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

Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated

PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established (CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma. METHODS: DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases. RESULTS: Both CNV- and methylation family-based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively). CONCLUSION: Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction

Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated.

PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established (CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma. METHODS: DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases. RESULTS: Both CNV- and methylation family-based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively). CONCLUSION: Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction