive Explanation method to display crucial factors and explain the decision process of models for each patient. Prediction performances were compared using area under the receiver operating characteristic curve (AUC), F1-score, and decision curve analysis (DCA).ResultsA total of 61 patients (55.5%) experienced futile recanalization, and 34 (30.9%) and 22 (20.0%) patients developed MCE and CH, respectively. In test set, the AUCs for the LR-Stacking model were 0.949, 0.885, and 0.904 for the three outcomes mentioned above. The F1-scores were 0.882, 0.895, and 0.909, respectively. The DCA showed that the LR-Stacking model provided more net benefits for predicting MCE and CH. The most important factors were the hypodensity volume and proportion in the corresponding vascular supply area.ConclusionUsing the ensemble ML model to analyze the clinical and imaging data of AIS patients with successful recanalization at admission and within 24 h after MT allowed for accurately predicting the risks of futile recanalization, MCE, and CH.

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Predictors of poor cerebral collaterals and cerebrovascular reserve in patients with chronic total carotid occlusion

Cited by 7 publications

References 11 publications

Cardiovascular risk factors indirectly affect acute post-stroke cognition through stroke severity and prior cognitive impairment: a moderated mediation analysis

Cardiovascular risk factors indirectly affect acute post-stroke cognition through stroke severity and prior cognitive impairment: a moderated mediation analysis

The Legacy of the TTASAAN Report—Premature Conclusions and Forgotten Promises: A Review of Policy and Practice Part I

Predicting futile recanalization, malignant cerebral edema, and cerebral herniation using intelligible ensemble machine learning following mechanical thrombectomy for acute ischemic stroke

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