Abstract TP388: Integrating standard-of-care clinical stroke workup within in silico embolic stroke models for etiology disambiguation
Conference Proceeding
Overview
abstract
; Introduction:; Embolic Stroke of Undetermined Source (ESUS) accounts for a critical proportion of all ischemic strokes. Disambiguating embolism etiology is important to improve treatment efficacy and reduce recurrent events. Patient-specific in silico models can shed valuable insights on embolus source-destination mapping. This requires reliable and accurate pre/post-stroke hemodynamic models, which benefit from integrating multiple modes of patient information from imaging and clinical records. This is a major state-of-the-art challenge. Here, we present a workflow for multi-modal data integration from standard-of-care workup towards recreating a data-rich digital twin of stroke patients.; ; ; Methods:; Our workflow integrates non-contrast and contrast-enhanced head-neck CT and cardiac CT, trans-thoracic echo, and perfusion imaging, along with clinical variables such as HR, systolic/diastolic volumes, and stroke locations (with NIHSS scores). Quantitative data from these sources are then integrated into a hemodynamic model by processing features such as arterial structure, inlet flow, tuned resistance boundary conditions, cardiac timing, and stroke location. Resulting hemodynamic data was used to further simulate embolus movement towards stroke site. Statistical sampling simulations using this model were conducted to evaluate the likelihood that an occlusion location corresponded to cardiogenic, aortogenic, or other arterial sources.; ; ; Results:; We present our complete in silico workflow, and demonstrate the outcomes using a small cohort of 5 patients acquired from a clinical database (anonymized, IRB exempt). We demonstrate that the workflow yields high-resolution space-time varying patient hemodynamic patterns. Additionally, the embolus source-destination likelihood mapping provides detailed quantitative insights on the embolism etiology in these stroke patients. These findings indicate that our workflow and resulting digital twins can be a valuable tool in addressing the current clinical challenges in discerning embolism etiology in ESUS cases.; ; ; Conclusions:; We introduce a pipeline of transforming raw patient-specific information from multi-modal imaging and clinical parameters into a cohesive, data-rich in silico model for embolic stroke comprising the full heart-to-brain pathway. This offers a flexible digital twin approach for elucidating stroke etiologies in patient-specific scenarios.;
