MTAA Joint Industry Working Group Position Paper · MTAA Joint Industry Working Group Position Paper . June, 2016 . MTAA Joint Industry Working Group Position Paper TGA Draft Evidence

Evidence Requirements for Magnetic Resonance Imaging (MRI) Labelling of Active Implantable Medical Devices (AIMDs)

MTAA Joint Industry Working Group Position Paper

June, 2016

MTAA Joint Industry Working Group Position Paper TGA Draft Evidence Requirements for MRI Labelling of Active Implantable Medical Devices (AIMDs) May 2016 Page 2 of 21

Contents

1. Executive Summary ............................................................................................... 3

Introduction ............................................................................................................ 4 2.

Background ............................................................................................................ 4 3.

Risk-based Approach ............................................................................................. 5 4.

4.1 Potential Hazards ................................................................................................... 6

Introduction to MRI Modelling ................................................................................. 7 5.

5.1 Major Variables Influencing Lead Electrode Heating .................................................... 8

5.1.1 Lead Design and Lead Length .................................................................................. 8

5.1.2 Lead Path ................................................................................................................. 9

5.1.3 Patient Size, Anatomy and Physiology...................................................................... 9

5.1.4 Patient Position in the Scanner ................................................................................. 9

5.1.5 Scanner Variables .................................................................................................... 9

Numerical Modelling of Clinical Risk ...................................................................... 9 6.

6.1 MRI Phase Effects: Sensitivity of heating to complex electric field parameters ..... 10

6.2 Numerical Integration of Modelling Components ................................................... 13

Clinical Validation of the MRI Modelling Framework: Consistency of Human clinical 7.study evidence with Modelling Output ......................................................................... 15

Industry Perspective on the Role of Modelling ..................................................... 16 8.

Demonstrating Compliance with the Essential Principles ..................................... 17 9.

Ethical Considerations of Requiring Clinical Trials ............................................... 18 10.

Risks of Denying Patients Access to MRI Scans .................................................. 18 11.

Post Market Data ................................................................................................. 19 12.

Equivalence in the Context of MRI Conditionality ................................................. 19 13.

Conclusion ........................................................................................................... 20 14.

Recommendations ............................................................................................... 20 15.

References .......................................................................................................... 21 16.


1. Executive Summary

Magnetic Resonance Imaging (MRI) is recognised as being an indispensable tool for the investigation and diagnosis of conditions that affect soft tissue. Until recent years, MRI scans were contraindicated in patients with Active Implantable Medical Device (AIMD) systems due to the potential for hazardous interactions between the electromagnetic fields generated by the MRI scanner and the implanted medical device system.

As the number of patients with implantable cardiac device systems increases concurrently with the increase in use of MRI scans, manufacturers have focussed their attention on validating the conditions under which MRI scans can be safely performed on patients with AIMDs.

While this position paper focuses mainly on lead heating, industry acknowledges the other potential hazards identified in the Consultation: Draft clinical evidence guidelines - Medical devices document in Table 4 on Page 99. All potential hazards associated with MRI scanning of implantable cardiac devices are tested in accordance with ISO/TS 10974 and other international standards. As part of product development, manufacturers identify and evaluate potentially hazardous interactions between implanted devices and a multitude of potential risks including MRI scanning. An outcome of the risk management process is the identification of risks that remain after mitigation strategies have been put in place. In these circumstances, a clinical trial may be required to address the significance of any residual risks.

There is a range of potential hazards associated with implantable cardiac devices .The effectiveness of risk mitigation measures for safe MRI scanning conditions can be demonstrated by testing according to the internationally recognised ISO/TS 10974:2012 Assessment of the safety of magnetic resonance imaging for patients with an active implantable medical device. Industry has reached a consensus that the most accurate and robust method of determining the MR safety of an implantable pacing or defibrillation system in a human population involves the use of this comprehensive modelling framework.

As is stated in Wilkoff et al, “Modelling is a practical and efficient method for exploring thousands of variable combinations of patient anatomy, scanner type, implant location, lead model and lead routing well beyond what is possible through clinical trials. It also allows for analysis of parameter extremes outside the bounds of normal clinical practice, which allows further assessment of safety margins and the sensitivity of influencing variables”. [5]

There is currently a large body of collective clinical data across all cardiac device manufacturers demonstrating that a broad range of implantable cardiac device systems perform safely when used under the MR conditions that they have been validated and labelled for. It is industry’s position that the existing clinical evidence in conjunction with modelling and bench testing is the most suitable way to quantify the performance of implantable leads and cardiac devices in an MR environment.

Modelling together with bench testing and animal studies is the most effective approach to holistically account for all of the potential hazards associated with MR scanning of patients with implantable cardiac devices.


Therefore, the key recommendation of this position paper is that TGA accept modelling using the internationally accepted test method document ISO/TS 10974 in conjunction with bench testing and risk management activities in lieu of clinical evidence for the safety of MR conditional labelling for AIMDs.. Once the modelling has been clinically validated on one system, the model should be applicable to all families of devices without having to provide additional clinical trial data.

Introduction 2.

The medical devices industry has moved to the use of the internationally recognised standards including ISO/TS 10974:2012 Assessment of the safety of magnetic resonance imaging for patients with an active implantable medical device as primary evidence of safety to support the MR conditional labelling of implantable cardiac systems. A MTAA Joint Industry Working Group comprising representatives from Medtronic, Boston Scientific, BIOTRONIK and St. Jude Medical was formed in September, 2015 to discuss the TGA’s requirement for clinical trial data to support MR conditional labelling. This group includes Australian regulatory affairs professionals and US based MRI subject matter experts who are participating in the development of several MRI related standards including ISO/TS 10974 and AAMI PC76 (which includes acceptance criteria for implantable cardiac systems). This industry Position Paper has been prepared to address the following specific concerns:

TGA Requirements:

1. Currently TGA requires clinical evidence resulting from a clinical study or direct clinical evidence from an equivalent product to meet EP14.

2. Computer modelling alone does not meet the TGA criteria for clinical evidence.

3. TGA’s definition of equivalence relates to products with the same technical characteristics (e.g. materials, design, manufacturing process), biological characteristics and clinical use.

4. Modelling data can be used to support the clinical evidence only if the specific modelling approach has been clinically validated for equivalent devices within the same family or system.

Background 3.

Magnetic Resonance Imaging (MRI) is the ‘gold standard’ scanning modality to investigate or diagnose conditions that affect soft tissue and is therefore an indispensable diagnostic tool. MRI scans have historically been contraindicated for patients with Active Implantable Medical Device (AIMD) systems due to the potential for hazardous interactions between the electromagnetic (EM) fields generated by the MRI scanner and the implanted medical device system. The number of patients with implantable cardiac systems is growing, while at the same time the use of MRI scanning is increasing. Based on these two factors it has been estimated that there is a 50 – 75 % probability that a pacemaker or ICD patient will require an MRI scan over the lifetime of their implanted device [1].


To meet this clinical need, manufacturers of AIMD systems have begun to validate and label their products as MR Conditional so that patients can be safely scanned according to specific MR conditions for use. Overview of PC 76: ISO/TS 10974 Ed1 was initiated in the summer of 2007 and intended to provide test methods for each MRI hazard for any type of AIMD (active implantable medical device). It was recognized that the TS could not include requirements because they would be different for each different type of AIMD. Hazard requirements would be handled by product specific standards which would draw on the test method content in the TS. PC76 is the cardiac (pacemaker and ICD) standard.

The PC76 Ed1 scope was limited to transvenous pacemakers, ICDs, and CRT devices. PC76 was introduced in October of 2012 with the intent of using the test methods from TS 10974, being more specific when possible, and also including requirements. The various hazard clauses in the TS frequently have multiple tiers (different test methods) to accommodate the wide range of AIMD types, but many of these tiers might not be applicable to CIEDs (cardiac implantable electronic device). For example, the RF heating clause contains 4 tiers (4 test methods) but only a single method is applicable for CIEDs. PC76 contains the single applicable test method with greater detail provided because the location in the body and the characteristics of the surrounding tissues are known.

Risk-based Approach 4.

As part of the Risk Management procedure performed during product development, manufacturers identify and evaluate potentially hazardous interactions between the devices and the MR environment, amongst a multitude of other potential risks. In each case, risks are evaluated and measures are applied for their mitigation. In relation to safe MRI scans, mitigation measures may include design changes to reduce heating in the MR environment, programming active devices to a specific mode prior to the MRI scan and imposing conditions on the MRI scan which are detailed in the product labelling and Instructions for Use. The clinical evaluation is expected to address the significance of any risks that remain after design risk mitigation strategies have been employed by the manufacturer [2]. A properly developed risk analysis is crucial in determining what clinical data is required for a particular device. An outcome of the risk management documentation is the identification of any residual risks. The clinical data are expected to quantify and address those risks [3]. The effectiveness of risk mitigation measures for safe MRI scanning conditions is demonstrated by testing according to ISO/TS 10974:2012. The objective of a modelling framework is to evaluate the risk in the MR environment and provide a robust safety evaluation for new and existing products [4]. Hence, for devices that meet the test criteria, the risk has been shown to be successfully mitigated and therefore additional clinical data is not required. Across all cardiac device manufacturers there is a large body of collective clinical data (more than 3,500 MRI scanned patients with an AIMD system in clinical trials) demonstrating that a broad range of implantable cardiac systems perform safely in the MR environment.


Consequently, since much of the clinical data has been demonstrated to clinically validate modelling performed by various manufacturers, the medical devices industry is moving towards computer aided modelling and bench testing in accordance with international standards in lieu of new clinical trial data to support MRI safety.

4.1 Potential Hazards

Table 1 below identifies the potential hazards to patients with an implanted cardiac system when undergoing an MRI scan and Table 2 provides an overview of the test method used to validate safe performance. Table 1. Potential hazards to patients with an implanted cardiac system when undergoing an MRI scan

MR hazard/clinical impact

Static field Gradient field

Radiofrequency field

Force and torque/discomfort, dislodgement X

Vibration/discomfort, device damage X X

Device interactions/therapy delivery, device reset, device damage

X X X

Device case heating/discomfort, tissue necrosis

X X

Unintended cardiac stimulation/arrhythmia induction, asystole

X X

Lead electrode heating/therapy delivery, sensing

X

Table 2. General Hazard to the Patient

Test Requirement Test Method

Heat RF field-induced heating Gradient field-induced heating

ISO/TS 10974 ISO/TS 10974

Vibration Gradient field-induced vibration

ISO/TS 10974

Force B0-induced force ISO/TS 10974 and ASTM F2052

Torque B0-induced torque

ISO/TS 10074 and ASTM F2213

Extrinsic electric potential Gradient field-induced lead voltage

ISO/TS 10974

Rectification RF field-induced rectified lead voltage

ISO/TS 10974

Malfunction B0 field-induced device malfunction RF field-induced device malfunction Gradient field-induced device malfunction

ISO/TS 10974


Safety in the context of MR is determined by the lead, irrespective of the device it is paired with. MR safety relates primarily to lead heating where the conductive lead acts as an antenna and couples radiofrequency energy, a portion of which is dissipated as heat in the cardiac tissue near the electrodes. There is industry consensus on the best practices to observe when validating a lead model. Although not published in a publically available standard, Version 2 of ISO/TS 10974 (which will likely be published in the next year) will address best practices for validating lead models

Introduction to MRI Modelling 5.

MRI utilizes three powerful magnetic fields, a static field, the gradient field and a radiofrequency field, which are shown in Figure 1. The static magnetic field is used to align hydrogen protons (primarily in water and fat) in order to create a net magnetization, the RF field is used to flip the protons out of alignment, and as they relax back into alignment, a signal is generated. The gradient magnetic field is used to spatially encode this signal. The combination of the three fields allows for the excitation and acquisition of MRI images.

A primary hazard associated with implanted devices is the clinical effect from heating of the lead electrodes, which is due to the interaction between the RF electric fields and lead wires, which act like antennas picking up the available energy. Note that this hazard is exclusively due to the lead, and is independent of the attached pulse generator. The amount of RF energy coupled to an implanted lead is a complex problem and depends on several parameters, including: patient size and position in the scanner bore, MR scanner RF output power, lead path, lead design, and lead length. The RF energy coupled to the lead propagates along the lead body as electromagnetic (EM) waves.

Much like waves in water, where coincidental waves will either add or subtract (depending on their relative phase), RF phase effects will either result in phase enhancement or phase cancellation. Figure 2 shows a simplified case of two sources of energy. For the leads in question, there is a RF source of energy continuously along the lead length. RF phase effects result in complex enhancement or cancellation of the RF energy that reaches the distal lead tip. A portion of the RF energy that reaches the distal lead end is dissipated as heat in the tissue near the lead electrodes. Depending on the severity, heating may lead to no change, a temporary change, or a permanent change in pacing capture threshold (PCT). A comprehensive modelling framework is necessary in order to incorporate clinically relevant distributions of the variables that impact RF lead electrodes. Using this method one can accurately model the significant variation expected in lead electrode heating and its clinical effect.

Figure 1. MRI scanner cross section, illustrating the coils that produce the magnetic fields

Gradient Coils – X, Y, Z

RF Field

Static Field


Figure 2. Physical waves behave like electromagnetic waves, where coincidental waves will either add (enhancement) or subtract (cancellation) depending on the relative phase. This phenomenon is referred to as RF phase effects. Figure adapted from http://www.daviddarling.info/images/interference_water_waves.jpg.

5.1 Major Variables Influencing Lead Electrode Heating Figure 3 provides a diagram of the dominant variables that impact lead heating. Computer-aided modelling is the only practical and efficient method for exploring all of the possible combinations of patient, lead, and scan variables in a holistic manner. Computer modelling also allows for analysis of parameter extremes, outside the bounds of normal clinical practice, which allows further assessment of safety margin and the sensitivity of influencing variables. [5]

Figure 3. Major variables influencing lead electrode heating. The total variation can exceed 10,000x depending on the combination of variables.

5.1.1 Lead Design and Lead Length A lead has two primary characteristics that impact lead tip heating: the antenna properties (efficiency in coupling RF) and transmission properties (efficiency conducting RF along the lead). Both of these characteristics are impacted by the lead design, most notably due to conductor construction and lead length, and to a lesser extent insulation material and insulation thickness. As a result, different lead designs are likely to have different antenna properties, and hence likely a different lead model.

Lead Path

Scanner Variables

Lead Tip Heating

Patient Size, Anatomy, and

Physiology

Patient Position in MRI Scanner

Lead Design

Lead Length


5.1.2 Lead Path The complex 3D shape of the implanted lead (path) also confounds the lead heating analysis. Imagine taking your TV or FM radio antenna and bending it into different pretzel like shapes. The antenna reception will be better for some shapes than others. For some lead paths the RF energy picked up by the first half of the lead can be cancelled by the RF energy picked up in the second half of the lead, resulting in almost zero heating. At the other extreme, the RF energy picked up by the first half of the lead can be enhanced by the RF energy picked up in the second half of the lead, resulting in high heating. Naturally, there is a continuum between these two extremes. This phenomenon is generally referred to as phase effects, and is a dominant source of lead heating variation. The phase effects are the primary reason why sophisticated modelling techniques are required.

5.1.3 Patient Size, Anatomy and Physiology The human body is composed of tissues and organs of varying electrical conductivity. Blood and muscle are two of the more conductive tissues in the body, while adipose tissue (fat) is the least conductive. RF electromagnetic energy is significantly attenuated by conductive medium and diminishes quickly as it penetrates the body. The amount of RF energy that couples to the lead is a complex function of all the tissue the RF travels through before reaching the lead, and varies patient to patient due to differences in anatomy and composition.

5.1.4 Patient Position in the Scanner The RF energy is highest in the center of the RF coil, and falls off rapidly outside the geometry of the coil. The patient anatomy to be scanned is typically centered in the MRI scanner. Therefore, the RF energy incident on cardiac leads is significantly greater with the torso (and pacing system) inside the scanner bore as compared to an extremity scan such as a knee scan.

5.1.5 Scanner Variables The scanner output power depends on the type of scan sequence, the portion of the anatomy to be imaged, and the size and shape of the patient. The scanner output power also varies based on the length and diameter of the scanner’s main RF coil. The scanner performs a short pre-scan sequence to determine the optimum RF pulse amplitude for the patient and anatomy of interest. There is an optimal RF pulse amplitude for each scan and higher or lower power results in image degradation.

SAR (specific absorption rate) is a measure of the average RF power deposited in tissue during an MRI scan and depends on the pulse amplitude and the spacing (time) between pulses. Lead tip heating is approximately proportional to SAR.

Numerical Modelling of Clinical Risk 6.

Industry has reached consensus that the most accurate, robust method of determining the MRI safety of an implantable pacing or defibrillation system in a human population requires the implementation of a comprehensive modelling framework. Section 6.1 discusses how RF phase effects impact lead electrode heating levels in more detail. These phase effects are a major factor that determines which patients will have a clinical effect due to lead electrode heating, and necessitates the utilization of a comprehensive modelling framework. This framework, discussed in Section 6.2 incorporates electromagnetic simulations of the electric fields induced in humans, numerical models of the leads, and physiologic response


models which determine the physiologic effect of the RF power deposited in tissue near the electrodes.

6.1 MRI Phase Effects: Sensitivity of heating to complex electric field parameters

For devices that are relatively short compared to the wavelength of the MRI RF energy (e.g., a stent), the MRI testing methods can be greatly simplified, as the phase component of the RF energy can be ignored. For electrically long devices such as cardiac leads, the complex interaction between the electric fields and the leads (which have magnitude and phase components) can interact in unintuitive ways that can only be described using a comprehensive modelling approach.

When the MRI RF signal interacts with the cardiac lead, multiple RF waves are generated within the lead body. Individual waves travel different distances from the location where they are generated within the lead body to the electrode (Figure 4). When the RF waves meet at the lead electrode, they will either add or subtract at the lead electrode. If the waves add at the tip electrode, tip heating will be higher than if the waves subtract at the tip electrode.

Figure 4. When the signal from the MRI RF coil interacts with the lead body, electromagnetic waves are generated within the lead body. The waves will travel toward the lead tip.

Due to this effect, the difference in lead tip heating can be dramatic – more than 100x for different lead paths – and therefore it is extremely important to account for these differences when calculating the RF power that reaches the lead tip during an MRI scan.

In order to demonstrate that the lead model is capable of predicting dissipated power for all phase scenarios, the accuracy of the lead model is evaluated in an in-vitro test environment, which is referred to as Model Validation. Lead model validation is performed by comparing simulation results to empirical measurements for well-defined cases in a homogeneous phantom. In order to ensure that the model retains validity for a variety of incident fields, validation cases are uniquely selected for each lead to exercise the model over a wide range of expected heating conditions. This is important, as it ensures the model is predictive and accurate over the entire range of predicted clinical heating values.

Figure 5 shows one method of validating a lead model by the direct comparison of simulated dissipated power to dissipated power measured directly in a phantom. First, a model of the phantom inside a MRI RF coil was created (A). The electrical model of the MRI RF coil was input to the electromagnetic simulator, which computed 3D distributions of the electric field (the magnitude in one cross-section of the phantom is shown in B). Example lead paths in the phantom (C) were defined, and the electric field distribution along the lead paths was used in conjunction with the lead model to compute the power levels at the electrode-solution interface (D). In (D), an example of how the electrode dissipated power can be shown for every combination of device (on the horizontal axis) and electrode (on the vertical axis) positions is provided. Dissipated power levels vary from high (red) to low (blue). Note

RF signals from MRI

Electromagnetic waves resulting from MRI signalMRI-induced lead tip heating

Tip electrode

Lead body


that the dissipated power varies greatly for different lead paths (different combinations of device and electrode locations).

Figure 5. Modelling strategy applied to the body phantom. A: a model of the phantom inside a MRI RF

coil. B: magnitude of the 3D distribution of the electric field (one coronal cross-section) computed using an electromagnetic simulator. C: lead paths defined in the phantom. D: power levels at the

electrode-solution interface for every combination of device (horizontal axis) and electrode (vertical axis) locations.

In order to demonstrate the importance of phase, three lead routings in a phantom (shown as a blue line) are described below in

D C

A B


Table 3. The electric field for each path is shown, and the phase component of the electric field relative to the lead model is represented either by a blue box (indicating cancellation), or a red box (indicating addition). When cancellation occurs (most predominant in Path#1, but present to a lesser extent in Path#2), it results in reduced power dissipated at the electrode interface. In the event of phase addition (shown in Path #3), a significant increase in heating can occur.


Table 3. Summary of three in-vitro test conditions that exhibit the importance of phase, where path #1, 2 and 3 represent phase cancellation, minor cancellation, and phase addition, respectively.

Path 1: Long Straight Path

Path 2: Short Straight Path

Path 3: Phase –reversal Path

Lead Routing though phantom

Electric field magnitude along path;

Relative path heating

1x 3x 5x

These examples demonstrate how phase effects influence the magnitude of heating, and further, how it can be difficult to predict which cases will exhibit phase cancellation or addition without explicit information on the lead model and exposed electric field. This becomes even more difficult when predicting heating in humans, as the electric field is much more complex compared to electric fields in a phantom. Only by exposing the lead model to a diverse set of electric fields can the performance be determined for the entire range of clinical scenarios. Electromagnetic models of scanners, human bodies using clinically relevant lead routings, clinically relevant electric fields can be exposed the lead models in a virtual environment in order to predict the real, clinical risk for hundreds of thousands of clinically relevant scenarios.

6.2 Numerical Integration of Modelling Components The major factors that influence the amount of power deposited into the tissue near the electrodes of cardiac leads can be assigned to two categories: electric field models, which determine the RF field available along the path of the lead body, and lead models, which determine the magnitude of power deposited for a given RF application. When clinically

Subtract

(less) Add Subtract Add

Add Add


relevant distributions of these quantities are combined with a model of the physiologic response to RF power, the probability of change in a physiologic response in the patient population can be calculated via statistical analysis (ex. probability of a change in pacing capture threshold). A summary of the variables used in the statistical analysis is shown in Figure 6.

Figure 6. Summary of MRI lead electrode heating modelling framework

Based on the potential combinations of electric fields, lead models and physiologic responses, the clinical possibilities are infinite; however, it is possible to effectively model them by simulating hundreds of thousands of clinically relevant scenarios, ensuring that the entire clinical landscape is considered when determining the safety of the implanted system.

Numerical modelling of these components allows for investigation into all potential combinations of variables described in Section 5.2. Simulations are executed using a library of human body models (representing the diversity of body sizes and weights expected in the human population), over a variety of lead paths trajectories (representing all the potential device and lead tip locations) and type of MRI scan (i.e., ankle scan or chest scan). In reality, far more variables and permutations are investigated during the evaluation of an MRI-conditional system in order to allow for the analysis of parameter extremes, outside the bounds of normal clinical practice. This allows for an assessment of safety margin and the sensitivity of influencing variables that cannot be done in a clinical trial.

As discussed above in Figure 3, the amount of dissipated power (proportional to heating) is highly dependent on the interaction of several important variables. For the majority of cases, the amount of heating deposited in the tissue near the electrodes will be clinically insignificant (i.e., less than a fever). However, for some combinations of these variables, the amount of heating could pose a safety risk to the patient. Due to the complexity of the hazard, modelling is the only way to evaluate the entire clinical landscape, as it is not possible to evaluate these combinations in a clinical setting.

The figure below explains this scenario, where a trend describing the likelihood of heating and a trend describing the likelihood of PCT change given an amount of heating are combined to predict the amount of patients that may observe a PCT change. As seen here, at most a small percentage of patients (1% or less) would be expected to have a PCT change. This 1% is the integral of the product of these two curves shown in yellow. The acceptance criteria is set across all possible MRI conditions, where the percentage of patients exposed to a PCT change will be defined. The acceptance criteria may vary slightly from manufacturer to manufacturer, but all ensure that the risks due to lead electrode heating are very low. As demonstrated by the figure below, for the simulation acceptance

Statistical Analysis PROBABILITY OF

CHANGE

LEAD MODELS PHYSIOLOGIC RESPONSE ELECTRIC FIELD MODEL


criteria to be met, the majority of patients that have a measurable PCT change will have very high power levels, but a likelihood of occurrence will be far less than 1%. This means that the vast majority of patients would be exposed to powers that will not have any likelihood to cause changes to PCT.

Figure 7. Likelihood of PCT change, where the probability of PCT change is defined as the area under the dissipated power cumulative density function (CDF), shown in blue, and the PCT change CDF, shown in red.

Clinical Validation of the MRI Modelling Framework: Consistency 7.of Human clinical study evidence with Modelling Output

In recent years, maturation of the modelling framework has enabled industry to assess the lead heating behavior for a wide variety of cardiac leads in a consistent manner and has contributed to the increase in MR Conditional pacing and defibrillation systems available worldwide. As a result of simulating hundreds of thousands of clinically relevant scenarios, device manufacturers have been able to demonstrate for certain products that only a small percentage of combinations of variables will cause a clinically significant PCT change. While the modelling framework allows the most clinically harmful scenarios to be determined, it is not possible to incorporate this information into the design of clinical trials because the exact combinations of variables rarely naturally occurs and cannot be designed to occur. The vast majority of combinations of variables that occur clinically are confirmed by the modelling framework to have non-significant heating and PCT change. Given the incidence of clinically significant PCT is most likely less than 1%, and often can be orders of magnitude less, it is practically impossible to validate the predicted harm rates with any statistical rigor using a clinical trial.

What clinical studies can do and have done is confirm that the modelling framework appropriately predicts the absence of clinically significant PCT change. Each major device manufacturer has performed at least one clinical trial with greater than 100 patients implanted with a specific pacing or defibrillation system of theirs. Prior to initiating and executing a clinical study, each manufacturer completed comprehensive computer modelling, including a validated lead model, to estimate the rate of adverse clinical events from lead heating. Because the modelling is performed using conditions more severe than typical in a clinical setting (i.e., it is unlikely that the patient will be exposed to the maximum RF energy permitted by the implanted system’s labeling, as is conducted using the


modelling), the modelling framework typically overestimates the possibility of significant PCT change in the real world. All studies completed to date have met their study endpoint relative to PCT change (or lack of it), consistent with the model-based predictions of low and acceptable adverse event rates. Within the limitations of a clinical trial, clinical studies have confirmed that the modelling framework appropriately predicts the absence of clinically significant PCT change.

The collective clinical evidence to date confirms the effectiveness of the modelling-based approach, in that the models predict the incidence of changes due to MRI are very low. No manufacturer-sponsored or independent investigator-initiated clinical study has invalidated this assertion. It is industry’s stance that modelling is the only way to qualify the performance of leads (and AIMDs) in an MRI environment and that additional clinical studies provide no value in determining risks to patients due to lead heating. Furthermore, almost every element of each device manufacturer’s modelling framework is reused in evaluating different products. For example, the same set of patient positions in the scanner are used, the same human body models (representing patient anatomy and size) are used, the same clinical lead paths are used (accommodating for leads that may only be indicated for one chamber of the heart), and the same physiologic response model is used. The main difference in the lead heating modelling framework for evaluating one product versus another is the generation and validation of the lead model for the specific product. Hence, further clinical trials are not necessary, as they simply are retesting the ability of the modelling framework to overestimate the true clinical event rate.

Industry Perspective on the Role of Modelling 8.

The MRI lead heating modelling that has been developed by medical device manufacturers is a significant achievement, but it is important to recognize that the bulk of the modelling framework reuses tools that were developed and validated in other industries.

1. The RF birdcage coils are known very precisely. The exact amount of RF and uniformity of the RF field is critical for MR imaging. This knowledge of how to model the RF from the RF birdcage coil is fundamental to the MR. This knowledge is replicated and applied in our cases, with little to no deviation from the approved methods for RF birdcage modelling already accepted for use on MR Imaging. Accurately predicting RF radiation patterns of antennas is no longer the challenge that it was prior to modern computer simulation techniques.

2. The human body models used in the simulations have important RF electric properties (conductivity and permittivity) assigned to each different tissue type (organ). These two electrical characteristics determine the RF currents that will flow in a body with an RF field impinging on the body and have been published by many independent university researchers. MRI scanner manufacturers pioneered the use of this work more than 20 years ago, and then it was adopted and used by the mobile phone industry to demonstrate that mobile phones are safe. Most companies utilize models that were commissioned by the U.S. FDA, and validated for accuracy by the FDA and through peer reviewed literature [7]. These models have been used across many different industries, including over 150 FDA submissions.

3. Finite Difference Time Domain (FDTD) is an RF simulation tool that is used in 1 and 2 above to calculate the electric fields in the body models. Again, several manufacturers sell these validated simulation tools, which are used in many industries.

The two novel items that have been created by the medical device industry are the lead paths and the RF electrical model of the lead. Creation of lead paths is relatively simple


because the medical device industry has been studying lead paths for years to gain a better understanding of where maximum stresses are. As a result the generation of an array of lead paths from x-ray, fluoroscopy, and/or CT scans is a straightforward endeavor.

The creation of the RF electrical lead model is the key technical achievement in the modelling framework which has advanced industry’s collective understanding of the RF heating hazard and enabled systematic and holistic evaluation of the safety profile of our products. In all cases, the medical device companies either invested heavily in internal R&D or contracted external RF experts to develop validated RF models for cardiac leads. In a phantom environment, the lead model is exposed to electric fields over a range of amplitudes and phases to examine and confirm that the lead model is accurately responding to known electrical fields. The models are only considered valid and used for clinical prediction if they predict the heating effect accurately. This validation criterion is robust, and only allows deviation from measurement and prediction as accounted by known sources in error in measurement or simulation technique. It is important to remember that the only characteristic being validated is the ability of the lead to pick up RF and then to handle the phase effects that were described earlier. This can easily be performed in a phantom, i.e. no need to validate the model in a clinical setting.

Demonstrating Compliance with the Essential Principles 9.

Compliance with several of the safety requirements defined in the Essential Principles (EP) is currently being demonstrated by bench testing and modelling to recognised international industry best practices, and does not require separate clinical validation. Examples include:

• EP 9 Construction and environmental properties; 9.2(c) refers to the risk of reciprocal interference involving other devices that are normally used in an investigation or treatment of the kind for which the device is intended to be used, which also applies to AIMDs used with MRI diagnostic imaging - covered by testing to IEC 60601-1-2, ISO/TS 10974 .;

• EP 12 Medical devices connected to or equipped with an energy source - covered by

testing to IEC 60601-1, IEC 60601-1-2, IEC 62304. Exposure to the magnetic fields of an MRI scanner is an environmental hazard similar to that of other medical diagnostic or therapeutic devices that the patient might be exposed to such as diathermy, lithotripsy and electrocautery. There are also numerous environmental hazards that the patient may be exposed to in their home or work place such as radio transmitters, arc welders, Electronic Article Surveillance devices and metal detectors. Environmental hazards such as these are identified in the risk analysis for a medical device under development. They are characterised by bench testing and mitigated by contraindications or cautionary labelling in the Instructions for Use provided with the device as required by EP13. International standards exist to support the validation of device performance under these conditions and compliance with standards has long been accepted as satisfactory evidence that a device will continue to perform safely if exposed to a variety of environmental hazards. Similarly MRI procedure manuals provided with devices labelled as MR conditional clearly articulate any potential adverse effects that may cause patient harm, the adverse conditions that must be checked prior to undergoing an MRI scan and the specific MRI scanning conditions that must be followed to ensure that the system continues to perform safely and without any harm to the patient. Safe MRI scanning conditions for specific implanted


systems is based on the extensive bench testing and modelling that has been conducted according to the internationally recognised ISO/TS 10974. Therefore, similarly to other environmental hazards, exposure to MR can be validated by bench testing and the risk mitigated by providing information in device labelling. Computer-aided modelling and bench testing should be accepted as sufficient to demonstrate safety in the context of MRI of cardiac devices. Clinical data should only be required where the risk analysis has identified new residual risks to the implanted system that cannot be mitigated without data from clinical trials.

Ethical Considerations of Requiring Clinical Trials 10. Supplying clinical data, in addition to computer modelling no longer adds value to the assessment of safety for any given device system. In the late 1990’s little was known about the performance of implanted devices in the MR environment and basic phantom testing and animal trials were used. During the 2000’s computer modelling was developed and subsequent clinical trials confirmed the predictions from modelling. Modelling capabilities have advanced rapidly since that time and are applied by various manufacturers in a consistent manner such that the knowledge being gained from new clinical trials is minimal. The modelling approach has been endorsed by leading experts in cardiology as it provides a comprehensive safety evaluation that is impossible to achieve using phantom testing, animal studies, or clinical trials alone.[5] The TGA requirement to provide clinical trial data poses an ethical dilemma in that the design of such trials often involves patients having to undergo a non-clinically indicated MRI scan. This not only exposes those patients to an unnecessary diagnostic procedure but also places additional demand on already stretched health care resources. Consequently, some ethics committees and authorities take an unfavourable view of those clinical trials. For instance, the French Authority ANSM will no longer approve clinical trials that involve patients undergoing a MRI scan without medical indication [6]. Since the number of patients with implanted cardiac systems undergoing a clinically indicated MRI scan is still relatively small, it is not practical to produce sufficient evidence in a reasonable timeframe (i.e. 2-3 years). Due to the relatively short life of devices, the products may well be approaching obsolescence by the time the required number of patients have undergone a clinically indicated MRI scan.

Risks of Denying Patients Access to MRI Scans 11. MRI is a valuable diagnostic tool that is used on an increasingly frequent basis [8]. In 2011, an estimated 32 million MRI procedures were performed in the U.S. alone, up 4% from the previous year [9]. In addition, the number of patients with implantable cardiac systems is growing by about 2% each year. Denying patients with implantable cardiac systems access to MRI scans carries its own risks as follows:

1. Use of another imaging modality. MRI is the preferred imaging modality for soft-tissue imaging with high resolution and no radiation. If a patient is denied an MRI scan, a CT could be an alternative however, this involves exposing the patient to radiation of increasing dose with


larger portions of the body being imaged and may result in an inferior diagnostic outcome. Fine needle biopsy for breast cancer detection is an alternative to MRI however is a more invasive and painful procedure for the patient.

2. Physicians deciding to perform MRI scan ‘off-label’. There is a risk when a physician decides to perform an MRI scan on a patient with an implanted system that has not been labelled MR conditional as there is no guidance from the manufacturer as to the conditions under which the MRI scan can be safely performed, if any. For off-label scans, the physician is often not fully aware of the potential risks, and does not have access to pre-determined “MRI safe” programming modes, which may expose the patient to unknown risk.

3. Explant of AIMD system to permit safe MRI scan. Explantation of AIMD systems, particularly leads carries a high morbidity and mortality. It would therefore be a last resort in order to perform an MRI scan on a patient with an invalidated AIMD system implanted.

Not having this important clinical tool available and not having clear guidance from the manufacturer of which conditions an MRI scan can be safely performed (if any), adds additional risks to patients with AIMDs. These important considerations must be taken into account when undertaking a risk/benefit assessment of MRI conditional labelling for an AIMD.

Post Market Data 12.

Currently there have been no publications or reports available of serious adverse incidents that have occurred when performing an MRI scan on a cardiac system labelled MR Conditional where the MRI scan has been performed according to the labelled conditions. Absence of serious adverse events under these conditions is considered to be additional supporting evidence that MR conditional devices are safe to undergo MRI scans under specified conditions for use. Companies within the JWG are not observing serious adverse events related to patients with implanted cardiac systems undergoing MRI scans. However, this may be an area where companies could review their post-market procedures to more specifically capture information about whether patients have undergone an MRI scan.

Equivalence in the Context of MRI Conditionality 13.

The current definition of equivalence provided in the TGA draft guidance document for medical devices is very restrictive. TGA’s definition of equivalence relates to products with the same technical characteristics (e.g. materials, design, manufacturing process), biological characteristics and clinical use. This may not be relevant when considering safety in an MR environment and should be considered on a case-by-case basis. Equivalence in terms of how the device performs in an MR environment is the appropriate criteria for demonstrating how safety data can be applied to systems. Provided that the accuracy and predictive capability of the modelling framework (especially the defined acceptance criteria) has been clinically demonstrated for a manufacturer, the


modelling framework should be deemed sufficient to support subsequent testing for MRI safety for other device systems by the same manufacturer.

Conclusion 14.The modelling framework together with bench testing and animal studies is the most effective approach to holistically account for all of the potential hazards associated with MR scanning of patients with implantable cardiac devices For any pacemaker or defibrillator patient in need of an MRI, it is impossible to precisely predict the clinical impact of RF heating which will occur when the patient undergoes a scan. This is due to the complex interactions of patient anatomy; lead design, electric field, and MRI scan parameter factors. The lead heating modelling framework is the only approach that holistically accounts for all the (major) factors contributing to lead heating and allows the risk of clinically significant heating to be quantified for a patient population. The modelling framework applies modern best practices in science and engineering, and utilizes validated, commercially available tools. The RF electrical lead models unique to the Cardiac Rhythm Management (CRM) industry were developed by RF experts as a result of over a decade of intensive research and development at each manufacturer. Industry, research laboratories, academia, and regulatory bodies have reached consensus regarding the methodology of creating and validating an accurate RF modelling framework and electrical lead model.

Clinical trials provide a small, random sampling of the infinite possible combinations of interactions of patient anatomy, lead design, electric field, and MRI scan parameter factors and therefore are less informative than the holistic model-based evaluation. The history of successful system-specific clinical trials demonstrates that the modelling framework employed by all CRM manufacturers has appropriately quantified the clinical risks associated with MR scanning. Once a clinical trial has been conducted the modelling framework can be reused with a new lead model with confidence in the conservative nature of its predictive capability.

The acceptance of modelling in association with bench testing for assessing MR conditional safety of cardiac device systems will enable faster patient access to this important diagnostic tool and reduce the risks to patients by ensuring that clear instructions are provided to radiologists on safe scan conditions for specific implanted device systems.

Recommendations 15.

Industry recommendations:

1. The TGA consider “equivalence” in terms of how the device performs in an MR environment as the appropriate criteria for demonstrating how safety data can be applied to systems.

2. That the TGA continue to collaborate with other international regulators and

relevant standards committees to develop a common understanding and approach to the evaluation of AIMDs with MR conditional labelling

3. That the TGA accept that modelling using the internationally accepted ISO/TS 10974:2012 in conjunction with bench testing and risk management activities in lieu of the need for clinical data for the safety of MR conditional labellingwhere the model has been clinically validated


References 16.

[1] Ron Kalin and Marshall S. Stanton, “Current Clinical Issues for MRI Scanning of

Pacemaker and Defibrillator Patients,” PACE 28, (April 2005): 326-328.

[2] MEDDEV 2.7.1. Rev. 3.

[3] Australian Medical Devices Guidelines – Clinical Evidence Requirements for Inclusion of Medical Devices in the Australian Register of Therapeutic Goods – Guidance Document Number 4, v1.6.

[4] J. Leal Del Ojo, et al, “Is Magnetic Resonance Imaging Safe in Cardiac Pacemaker Recipients?” PACE 28, (April 2005): 274-278.

[5] Wilkoff BL et al. Safe magnetic resonance imaging scanning of patients with cardiac rhythm devices: A role for computer modelling. Heart Rhythm2013;10:1815–1821.

[6] Correspondence of ANSM to BIOTRONIK, letter on file at TGA or can be provided again.

[7] Christ, Andreas, et al. "The Virtual Family—development of surface-based anatomical models of two adults and two children for dosimetric simulations." Physics in medicine and biology 55.2 (2009): N23.

[8] Glenn N. Levine, et al, “Safety of Magnetic Resonance Imaging in Patients With Cardiovascular Devices: An American Heart Association Scientific Statement From the Committee on Diagnostic and Interventional Cardiac Catheterization, Council on Clinical Cardiology, and the Council on Cardiovascular Radiology and Intervention,” Circulation 116, (2007): 2878-2891.

[9] 2012 MR Benchmark Report, IMV Medical Information Division, Inc.

Documents

MTAA Joint Industry Working Group Position Paper · MTAA Joint Industry Working Group Position Paper . June, 2016 . MTAA Joint Industry Working Group Position Paper TGA Draft Evidence