Predicting the Lack of Development of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

Crobeddu, Emanuela; Mittal, Manoj; Dupont, Stefan A.; Wijdicks, Eelco F. M.; Lanzino, Giuseppe; Rabinstein, Alejandro A.

doi:10.1161/strokeaha.111.638403

Cited by 54 publications

(44 citation statements)

References 33 publications

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“…A recent study found exactly the same predictors (good clinical condition on admission, small amounts of extravasated blood, and older age) to be associated with a low risk of DCI. 8 However, because the authors chose to predict 100% absence of *Both models are after adjustment for overfitting by shrinkage (regression coefficients were shrunk by 16% in the first model, and 21% in the second model).…”

Section: Discussionmentioning

confidence: 99%

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Early Prediction of Delayed Cerebral Ischemia After Subarachnoid Hemorrhage

Rooij

Greving

Rinkel

et al. 2013

Stroke

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All patients were derived from the prospectively collected cohort of SAH patients admitted to our hospital. The development cohort originated from a previously described study population of patients admitted between January 1999 and June 2007.9 Three-hundred seventy-one patients met the following inclusion criteria (1) SAH confirmed by computed tomography (CT) or lumbar puncture; (2) aneurysm proven by means of CT/MR-or catheter angiography; (3) admitted within 3 days after onset; (4) at least 1 follow-up scan performed >24 hours after the initial scan; (5) initial CT scan available for review; (6) absence of a large intracerebral hemorrhage Background and Purpose-To develop and validate a risk chart for prediction of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage based on admission characteristics. Methods-For derivation of the risk chart, we studied data from 371 prospectively collected consecutive subarachnoid hemorrhage patients with a confirmed aneurysm admitted between 1999 and 2007. For its validation we similarly studied 255 patients admitted between 2007 and 2009. The predictive value of admission characteristics was tested in logistic regression models with delayed cerebral ischemia-related infarction as primary outcome. Procedure-related infarctions were not included. Performance of the models was tested by discrimination and calibration. On the basis of these models, a risk chart was developed for application in clinical practice. Results-The strongest predictors were clinical condition on admission, amount of blood on computed tomography (both cisternal and intraventricular) and age. A model that combined these 4 predictors had an area under the receiver operating characteristic curve of 0.63 (95% confidence interval, 0.57-0.69). This model improved little by including current smoking and hyperglycemia on admission (area under the receiver operating characteristic curve, 0.65; 95% confidence interval, 0.59-0.71). The risk chart predicted risks of delayed cerebral ischemia-related infarction varying from 12% to 61%. Both low risk (<20% risk) and high risk (>40% risk) were predicted in ≈20% of the patients. Validation confirmed that the discriminative ability was adequate (area under the receiver operating characteristic curve, 0.69; 95% confidence interval, 0.61-0.77). Conclusions-Absolute risks of delayed cerebral ischemia-related infarction can be reliably estimated by a simple risk chart that includes clinical condition on admission, amount of blood on computed tomography (both cisternal and intraventricular), and age. (Stroke. 2013;44:1288-1294.)

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Section: Discussionmentioning

confidence: 99%

“…5 Previous prediction models for DCI after SAH are impractical in daily practice or not confined to data present at admission, and most are not validated. [6][7][8] Furthermore, a risk prediction chart does not exist.…”

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confidence: 99%

Early Prediction of Delayed Cerebral Ischemia After Subarachnoid Hemorrhage

Rooij

Greving

Rinkel

et al. 2013

Stroke

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“…24 The VASOGRADE derived from the studies published by de Rooij et al 15 and Crobeddu et al, 16 who developed risk charts to predict who would or would not develop DCI, respectively, combining the WFNS 9 and the modified Fisher scales.…”

Section: Discussionmentioning

confidence: 99%

“…The main idea was to create an ease-to-remember, ease-to-use, and practical grading system that would allow clinicians to tailor monitoring strategies and treatment aggressiveness according to the different grades. The VASOGRADE derived from previously published studies by de Rooij et al 15 and Crobeddu et al, 16 which showed clinical condition on admission and the amount of blood on CT (associated with age), as the major risk factors for DCI and DCI-related cerebral infarction. This analysis represents, therefore, the external validation of de Rooij et al, 15 in a large and diverse sample of SAH patients.…”

Section: Strokementioning

confidence: 99%

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The VASOGRADE

Manoel¹,

Jaja

Germans³

et al. 2015

Stroke

103

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Background and Purpose— Patients are classically at risk of delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage. We validated a grading scale—the VASOGRADE—for prediction of DCI. Methods— We used data of 3 phase II randomized clinical trials and a single hospital series to assess the relationship between the VASOGRADE and DCI. The VASOGRADE derived from previously published risk charts and consists of 3 categories: VASOGRADE-Green (modified Fisher scale 1 or 2 and World Federation of Neurosurgical Societies scale [WFNS] 1 or 2); VASOGRADE-Yellow (modified Fisher 3 or 4 and WFNS 1–3); and VASOGRADE-Red (WFNS 4 or 5, irrespective of modified Fisher grade). The relation between the VASOGRADE and DCI was assessed by logistic regression models. The predictive accuracy of the VASOGRADE was assessed by receiver operating characteristics curve and calibration plots. Results— In a cohort of 746 patients, the VASOGRADE significantly predicted DCI ( P <0.001). The VASOGRADE-Yellow had a tendency for increased risk for DCI (odds ratio [OR], 1.31; 95% CI, 0.77–2.23) when compared with VASOGRADE-Green; those with VASOGRADE-Red had a 3-fold higher risk of DCI (OR, 3.19; 95% CI, 2.07–4.50). Studies were not a significant confounding factor between the VASOGRADE and DCI. The VASOGRADE had an adequate discrimination for prediction of DCI (area under the receiver operating characteristics curve=0.63) and good calibration. Conclusions— The VASOGRADE results validated previously published risk charts in a large and diverse sample of subarachnoid hemorrhage patients, which allows DCI risk stratification on presentation after subarachnoid hemorrhage. It could help to select patients at high risk of DCI, as well as standardize treatment protocols and research studies.

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