Stroke · 2019 · 42 citations · 10 references
MedicineNeurologyNeurosurgeryCerebrovascular NeurosurgeryCerebrovascular SurgeryClinical NeuroscienceAcute Ischemic StrokeCerebrovascular DiseaseCerebrovascular ImagingNeurological AssessmentStrokeStroke ImagingAutomated MeasurementCerebral Atrophy QuantificationDecision-making For Endovascular TreatmentFunctional Independence MeasureFunctional Outcome EvaluationFunctional Outcome MeasurementHounsfield UnitsPrimary Outcome Analysis
Background and Purpose— Methods of identifying ischemic stroke patients with a greater probability of poor outcome following endovascular thrombectomy (EVT) might improve shared treatment decision-making between patients, families, and physicians. We used an objective, automated method to measure cerebral atrophy and investigated whether this was associated with outcome in EVT patients. Methods— Consecutive EVT patients from a single-center registry were studied. CT brain scans were segmented with a combination of a validated U-Net and Hounsfield unit thresholding. Intracranial cerebrospinal fluid (CSF) volume was used as a marker of cerebral atrophy and calculated as a proportion of total intracranial volume. The primary outcome was functional independence, defined as a 3-month modified Rankin Scale score of 0 to 2. Results— Three-hundred sixty EVT patients were included. Functional independence was achieved in 204 (56.7%) patients. The mean±SD CSF volume was 9.0±4.7% of total intracranial volume. Multivariable regression demonstrated that increasing CSF volume was associated with reduced functional independence (OR=0.65 per 5% increase in CSF volume; 95% CI, 0.48–0.89; P =0.007) and higher 3-month modified Rankin Scale scores (common OR, 1.59 per 5% increase in CSF volume; 95% CI, 1.05–2.41; P =0.03). Conclusions— Cerebral atrophy determined by automated measurement of intracranial CSF volume is associated with functional outcome in patients undergoing EVT. If validated in future studies, this simple, objective, and automated imaging marker could potentially be incorporated into decision-support tools to improve shared treatment decision-making.
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