R ₂ eD AVM Score

Abstract

Background and Purpose— The management of unruptured brain arteriovenous malformations remains unclear. Using a large cohort to determine risk factors predictive of hemorrhagic presentation of arteriovenous malformations, this study aims to develop a predictive tool that could guide hemorrhage risk stratification. Methods— A database of 789 arteriovenous malformation patients presenting to our institution between 1990 and 2017 was used. A hold-out method of model validation was used, whereby the data was randomly split in half into training and validation data sets. Factors significant at the univariable level in the training data set were used to construct a model based on multivariable logistic regression. Model performance was assessed using receiver operating curves on the training, validation, and complete data sets. The predictors and the complete data set were then used to derive a risk prediction formula and a practical scoring system, where every risk factor was worth 1 point except race, which was worth 2 points (total score varies from 0 to 6). The factors are summarized by R 2 eD arteriovenous malformation (acronym: R 2 eD AVM). Results— In 755 patients with complete data, 272 (36%) presented with hemorrhage. From the training data set, a model was derived containing the following risk factors: nonwhite race (odds ratio [OR]=1.8; P =0.02), small nidus size (OR=1.47; P =0.14), deep location (OR=2.3; P< 0.01), single arterial feeder (OR=2.24; P< 0.01), and exclusive deep venous drainage (OR=2.07; P =0.02). Area under the curve from receiver operating curve analysis was 0.702, 0.698, and 0.685 for the training, validation, and complete data sets, respectively. In the entire study population, the predicted probability of hemorrhagic presentation increased in a stepwise manner from 16% for patients with no risk factors (score of 0) to 78% for patients having all the risk factors (score of 6). Conclusions— The final model derived from this study can be used as a predictive tool that supplements clinical judgment and aids in patient counseling.

Development and Validation of a Scoring System for Hemorrhage Risk in Brain Arteriovenous Malformations

“…Survival analyses were conducted using the rms, version 6.3.0, 19 and survival, version 3.4.0, 20 packages for R software (R Foundation for Statistical Computing). Parameters in the R 2 eD score (except for race and ethnicity because the study population included only Chinese patients) were used to fit the model in our training set (data from the derivation cohort), and the DeLong test 14 was conducted to compare the ROCs between the R 2 eD-based model and our model. Data from the 3 cohorts were also used for external validation of the R 2 eD score to investigate its generalizability beyond its majority White population.…”

“…The R 2 eD score developed by Feghali et al 14 was the first scale to estimate the rupture risk of AVMs and was externally validated by Bird et al 15 This scoring system identified 5 risk factors from statistical models (race [non-White], exclusive deep location, small AVM size, exclusive deep drainage, and monoarterial feeding). However, this scale should be applied and interpreted with caution due to its cross-sectional data–based selection of risk factors and its lack of prospective validation.…”

Bibliometric and visual analysis of cerebral revascularization from 1999 to 2022

“…or "STA-MCA bypass" is the most important neurosurgical treatment in the field of cerebral revascularization, and the majority of them have significant effects on the treatment and prognosis of moyamoya disease, for instance, the safety of STA-MCA bypass in the treatment of moyamoya disease (Liu et al, 2017), the hemodynamic problems of STA-MCA bypass in the treatment of moyamoya disease (Jeffree and Stoodley, 2009), the significance of Color Doppler monitoring of STA-MCA bypass in the treatment of moyamoya disease (Kawamata et al, 2011;Feghali et al, 2019). However, we employed keyword clustering analysis to determine the tight association within the cerebral revascularization research field.…”

“…In addition, the incidence of cerebral hyperperfusion has been estimated to range from 0.3 to 1.2% (Meyers et al, 2000), and it is regarded as a complication after carotid endarterectomy. Notably, transcranial Doppler (TCD), the only clinical device that can adequately assess cerebral hyperperfusion, provides a cerebral blood flow monitoring technology (Feghali et al, 2019). In addition, a number of studies indicate that, when performing superficial temporal artery/middle cerebral artery bypass surgery in elderly patients with moyamoya disease (Kim et al, 2022;Tsunoda et al, 2022), we must focus on strict blood pressure control and reduce the harmful cascade reaction caused by over perfusion during cerebral revascularization.…”

Machine Learning Application With Quantitative Digital Subtraction Angiography for Detection of Hemorrhagic Brain Arteriovenous Malformations

“…Most relevant studies have focused on finding predictors of BAVM hemorrhage from clinical features [4], [5], [8], [23], [36], [37]. Feghali et al developed a scoring model termed R2eD [36] based on multivariable logistic regression to detect hemorrhagic BAVMs based on race (nonwhite vs. white), exclusive deep location, BAVM size (small vs. large), venous drainage (exclusive deep vs. other), and monoarterial feeding (1 vs. >1 feeding artery). They used the hold-out method, in which half of the data was split as the validation data set to observe if the model was overfitting; the AUCROC was 0.685.…”