scholarly journals Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia

2019 ◽  
Vol 10 ◽  
Author(s):  
Alejandro Lopez-Perez ◽  
Rafael Sebastian ◽  
M. Izquierdo ◽  
Ricardo Ruiz ◽  
Martin Bishop ◽  
...  
Keyword(s):  
Ventricular Tachycardia ◽  
Clinical Data ◽  
Computational Models

scholarly journals An Accelerated Thrombosis Model for Computational Fluid Dynamics Simulations in Rotary Blood Pumps

2022 ◽  
Author(s):  
Christopher Blum ◽  
Sascha Groß-Hardt ◽  
Ulrich Steinseifer ◽  
Michael Neidlin
Keyword(s):  
Clinical Data ◽  
Computational Models ◽  
Thrombus Formation ◽  
Fluid Dynamic ◽  
Operating Conditions ◽  
Model Parameters ◽  
Dynamic Simulations ◽  
Heartmate Ii ◽  
Blood Pumps ◽  
Rotary Blood Pumps

Abstract Purpose Thrombosis ranks among the major complications in blood-carrying medical devices and a better understanding to influence the design related contribution to thrombosis is desirable. Over the past years many computational models of thrombosis have been developed. However, numerically cheap models able to predict localized thrombus risk in complex geometries are still lacking. The aim of the study was to develop and test a computationally efficient model for thrombus risk prediction in rotary blood pumps. Methods We used a two-stage approach to calculate thrombus risk. The first stage involves the computation of velocity and pressure fields by computational fluid dynamic simulations. At the second stage, platelet activation by mechanical and chemical stimuli was determined through species transport with an Eulerian approach. The model was compared with existing clinical data on thrombus deposition within the HeartMate II. Furthermore, an operating point and model parameter sensitivity analysis was performed. Results Our model shows good correlation (R2 > 0.93) with clinical data and identifies the bearing and outlet stator region of the HeartMate II as the location most prone to thrombus formation. The calculation of thrombus risk requires an additional 10–20 core hours of computation time. Conclusion The concentration of activated platelets can be used as a surrogate and computationally low-cost marker to determine potential risk regions of thrombus deposition in a blood pump. Relative comparisons of thrombus risk are possible even considering the intrinsic uncertainty in model parameters and operating conditions.

scholarly journals Effects of Fibrosis Morphology on Reentrant Ventricular Tachycardia Inducibility and Simulation Fidelity in Patient-Derived Models

2014 ◽  
Vol 8s1 ◽  
pp. CMC.S15712 ◽  
Author(s):  
Jordan Ringenberg ◽  
Makarand Deo ◽  
David Filgueiras-Rama ◽  
Gonzalo Pizarro ◽  
Borja Ibañez ◽  
...  
Keyword(s):  
Ventricular Tachycardia ◽  
Computational Models ◽  
Border Zone ◽  
Patient Specific ◽  
Narrow Channels ◽  
Simulation Fidelity ◽  
Strong Indicator ◽  
Significant Step ◽  
Magnetic Resonance Imaging Mri

Myocardial fibrosis detected via delayed-enhanced magnetic resonance imaging (MRI) has been shown to be a strong indicator for ventricular tachycardia (VT) inducibility. However, little is known regarding how inducibility is affected by the details of the fibrosis extent, morphology, and border zone configuration. The objective of this article is to systematically study the arrhythmogenic effects of fibrosis geometry and extent, specifically on VT inducibility and maintenance. We present a set of methods for constructing patient-specific computational models of human ventricles using in vivo MRI data for patients suffering from hypertension, hypercholesterolemia, and chronic myocardial infarction. Additional synthesized models with morphologically varied extents of fibrosis and gray zone (GZ) distribution were derived to study the alterations in the arrhythmia induction and reentry patterns. Detailed electrophysiological simulations demonstrated that (1) VT morphology was highly dependent on the extent of fibrosis, which acts as a structural substrate, (2) reentry tended to be anchored to the fibrosis edges and showed transmural conduction of activations through narrow channels formed within fibrosis, and (3) increasing the extent of GZ within fibrosis tended to destabilize the structural reentry sites and aggravate the VT as compared to fibrotic regions of the same size and shape but with lower or no GZ. The approach and findings represent a significant step toward patient-specific cardiac modeling as a reliable tool for VT prediction and management of the patient. Sensitivities to approximation nuances in the modeling of structural pathology by image-based reconstruction techniques are also implicated.

Use of deep learning frameworks to detect super-responder and super-survivor stage IV squamous non-small-cell lung cancer (NSCLC) patients treated with a gemcitabine and cisplatin combination.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1548-1548
Author(s):  
Carlos Maria Galmarini ◽  
Maximiliano Lucius
Keyword(s):  
Deep Learning ◽  
Clinical Data ◽  
Concomitant Medication ◽  
Computational Models ◽  
Response Rate ◽  
Stage Iv ◽  
Phase Iii ◽  
Term Survival ◽  
Learning Frameworks ◽  
Grade Iii

1548 Background: Synthetic fingerprints integrate clinical data within computational models allowing the identification of particular clinical subpopulations at a given moment. We here describe a deep learning strategy to detect super-responder and super-survivor patients with squamous NSCLC by setting up synthetic fingerprints and using unsupervised deep learning frameworks (UDLF). Methods: Through www.projectdatasphere.org, we accessed the control arm clinical data (N = 548) of the randomised phase III SQUIRE trial (NCT00981058). This trial included patients with stage IV squamous NSCLC who had not received previous chemotherapy. These patients were treated with gemcitabine 1,250 mg/m2 (IV, 30-min infusion, d1/d8) and cisplatin 75 mg/m2 (IV, 120 min infusion, d1) on a 3-week cycle for a maximum of six cycles. Synthetic fingerprints resulted of the integration of 180 features collected during the first 3 cycles including demographics, medical history, physical exam, concomitant medication, histopathology, PK parameters, adverse events and common labs. These fingerprints were used as input for the UDLF. The resultant clusters were correlated with overall-response rate (ORR) and overall survival (OS). Results: After missing data removal and feature standardization, 192 patients were eligible for the study. The UDLF was able to generate two different clusters: P0 (n = 107) and P1 (n = 84). ORR was higher in the P1 than in the P0 cluster (mean 41.6% [95% CI 31.7-52.3] vs. 28.0% [95% CI 20.4-37.2]; p = 0.04). OS was significantly longer in the P1 than in the P0 cluster (median 13.2 months vs. 9.7 months; hazard ratio 1.56 [95% CI 1.12-2.17; p = 0.008]). Feature contribution analysis showed that P1 had more patients and more events of grade III/IV neutropenia. In contrast, P0 had more patients and more events of grade III/IV nausea and vomiting. Other major differences were observed on vital signs (SBP, DBP, HR, RR, Temp), concomitant medication (osmotically-active laxatives, dexamethasone, furosemide, granisetron and ondansetron) and in hematological (RBC, HGB, HCT, MCV, WBC, neutrophils, monocytes, lymphocytes) and biochemistry (albumin, globulins, ALP, LDH, creatinine, BUN, urea, sodium, magnesium and phosphate) tests. Conclusions: Our findings show that synthetic fingerprints and subsequent deep learning analysis can be of use to identify patients with clinical characteristics associated with high-response rate and long-term survival.

scholarly journals Computational Models for Diagnosing and Treating Endometriosis

2021 ◽  
Vol 3 ◽  
Author(s):  
Wangui Mbuguiro ◽  
Adriana Noemi Gonzalez ◽  
Feilim Mac Gabhann
Keyword(s):  
Epithelial Cells ◽  
Computational Modeling ◽  
Clinical Data ◽  
Immune Cells ◽  
Computational Models ◽  
Systems Pharmacology ◽  
Biological Mechanisms ◽  
Stromal Fibroblasts ◽  
Disease Symptoms ◽  
Quantitative Systems Pharmacology

Endometriosis is a common but poorly understood disease. Symptoms can begin early in adolescence, with menarche, and can be debilitating. Despite this, people often suffer several years before being correctly diagnosed and adequately treated. Endometriosis involves the inappropriate growth of endometrial-like tissue (including epithelial cells, stromal fibroblasts, vascular cells, and immune cells) outside of the uterus. Computational models can aid in understanding the mechanisms by which immune, hormone, and vascular disruptions manifest in endometriosis and complicate treatment. In this review, we illustrate how three computational modeling approaches (regression, pharmacokinetics/pharmacodynamics, and quantitative systems pharmacology) have been used to improve the diagnosis and treatment of endometriosis. As we explore these approaches and their differing detail of biological mechanisms, we consider how each approach can answer different questions about endometriosis. We summarize the mathematics involved, and we use published examples of each approach to compare how researchers: (1) shape the scope of each model, (2) incorporate experimental and clinical data, and (3) generate clinically useful predictions and insight. Lastly, we discuss the benefits and limitations of each modeling approach and how we can combine these approaches to further understand, diagnose, and treat endometriosis.

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