Stasis Imaging Predicts the Risk of Cardioembolic Stroke Related to Acute Myocardial Infarction

Rodríguez-González, Elena; Martínez-Legazpi, Pablo; Mombiela, Teresa; González-Mansilla, Ana; Delgado-Montero, Antonia; Guzmán-De-Villoria, Juan A.; Díaz-Otero, Fernando; Prieto-Arévalo, Raquel; Juárez, Miriam; García del Rey, Maria del Carmen; Fernández-García, Pilar; Flores, Oscar; Postigo, Andrea; Yotti, Raquel; García-Villalba, Manuel; Fernández-Avilés, Francisco; del Álamo, Juan C; Bermejo, Javier

doi:10.1101/2023.09.15.23295650

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…This choice can be straightforward for conditions with a well understood pathophysiology clearly linked to hemodynamics. For instance, blood stasis is an essential factor in thrombogenesis (Watson et al, 2009), and quantifying LV stasis offers routes to translation (Martinez-Legazpi et al, 2018;Rodríguez-González et al, 2023). However, when the link between flow and disease is less well understood, as its relationship with systolic function (Watanabe et al, 2008), the low metrics that can diagnose or risk-stratify subjects are more elusive.…”

Section: Introductionmentioning

confidence: 99%

Deriving Explainable Metrics of Left Ventricular Flow by Reduced-Order Modeling and Classification

Borja,

Martinez-Legazpi,

Nguyen

et al. 2023

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BackgroundExtracting explainable flow metrics is a bottleneck to the clinical translation of advanced cardiac flow imaging modalities. We hypothesized that reduced-order models (ROMs) of intraventricular flow are a suitable strategy for deriving simple and interpretable clinical metrics suitable for further assessments. Combined with machine learning (ML) flow-based ROMs could provide new insight to help diagnose and risk-stratify patients.MethodsWe analyzed 2D color-Doppler echocardiograms of 81 non-ischemic dilated cardiomyopathy (DCM) patients, 51 hypertrophic cardiomyopathy (HCM) patients, and 77 normal volunteers (Control). We applied proper orthogonal decomposition (POD) to build patient-specific and cohort-specific ROMs of LV flow. Each ROM aggregates a low number of components representing a spatially dependent velocity map modulated along the cardiac cycle by a time-dependent coefficient. We tested three classifiers using deliberately simple ML analyses of these ROMs with varying supervision levels. In supervised models, hyperparameter gridsearch was used to derive the ROMs that maximize classification power. The classifiers were blinded to LV chamber geometry and function. We ran vector flow mapping on the color-Doppler sequences to help visualize flow patterns and interpret the ML results.ResultsPOD-based ROMs stably represented each cohort through 10-fold cross-validation. The principal POD mode captured >80% of the flow kinetic energy (KE) in all cohorts and represented the LV filling/emptying jets. Mode 2 represented the diastolic vortex and its KE contribution ranged from <1% (HCM) to 13% (DCM). Semi-unsupervised classification using patient-specific ROMs revealed that the KE ratio of these two principal modes, the vortex-to-jet (V2J) energy ratio, is a simple, interpretable metric that discriminates DCM, HCM, and Control patients. Receiver operating characteristic curves using V2J as classifier had areas under the curve of 0.81, 0.91, and 0.95 for distinguishing HCM vs. Control, DCM vs. Control, and DCM vs. HCM, respectively.ConclusionsModal decomposition of cardiac flow can be used to create ROMs of normal and pathological flow patterns, uncovering simple interpretable flow metrics with power to discriminate disease states, and particularly suitable for further processing using ML.

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Section: Introductionmentioning

confidence: 99%

Deriving Explainable Metrics of Left Ventricular Flow by Reduced-Order Modeling and Classification

Borja,

Martinez-Legazpi,

Nguyen

et al. 2023

Preprint

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Pulmonary vein flow split effects in patient-specific simulations of left atrial flow

Durán

García-Villalba

Martínez‐Legazpi

et al. 2023

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Disruptions to left atrial (LA) blood flow, such as those caused by atrial fibrillation (AF), can lead to thrombosis in the left atrial appendage (LAA) and an increased risk of systemic embolism. LA hemodynamics are influenced by various factors, including LA anatomy and function, and pulmonary vein (PV) inflow conditions. In particular, the PV flow split can vary significantly among and within patients depending on multiple factors. In this study, we investigated how changes in PV flow split affect LA flow transport, focusing on blood stasis in the LAA, using a high-fidelity patient-specific computational fluid dynamics (CFD) model. We analyzed LA anatomies from eight patients with varying atrial function, including three with AF and either a LAA thrombus or a history of TIAs. Using four different flow splits (60/40\% and 55/45\% through right and left PVs, even flow rate, and same velocity through each PV), we found that flow patterns are sensitive to PV flow split variations, particularly in planes parallel to the mitral valve. Changes in PV flow split also had a significant impact on blood stasis and could contribute to increased risk for thrombosis inside the LAA, particularly in patients with AF and previous LAA thrombus or a history of TIAs. Our study highlights the importance of considering patient-specific PV flow split variations when assessing LA hemodynamics and identifying patients at increased risk for thrombosis and stroke.

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Cardiac Stasis Imaging, Stroke and Silent Brain Infarcts in Patients with Non-Ischemic Dilated Cardiomyopathy

Rodríguez-González,

Martínez-Legazpi,

González-Mansilla

et al. 2024

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Background: Cardioembolic stroke is one of the most devastating complications of non-ischemic dilated cardiomyopathy (NIDCM). However, in clinical trials of primary prevention, the benefits of anticoagulation were hampered by the risk of bleeding. If indices of cardiac blood stasis account for the risk of stroke, they may be useful to individualize primary prevention treatments. Methods: We performed a cross-sectional study in patients with NIDCM and no history of atrial fibrillation (AF) from two sources: 1) a prospective enrollment of unselected patients with left ventricular (LV) ejection fraction <45% and 2) a retrospective identification of patients with a history of previous cardioembolic neurological event. The primary endpoint integrated a history of ischemic stroke, transient ischemic attack (TIA), or the presence intraventricular thrombus, or a silent brain infarction (SBI) by imaging. From echocardiography, we calculated blood flow inside the LV and its residence time (RT). The study was registered in ClinicalTrials.gov (NCT03415789). Results: Of the 89 recruited patients, 18 showed a positive primary endpoint: 9 patients had a history stroke or TIA and another 9 were diagnosed with SBIs in the brain imaging. RT performed good to identify the primary endpoint (AUC (95% CI)= 0.75 (0.61-0.89), p= 0.001). A RT > 2.21 cycles showed a sensitivity of 0.88 (0.77-1.00) and specificity of 0.70 (0.10-0.81). When accounting only for identifying a history of stroke or TIA, AUC for RT was 0.92 (0.85-1.00) with and odds ratio= 7.2 (2.3 – 22.3) per cycle, p< 0.001. Conclusions: In patients with NIDCM in sinus rhythm, stasis imaging derived from echocardiographyis is closely related to the burden of stroke. Stasis imaging may be useful to address stroke risk in patients with systolic dysfunction.

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Stasis Imaging Predicts the Risk of Cardioembolic Stroke Related to Acute Myocardial Infarction

Cited by 3 publications

References 39 publications

Deriving Explainable Metrics of Left Ventricular Flow by Reduced-Order Modeling and Classification

Deriving Explainable Metrics of Left Ventricular Flow by Reduced-Order Modeling and Classification

Pulmonary vein flow split effects in patient-specific simulations of left atrial flow

Cardiac Stasis Imaging, Stroke and Silent Brain Infarcts in Patients with Non-Ischemic Dilated Cardiomyopathy

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