Factors influencing infarct growth including collateral status assessed using computed tomography in acute stroke patients with large artery occlusion

Abstract

In major ischemic stroke caused by a large artery occlusion, neuronal loss varies considerably across individuals without revascularization. This study aims to identify which patient characteristics are most highly associated with this variability. Demographic and clinical information were retrospectively collected on a registry of 878 patients. Imaging biomarkers including Alberta Stroke Program Early CT score, noncontrast head computed tomography infarct volume, perfusion computed tomography infarct core and penumbra, occlusion site, collateral score, and recanalization status were evaluated on the baseline and early follow-up computed tomography images. Infarct growth rates were calculated by dividing infarct volumes by the time elapsed between the computed tomography scan and the symptom onset. Collateral score was graded into four levels (0, 1, 2, and 3) in comparison with the normal side. Correlation of perfusion computed tomography and noncontrast head computed tomography infarct volumes and infarct growth rates were estimated with the nonparametric Spearman's rank correlation. Conditional inference trees were used to identify the clinical and imaging biomarkers that were most highly associated with the infarct growth rate and modified Rankin Scale at 90 days. Two hundred and thirty-two patients met the inclusion criteria for this study. The median infarct growth rates for perfusion computed tomography and noncontrast head computed tomography were 11.2 and 6.2 ml/log(min) in logarithmic model, and 18.9 and 10.4 ml/h in linear model, respectively. Noncontrast head computed tomography and perfusion computed tomography infarct volumes and infarct growth rates were significantly correlated (rho=0.53; P < 0.001). Collateral status was the strongest predictor for infarct growth rates. For collateral=0, the perfusion computed tomography and noncontrast head computed tomography infarct growth rate were 31.56 and 16.86 ml/log(min), respectively. Patients who had collateral >0 and penumbra volumes>92 ml had the lowest predicted perfusion computed tomography infarct growth rates (6.61 ml/log(min)). Collateral status was closely related to the diversity of infarct growth rates, poor collaterals were associated with a faster infarct growth rates and vice versa.

Collateral status contributes to differences between observed and predicted 24-h infarct volumes in DEFUSE 3

“…We found that patients with infarcts larger than predicted had higher infarct growth rates, which have been shown to be associated with poor collaterals in prior studies, 9 , 10 , 19 while lower growth rates have been shown to be associated with good baseline collaterals. 8 , 19…”

Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100

“…In addition, these models were built on the basis of the hypothesis of a linear relationship between the parameters and the outcome, but some studies have highlighted a nonlinear correlation. 13,14 In comparison with traditional modeling methods, machine learning algorithms have much higher scalability, allowing large numbers of features and parameters to be incorporated into the models. Machine learning models have been trained not only for outcome prediction following intravenous thrombolysis 15 and intra-arterial therapy 16,17 after AIS but also for subtype classification, 18 hemorrhagic transformation, 19 and clot-characteristic identification.…”

Exploration of Machine Learning Techniques to Examine the Journey to Neuroendocrine Tumor Diagnosis with Real-World Data

“…For this study, the decision tree described factors such as having an endoscopic/biopsy procedure and vomiting symptom with the most probable pathway to NET diagnosis. Conditional interference trees have been previously used to identify characteristics associated with infarct growth rate and neurologic disability among ischemic stroke patients and to identify predictors of breast cancer survival [26,27].…”