V

Paula Mira López & Mercedes Caballero García

Modelling approaches- IMPLANTABLE CARDIOVERTER DEFIBRILLATOR (ICD)

conclusions

Steps for asme v&v 40 guidelines

modeling approaches

ChoOsing a medical device

citations

INDEX

CHOOSING A MEDICAL DEVICE

• The ICD is surgically implanted near the heart to continuously monitor the heart's electrical activity to detect fibrillations

• Delivers a powerful electric shock to the heart, (30 to 40J at a voltage of 800 V) . This shock aims to reset the heart’s electrical system

• The ICD can differentiate between various types of arrhythmias and determine the most appropriate response, whether that be a shock or a pacing intervention

• It is a vital tool in cardiac care, providing life-saving treatment for patients at risk of severe arrhythmias

implantable cardioverter desfrillator (icd)

ICD

The (ICD) utilizes various computational models to assess patient risk, guide therapy, and predict device performance in clinical settings

MODELING APPROACHES

Cardiac Electrophysiology Models Application: Simulate abnormal electrical activity leading to arrhythmias, informing effective ICD therapy design Physical Phenomena: Captures electrical conduction dynamics and disturbances leading to arrhythmias. Data Required for Validation: ECG recordings, intracardiac electrograms, and heart structure/function data

Seattle Heart Failure Model (SHFM) Application: likelihood of sudden death in heart failure patients, assisting in ICD implantation decisions. Physical Phenomena: relationship between clinical parameters and heart failure progression Data Required for Validation: Patient demographics, clinical history, lab results, and follow-up mortality data

MODELING APPROACHES

Arrhythmia Prediction Models

Risk Prediction Modeling

DESCRIPCIÓN AQUÍ

DESCRIPCIÓN AQUÍ

DESCRIPCIÓN AQUÍ

PROSE-ICD Risk Model Application: Identifies patients at risk of inappropriate ICD shocks and complications to guide programming decisions. Physical Phenomena: Interaction between patien-specific factors and arrhythmia likelihood leading to innapropiate shocks Data Required for Validation: Patient demography, previous ICD history and related outcomes.

Shock Efficacy Models Application: Evaluate how ICD shocks propagate through heart tissue, optimizing settings for defibrillation success. Physical Phenomena: Interaction between electrical fields shocks and myocardial tissue Data Required for Validation: Experimental data on shock delivery, tissue response, and clinical outcomes of defibrillation

MODELING APPROACHES

Patient Selection Models

Device Performance Models

DESCRIPCIÓN AQUÍ

DESCRIPCIÓN AQUÍ

DESCRIPCIÓN AQUÍ

STEPS FOR ASME V&V 40 GUIDELINES

05

06

07

EXECUTE PLAN

ESTABLISH PLAN

COMPUTATIONAL MODEL CREDIBILITY FOR COU?

DOCUMENTATION AND EVIDENCE

08

ASSESS CREDIBILITY

CREDIBILITY ACTIVITIES

ESTABLISH RISK - INFORMED CREDIBILITY

ESTABLISH CREDIBILITY GOALS

ASSESS MODEL RISK

04

03

03

02

CONTEXT OF USE

01

QUESTION OF INTEREST

ASME V&V 40

STEPS

APPLICATIONS · Preclinical testing · Device optimization · Regulatory submissions

PARAMETERS OF FOCUS · Energy threshold for defribilation success · Electrode positioning · Interaction between shoks and the different myocardial structures

PURPOSE Evaluate optimal energy levels and electrode configurations for ICDs to enhance defibrillation success while minimizing tissue damage risk.

What are the optimal energy levels and electrode configurations to enhance shock efficacy while minimizing potential tissue damage?

CONTEXT OF USE

02

QUESTION OF INTEREST

01

ASME V&V 40

STEPS

GOALS · Accuracy · Generalizability · Reproducibility · Validation · Clinical Relevance

RISK LEVEL High

POTENTIAL RISKS · Inaccurate predictions leading to failed defibrillation attempts · Incorrect electrode configurations · Poor representation of patient variability

ESTABLISH CREDIBILITY GOALS

04

ASSESS MODEL RISK

03

ASME V&V 40

STEPS

UNCERTAINTY QUANTIFICATION Assess how variations in inputs impact model predictions to ensure robustness

VALIDATION Compare simulation outcomes with clinical data to verify predictions

VERIFICATION Conduct simulations to ensure numerical stability and consistency

MODEL DEVELOPMENT Implement bidomain or monodomain models to simulate shock propagation

Validate numerical algorithms Compare predictions with real-world data Identify and quantify sources of variability Key parameters of model outputs Improve model accuracy

Model verification Model validation Uncertainty quantificaton Sensitivity analysis Iterative refinement

STEPS REQUIRED

EXECUTE PLAN

06

ESTABLISH PLAN

05

ASME V&V 40

STEPS

REGULATORY ALIGNMENT Ensure documentation meets regulatory guidelines

UNCERTAINTY QUANTIFICATION Reports on variability impacts and sensitivity

VALIDATION DATA Results from validation tests comparing predictions with clinical data

VERIFICATION RESULTS Evidence of model correctness through verification test

MODEL DESCRIPTION Detailed explanation of the mathematical framework and assumptions

CREDIBILITY EVIDENCE Establish credibility through successful verification and validation UNCERTAINTY ANALYSIS Demonstrate model robustness against variability in key inputs CLINICAL RELEVANCE Ensure the model offers actionable insights for clinical decision-making MODEL LIMITATIONS Predicting long-term effects in scarred hearts

DOCUMENTATION AND EVIDENCE

08

COMPUTATIONAL MODEL CREDIBILITY FOR COU?

07

ASME V&V 40

STEPS

CONCLUSIONS

In this framework, the ICD simulation model can be validated and verified according to the ASME V&V 40 guidelines by addressing these steps systematically. This will ensure the model is robust, reliable, and applicable to real-world scenarios, supporting device development or regulatory decisions

CONCLUSIONS

• Kristensen SL, Levy WC, Shadman R, et al. Risk Models for Prediction of Implantable Cardioverter-Defibrillator Benefit: Insights From the DANISH Trial. JACC Heart Fail. 2019;7(8):717-724. doi: 10.1016/j.jchf.2019.03.019

• Bilchick KC, Wang Y, Cheng A, et al. Seattle Heart Failure and Proportional Risk Models Predict Benefit From Implantable Cardioverter-Defibrillators. J Am Coll Cardiol. 2017;69(21):2606-2618. doi:10.1016/j.jacc.2017.03.568

• Z. Jiang et al., "In-silico pre-clinical trials for implantable cardioverter defibrillators," 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 2016, pp. 169-172, doi: 10.1109/EMBC.2016.7590667

CITATIONS

PAULA MIRA & MERCEDES CABALLERO

THANKS FOR ATTENDING