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Creation and application of virtual patient cohorts of heart models.

ABSTRACT: Patient-specific cardiac models are now being used to guide therapies. The increased use of patient-specific cardiac simulations in clinical care will give rise to the development of virtual cohorts of cardiac models. These cohorts will allow cardiac simulations to capture and quantify inter-patient variability. However, the development of virtual cohorts of cardiac models will require the transformation of cardiac modelling from small numbers of bespoke models to robust and rapid workflows that can create large numbers of models. In this review, we describe the state of the art in virtual cohorts of cardiac models, the process of creating virtual cohorts of cardiac models, and how to generate the individual cohort member models, followed by a discussion of the potential and future applications of virtual cohorts of cardiac models. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

SUBMITTER: Niederer SA

PROVIDER: S-EPMC7287335 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Creation and application of virtual patient cohorts of heart models.

Niederer S A SA Aboelkassem Y Y Cantwell C D CD Corrado C C Coveney S S Cherry E M EM Delhaas T T Fenton F H FH Panfilov A V AV Pathmanathan P P Plank G G Riabiz M M Roney C H CH Dos Santos R W RW Wang L L

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 20200525 2173

Patient-specific cardiac models are now being used to guide therapies. The increased use of patient-specific cardiac simulations in clinical care will give rise to the development of virtual cohorts of cardiac models. These cohorts will allow cardiac simulations to capture and quantify inter-patient variability. However, the development of virtual cohorts of cardiac models will require the transformation of cardiac modelling from small numbers of bespoke models to robust and rapid workflows that ...[more]

PMID: 32448064

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Project description:BackgroundCurrent ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability.MethodsPatient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics.ResultsWe performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14).ConclusionA novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.

Project description:BACKGROUND:Peer-assisted learning (PAL) refers to a learning activity whereby students of similar academic level teach and learn from one another. Groupe de perfectionnement des habiletés cliniques (Clinical Skills Improvement Group), a student organization at Université Laval, Canada, propelled PAL into the digital era by creating a collaborative virtual patient platform. Medical interviews can be completed in pairs (a student-patient and a student-doctor) through an interactive Web-based application, which generates a score (weighted for key questions) and automated feedback. OBJECTIVES:The aim of the study was to measure the pedagogical impact of the application on the score at medical interview stations at the summative preclerkship Objective Structured Clinical Examination (OSCE). METHODS:We measured the use of the application (cases completed, mean score) in the 2 months preceding the OSCE. We also accessed the results of medical interview stations at the preclerkship summative OSCE. We analyzed whether using the application was associated with higher scores and/or better passing grades (≥60%) at the OSCE. Finally, we produced an online form where students could comment on their appreciation of the application. RESULTS:Of the 206 students completing the preclerkship summative OSCE, 170 (82.5%) were registered users on the application, completing a total of 3133 cases (18 by active user in average, 7 minutes by case in average). The appreciation questionnaire was answered online by 45 students who mentioned appreciating the intuitive, easy-to-use, and interactive design, the diversity of cases, and the automated feedback. Using the application was associated with reduced reported stress, improved scores (P=.04), and improved passing rates (P=.11) at the preclerkship summative OSCE. CONCLUSIONS:This study suggests that PAL can go far beyond small-group teaching, showing students' potential to create helpful pedagogical tools for their peers.

Project description:Apart from their role in the immune defence against pathogens evidence of a role of antimicrobial peptides (AMPs) in autoimmune diseases has accumulated in the past years. The aim of this project was to examine the functional impact of the human cathelicidin LL-37 and the mouse cathelicidin-related AMP (CRAMP) on the pathogenesis of lupus and arthritis. Serum LL-37 and anti-LL-37 levels were measured by ELISA in healthy donors and patients with Systemic Lupus Erythematosus (SLE) and Rheumatoid arthritis (RA). Pristane-induced lupus was induced in female wild type (WT) and cathelicidin-deficient (CRAMP-/-) mice. Serum levels of anti-Sm/RNP, anti-dsDNA, and anti-histone were determined via ELISA, cytokines in sera and peritoneal lavages were measured via Multiplex. Expression of Interferon I stimulated genes (ISG) was determined by real-time PCR. Collagen-induced arthritis (CIA) was induced in male WT and CRAMP-/- mice and arthritis severity was visually scored and analysed histomorphometrically by OsteoMeasure software. Serum levels of anti-LL-37 were higher in SLE-patients compared to healthy donors or patients with RA. However, no correlation to markers of disease activity or organ involvement was observed. No significant differences of autoantibody or cytokine/chemokine levels, or of expression of ISGs were observed between WT and CRAMP-/- mice after pristane-injection. Furthermore, lung and kidney pathology did not differ in the absence of CRAMP. Incidence and severity of CIA and histological parameters (inflammation, cartilage degradation, and bone erosion) were not different in WT and CRAMP-/- mice. Although cathelicidins are upregulated in mouse models of lupus and arthritis, cathelicidin-deficiency did not persistently affect the diseases. Also in patients with SLE, autoantibodies against cathelicidins did not correlate with disease manifestation. Reactivity against cathelicidins in lupus and arthritis could thus be an epiphenomenon caused by extensive overexpression in blood and affected tissues. In addition, other cationic AMPs could functionally compensate for the deficiency of cathelicidins.

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Creation and application of virtual patient cohorts of heart models.

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Creation and application of virtual patient cohorts of heart models.

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