External Validation of the HATCH (Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus) Score for Prediction of Functional Outcome After Subarachnoid Hemorrhage.

BACKGROUND: The Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus (HATCH) Score has previously shown to predict functional outcome in aneurysmal subarachnoid hemorrhage (aSAH). OBJECTIVE: To validate the HATCH score. METHODS: This is a pooled cohort study including prospective collected data on 761 patients with aSAH from 4 different hospitals. The HATCH score for prediction of functional outcome was validated using calibration and discrimination analysis (area under the curve). HATCH score model performance was compared with the World Federation of Neurosurgical Societies and Barrow Neurological Institute score. RESULTS: At the follow-up of at least 6 months, favorable (Glasgow Outcome Score 4-5) and unfavorable functional outcomes (Glasgow Outcome Score 1-3) were observed in 512 (73%) and 189 (27%) patients, respectively. A higher HATCH score was associated with an increased risk of unfavorable outcome with a score of 1 showing a risk of 1.3% and a score of 12 yielding a risk of 67%. External validation showed a calibration intercept of -0.07 and slope of 0.60 with a Brier score of 0.157 indicating good model calibration and accuracy. With an area under the curve of 0.81 (95% CI 0.77-0.84), the HATCH score demonstrated superior discriminative ability to detect favorable outcome at follow-up compared with the World Federation of Neurosurgical Societies and Barrow Neurological Institute score with 0.72 (95% CI 0.67-0.75) and 0.63 (95% CI 0.59-0.68), respectively. CONCLUSION: This multicenter external validation analysis confirms the HATCH score to be a strong independent predictor for functional outcome. Its incorporation into daily practice may be of benefit for goal-directed patient care in aSAH.

More pronounced hemodynamic alterations in patients with brain arteriovenous malformation-associated epilepsy.

OBJECTIVE: Epileptic seizures in patients with brain arteriovenous malformations (bAVMs) may be caused by hemodynamic alterations due to the complex angioarchitecture of bAVMs. In particular, an arterial steal phenomenon and venous outflow disruption may play an etiological role in seizure development but remain challenging to demonstrate quantitatively. Blood oxygenation level-dependent (BOLD) cerebrovascular reactivity (CVR) imaging is an emerging technique that can measure both arterial steal phenomenon (as a paradoxical BOLD signal decrease during a vasodilatory stimulus) and impaired perinidal BOLD-CVR (which has been found in the presence of venous congestion on conventional angiography in bAVM patients with epilepsy). By applying this innovative BOLD-CVR technique, the aim is to better study CVR patterns and their correlation with morphological features on conventional angiography in patients with bAVM with and without epilepsy. METHODS: Twenty-two patients with unruptured and previously untreated bAVMs (8 with and 14 without epilepsy) were included in this case-control study. Quantitative CVR measurements were derived from BOLD functional MRI volumes using a novel standardized and precise hypercapnic stimulus (i.e., % BOLD/mm Hg CO2). In addition, 22 matched healthy controls underwent an identical BOLD-CVR study. Evaluation of venous congestion was performed on conventional angiography for all patients with bAVM. RESULTS: Patients with bAVM-associated epilepsy showed impaired whole-brain BOLD-CVR compared to those in the nonepilepsy group, even after correction for AVM volume and AVM grade (epilepsy vs nonepilepsy group: 0.17 ± 0.07 vs 0.25 ± 0.07, p = 0.04). A BOLD-CVR-derived arterial steal phenomenon was observed in 2 patients with epilepsy (25%). Venous congestion was noted in 3 patients with epilepsy (38%) and in 1 patient without epilepsy (7%; p = 0.08). CONCLUSIONS: These data suggest that whole-brain CVR impairment, and more pronounced hemodynamic alterations (i.e., arterial steal phenomenon and venous outflow restriction), may be more present in patients with bAVM-associated epilepsy. The association of impaired BOLD-CVR and bAVM-associated epilepsy will need further investigation in a larger patient cohort.

Validation and Optimization of Barrow Neurological Institute Score in Prediction of Adverse Events and Functional Outcome After Subarachnoid Hemorrhage-Creation of the HATCH (Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus) Score.

BACKGROUND: The Barrow Neurological Institute (BNI) score, measuring maximal thickness of aneurysmal subarachnoid hemorrhage (aSAH), has previously shown to predict symptomatic cerebral vasospasms (CVSs), delayed cerebral ischemia (DCI), and functional outcome. OBJECTIVE: To validate the BNI score for prediction of above-mentioned variables and cerebral infarct and evaluate its improvement by integrating further variables which are available within the first 24 h after hemorrhage. METHODS: We included patients from a single center. The BNI score for prediction of CVS, DCI, infarct, and functional outcome was validated in our cohort using measurements of calibration and discrimination (area under the curve [AUC]). We improved it by adding additional variables, creating a novel risk score (measure by the dichotomized Glasgow Outcome Scale) and validated it in a small independent cohort. RESULTS: Of 646 patients, 41.5% developed symptomatic CVS, 22.9% DCI, 23.5% cerebral infarct, and 29% had an unfavorable outcome. The BNI score was associated with all outcome measurements. We improved functional outcome prediction accuracy by including age, BNI score, World Federation of Neurologic Surgeons, rebleeding, clipping, and hydrocephalus (AUC 0.84, 95% CI 0.8-0.87). Based on this model we created a risk score (HATCH-Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus), ranging 0 to 13 points. We validated it in a small independent cohort. The validated score demonstrated very good discriminative ability (AUC 0.84 [95% CI 0.72-0.96]). CONCLUSION: We developed the HATCH score, which is a moderate predictor of DCI, but excellent predictor of functional outcome at 1 yr after aSAH.

Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

OBJECTIVEThe aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH).METHODSThe database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7.RESULTSThe overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission.CONCLUSIONSThe multiple variable analysis capability of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.