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Force characterization of intracranial endovascular embolization: coil type, microcatheter placement, and insertion rate.

ABSTRACT: Intraoperative rupture (IOR) is a rare, but potentially morbid complication of endovascular aneurysm coil embolization. Yet, IOR predictors have remained relatively uninvestigated in relation to coil design.To develop a novel in vitro aneurysm model to characterize forces exerted by coils of different design on the aneurysm during endovascular embolization that are hypothesized to contribute to IOR.A 3-mm saccular aneurysm model was developed with flat latex membrane at the dome apex. Membrane deflection was observed throughout simulated embolization and converted to force measurement. Simultaneous coil insertion and force measurement were accomplished with a compression strength-testing machine. Membrane and insertion forces across coil type, microcatheter tip placement, and insertion rate were evaluated.Insertion force and force directly on the aneurysm wall exhibited a difference, with framing coils exerting greatest force, followed by filling and finishing coils. Regarding microcatheter placement, a similar graded response in membrane and insertion forces was observed with positioning in the top-third of the aneurysm generating the greatest force compared with central and bottom-third placement. Insertion rate was also a factor with the slowest rate (10 mm/min) exhibiting the greatest membrane force, followed by lower forces at 30 and 50 mm/min. A multiple linear regression model was created to assess the contributions of each factor toward aneurysm forces.Increased force on the aneurysm is associated with framing coil use, microcatheter placement proximal to aneurysm dome, and slow insertion rate. Further characterization remains necessary to reduce IOR risk, especially concerning the contributions of insertion rate.

SUBMITTER: Lamano JB

PROVIDER: S-EPMC4237704 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Project description:ObjectivePeriprocedural thromboembolic events are a serious complication associated with coil embolization of unruptured intracranial aneurysms. However, no established clinical rule for predicting thromboembolic events exists. This study aimed to clarify the significance of adding preoperative clopidogrel response value to clinical factors when predicting the occurrence of thromboembolic events during/after coil embolization and to develop a nomogram for thromboembolic event prediction.MethodsIn this prospective, single-center, cohort study, we included 345 patients undergoing elective coil embolization for unruptured intracranial aneurysm. Thromboembolic event was defined as the occurrence of intra-procedural thrombus formation and postprocedural symptomatic cerebral infarction within 7 days. We evaluated preoperative clopidogrel response and patients' clinical information. We developed a patient-clinical-information model for thromboembolic event using multivariate analysis and compared its efficiency with that of patient-clinical-information plus preoperative clopidogrel response model. The predictive performances of the two models were assessed using area under the receiver-operating characteristic curve (AUC-ROC) with bootstrap method and compared using net reclassification improvement (NRI) and integrated discrimination improvement (IDI).ResultsTwenty-eight patients experienced thromboembolic events. The clinical model included age, aneurysm location, aneurysm dome and neck size, and treatment technique. AUC-ROC for the clinical model improved from 0.707 to 0.779 after adding the clopidogrel response value. Significant intergroup differences were noted in NRI (0.617, 95% CI: 0.247-0.987, p < .001) and IDI (0.068, 95% CI: 0.021-0.116, p = .005).ConclusionsEvaluation of preoperative clopidogrel response in addition to clinical variables improves the prediction accuracy of thromboembolic event occurrence during/after coil embolization of unruptured intracranial aneurysm.

Project description:BackgroundCoil embolization is a common method for treating unruptured intracranial aneurysms (UIAs). To effectively perform coil embolization for UIAs, clinicians must undergo extensive training with the assistance of senior physicians over an extended period. This study aimed to establish a deep-learning system for measuring the morphological features of UIAs and help the surgical planning of coil embolization for UIAs.MethodsPreoperative computational tomography angiography (CTA) data and surgical data from UIA patients receiving coil embolization in our medical institution were retrospectively reviewed. A convolutional neural network (CNN) model was trained on the preoperative CTA data, and the morphological features of UIAs were measured automatically using this CNN model. The intraclass correlation coefficient (ICC) was utilized to examine the similarity between the morphologies measured by the CNN model and those determined by experienced clinicians. A deep neural network model to determine the diameter of first coil was further established based on the CNN model within the derivation set (75% of all patients) using neural factorization machines (NFM) model and was validated using a validation set (25% of all patients). The general match ratio (the difference was within ± 1 mm) between the predicted diameter of first coil by model and that used in practical scenario was calculated.ResultsOne-hundred fifty-three UIA patients were enrolled in this study. The CNN model could diagnose UIAs with an accuracy of 0.97. The performance of this CNN model in measuring the morphological features of UIAs (i.e., size, height, neck diameter, dome diameter, and volume) was comparable to the accuracy of senior clinicians (all ICC > 0.85). The diameter of first coil predicted by the model established based on CNN model and the diameter of first coil used actually exhibited a high general match ratio (0.90) within the derivation set. Moreover, the model performed well in recommending the diameter of first coil within the validation set (general match ratio as 0.91).ConclusionThis study presents a deep-learning system which can help to improve surgical planning of coil embolization for UIAs.

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Force characterization of intracranial endovascular embolization: coil type, microcatheter placement, and insertion rate.

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