Conference Paper

In-silico pre-clinical trials for implantable cardioverter defibrillators

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Abstract

Regulatory authorities require that the safety and efficacy of a new high-risk medical device be proven in a Clinical Trial (CT), in which the effects of the device on a group of patients are compared to the effects of the current standard of care. Phase III trials can run for several years, cost millions of dollars, and expose patients to an unproven device. In this paper, we demonstrate how to use a large group of synthetic patients based on computer modeling to improve the planning of a CT so as to increase the chances of a successful trial for implantable cardioverter defibrillators (ICDs). We developed a computer model of the electrical generation and propagation in the heart. This model was used to generate a large group of heart instances capable of producing episodes of 19 different arrhythmias. We also implemented two arrhythmia detection algorithms from the literature: Rhythm ID from Boston Scientific and PR Logic + Wavelet from Medtronic. Using this setup, we conducted multiple in-silico trials to compare the ability of the two algorithms to appropriately discriminate between potentially fatal Ventricular Tachy-arrhythmias (VT) and nonfatal Supra-Ventricular Tachy-arrhythmias (SVTs). The results of our in-silico trial indicate that Rhythm ID was less able to discriminate between SVT and VT and so may lead to more cases of inappropriate therapy. This corroborates the findings of the Rhythm ID Going Head to Head Trial (RIGHT), a clinical trial that compared the two algorithms in patients. We further demonstrated that the result continues to hold if we vary the distribution of arrhythmias in the synthetic population. We also used the same in-silico cohort to explore the sensitivity of the outcome to different parameter settings of the device algorithms, which is not feasible in a real clinical trial. In-silico trials can provide early insight into the factors which affect the outcome of a CT at a fraction of the cost and duration and without the ethical issues.

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... Closed-loop model checking of medical device software can provide formal and rigorous evidence, but may not be scalable to more complex algorithms [15]. Model-based (in-silico) pre-clinical trials use physiological models as virtual patient, and have been used to complement and support clinical trials [13,18]. However, these pre-clinical trials do not fully align with the statistical approaches in clinical trials, and may not be able to generate clinically-relevant results that can guide clinical decisions. ...
... In this case study, we have adopted the Virtual Heart Model (VHM) [14] to model the interaction between the heart and the ICD, as well as disease mechanism. The VHM is a timed-automata-based heart model which is capable Fig. 4. Timed automata heart model of simulating the electrical activities of various heart conditions, and has been used to model the electrical activities of the heart during model checking and closed-loop testing of implantable cardiac devices [15,13]. The VHM model used in this case study is illustrated in Fig. 4. The two node automata HA Heart and HV Heart model the generation of the three input events to the ICDs. ...
... In this case study, we use the RhythmID algorithm from GDT and PRLogic algorithm from MDT presented in [13], which were inferred from algorithm descriptions in open literature. The algorithms are converted from Matlab code to timed automata [2], which are attached in the Appendix for interested readers. ...
Preprint
Full-text available
Clinical trials are considered as the golden standard for medical device validation. However, many sacrifices have to be made during the design and conduction of the trials due to cost considerations and partial information, which may compromise the significance of the trial results. In this paper, we proposed a model-based pre-clinical trial framework using statistical model checking. Physiological models represent disease mechanism, which enable automated adjudication of simulation results. Sampling of the patient parameters and hypothesis testing are performed by statistical model checker. The framework enables a broader range of hypothesis to be tested with guaranteed statistical significance, which are useful complements to the clinical trials. We demonstrated our framework with a pre-clinical trial on implantable cardioverter defibrillators.
... The synthesized reprogramming attacks yield optimal effectiveness and stealthiness with respect to a set of training EGM signals. We employ the method of [12] to generate synthetic EGMs with prescribed arrhythmia. This allows the attacker to synthesize malicious parameters tailored to the victim's cardiac condition. ...
... The parameters of the algorithm are given in Table 1. We consider the description of the Rhythm ID algorithm by Jiang et al. [12], where the authors provided a MATLAB implementation of the algorithm based on the manufacturer's manuals and the medical literature [6,28]. This implementation faithfully captures the behavior of the Rhythm ID algorithm, as it was validated by demonstrating conformance to a BSc commercial ICD device on 11 test cases. ...
... Discrimination algorithms utilize two elements of EGMs for feature extraction: timing of atrial and ventricular events, and morphology of far-field ventricular events. Jiang et al. [12] have developed a heart model that can generate realistic synthetic EGMs that can be used to evaluate the safety and efficacy of discrimination algorithms. The timing of heart events is generated by a timed-automata model of the electrical conduction system of the heart [13], which allows simulating cardiac dynamics under different parameter settings. ...
Article
Full-text available
An Implantable Cardioverter Defibrillator (ICD) is a medical device used for the detection of potentially fatal cardiac arrhythmias and their treatment through the delivery of electrical shocks intended to restore normal heart rhythm. An ICD reprogramming attack seeks to alter the device’s parameters to induce unnecessary therapy or prevent required therapy. In this paper, we present a formal approach for the synthesis of ICD reprogramming attacks that are both effective, i.e., lead to fundamental changes in the required therapy, and stealthy, i.e., are hard to detect. We focus on the discrimination algorithm underlying Boston Scientific devices (one of the principal ICD manufacturers) and formulate the synthesis problem as one of multi-objective optimization. Our solution technique is based on an Optimization Modulo Theories encoding of the problem and allows us to derive device parameters that are optimal with respect to the effectiveness-stealthiness trade-off. Our method can be tailored to the patient’s current condition, and readily generalizes to new rhythms. To the best of our knowledge, our work is the first to derive systematic ICD reprogramming attacks designed to maximize therapy disruption while minimizing detection.
... The synthesized reprogramming attacks yield optimal effectiveness and stealthiness with respect to a set of training EGM signals. We employ the method of [12] to generate synthetic EGMs with prescribed arrhythmia. This allows the attacker to synthesize malicious parameters tailored to the victim's cardiac condition. ...
... The parameters of the algorithm are given in Table 1. We consider the description of the Rhythm ID algorithm by Jiang et al. [12], where the authors provided a MATLAB implementation of the algorithm based on the manufacturer's manuals and the medical literature [6,28]. This implementation faithfully captures the behavior of the Rhythm ID algorithm, as it was validated by demonstrating conformance to a BSc commercial ICD device on 11 test cases. ...
... Discrimination algorithms utilize two elements of EGMs for feature extraction: timing of atrial and ventricular events, and morphology of far-field ventricular events. Jiang et al. [12] have developed a heart model that can generate realistic synthetic EGMs that can be used to evaluate the safety and efficacy of discrimination algorithms. The timing of heart events is generated by a timed-automata model of the electrical conduction system of the heart [13], which allows simulating cardiac dynamics under different parameter settings. ...
Conference Paper
Full-text available
An Implantable Cardioverter Defibrillator (ICD) is a medical device used for the detection of potentially fatal cardiac arrhythmias and their treatment through the delivery of electrical shocks intended to restore normal heart rhythm. An ICD reprogramming attack seeks to alter the device's parameters to induce unnecessary therapy or prevent required therapy. In this paper, we present a formal approach for the synthesis of ICD reprogramming attacks that are both effective, i.e., lead to fundamental changes in the required therapy, and stealthy, i.e., are hard to detect. We focus on the discrimination algorithm underlying Boston Scientific devices (one of the principal ICD manufacturers) and formulate the synthesis problem as one of multi-objective optimization. Our solution technique is based on an Optimization Modulo Theories encoding of the problem and allows us to derive device parameters that are optimal with respect to the effectiveness-stealthiness tradeoff. Our method can be tailored to the patient's current condition, and readily generalizes to new rhythms. To the best of our knowledge, our work is the first to derive systematic ICD reprogramming attacks designed to maximize therapy disruption while minimizing detection.
... The synthesized reprogramming attacks yield optimal effectiveness and stealthiness with respect to a set of training EGM signals. We employ the method of [14] to generate synthetic EGMs with prescribed arrhythmia. This allows the attacker to synthesize malicious parameters tailored to the victim's cardiac condition. ...
... The parameters of the algorithm are given in Table 1. We consider the description of the Rhythm ID algorithm by Jiang et al. [14], where the authors provided a MATLAB implementation of the algorithm based on the manufacturer's manuals and the medical literature [5,29]. This implementation faithfully captures the behavior of the Rhythm ID algorithm, as it was validated by demonstrating conformance to a BSc commercial ICD device on 11 test cases. ...
... Discrimination algorithms utilize two elements of EGMs for feature extraction: timing of atrial and ventricular events, and morphology of far-field ventricular events. Jiang et al. [14] have developed a heart model that can generate realistic synthetic EGMs that can be used to evaluate the safety and efficacy of discrimination algorithms. ...
Preprint
Full-text available
An Implantable Cardioverter Defibrillator (ICD) is a medical device used for the detection of potentially fatal cardiac arrhythmia and their treatment through the delivery of electrical shocks intended to restore normal heart rhythm. An ICD reprogramming attack seeks to alter the device's parameters to induce unnecessary shocks and, even more egregious, prevent required therapy. In this paper, we present a formal approach for the synthesis of ICD reprogramming attacks that are both effective, i.e., lead to fundamental changes in the required therapy, and stealthy, i.e., involve minimal changes to the nominal ICD parameters. We focus on the discrimination algorithm underlying Boston Scientific devices (one of the principal ICD manufacturers) and formulate the synthesis problem as one of multi-objective optimization. Our solution technique is based on an Optimization Modulo Theories encoding of the problem and allows us to derive device parameters that are optimal with respect to the effectiveness-stealthiness tradeoff (i.e., lie along the corresponding Pareto front). To the best of our knowledge, our work is the first to derive systematic ICD reprogramming attacks designed to maximize therapy disruption while minimizing detection. To evaluate our technique, we employ an extensive dataset of synthetic EGMs (cardiac signals), each generated with a prescribed arrhythmia, allowing us to synthesize attacks tailored to the victim's cardiac condition. Our approach readily generalizes to unseen signals, representing the unknown EGM of the victim patient.
... The database of signals we used in this evaluation consists of 1920 EGMs, equally divided between 960 VTs and 960 SVTs. The input stream was generated by the heart model of [3], [22], whose outputs had been validated for realism by cardiologists [22]. Using a heart model allows us to generate different types of VTs and SVTs, thus exposing the QRE implementation to a wider range of rhythms than is possible by using a database of natural signals. ...
... The database of signals we used in this evaluation consists of 1920 EGMs, equally divided between 960 VTs and 960 SVTs. The input stream was generated by the heart model of [3], [22], whose outputs had been validated for realism by cardiologists [22]. Using a heart model allows us to generate different types of VTs and SVTs, thus exposing the QRE implementation to a wider range of rhythms than is possible by using a database of natural signals. ...
Article
Implantable medical devices are safety-critical systems whose incorrect operation can jeopardize a patient's health, and whose algorithms must meet tight platform constraints like memory consumption and runtime. In particular, we consider here the case of implantable cardioverter defibrillators, where peak detection algorithms and various others discrimination algorithms serve to distinguish fatal from non-fatal arrhythmias in a cardiac signal. Motivated by the need for powerful formal methods to reason about the performance of arrhythmia detection algorithms, we show how to specify all these algorithms using Quantitative Regular Expressions (QREs). QRE is a formal language to express complex numerical queries over data streams, with provable runtime and memory consumption guarantees. We show that QREs are more suitable than classical temporal logics to express in a concise and easy way a range of peak detectors (in both the time and wavelet domains) and various discriminators at the heart of today's arrhythmia detection devices. The proposed formalization also opens the way to formal analysis and rigorous testing of these detectors' correctness and performance, alleviating the regulatory burden on device developers when modifying their algorithms. We demonstrate the effectiveness of our approach by executing QRE-based monitors on real patient data on which they yield results on par with the results reported in the medical literature.
... The three versions were run on a database of 960 EGMs, equally divided into 480 SVTs and 480 VTs. The beat timing in the EGMs (in other words, the boolean stream s) was generated by the heart model of [36], [37]. Briefly, this model can simulate beat generation and propagation at different rates, from different locations in the heart. ...
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This study sought to identify the incidence and outcome related to inappropriate implantable cardioverter-defibrillator (ICD) shocks, that is, those for nonventricular arrhythmias. The MADIT (Multicenter Automatic Defibrillator Implantation Trial) II showed that prophylactic ICD implantation improves survival in post-myocardial infarction patients with reduced ejection fraction. Inappropriate ICD shocks are common adverse consequences that may impair quality of life. Stored ICD electrograms from all shock episodes were adjudicated centrally. An inappropriate shock episode was defined as an episode during which 1 or more inappropriate shocks occurred; another inappropriate ICD episode occurring within 5 min was not counted. Programmed parameters for patients with and without inappropriate shocks were compared. One or more inappropriate shocks occurred in 83 (11.5%) of the 719 MADIT II ICD patients. Inappropriate shock episodes constituted 184 of the 590 total shock episodes (31.2%). Smoking, prior atrial fibrillation, diastolic hypertension, and antecedent appropriate shock predicted inappropriate shock occurrence. Atrial fibrillation was the most common trigger for inappropriate shock (44%), followed by supraventricular tachycardia (36%), and then abnormal sensing (20%). The stability detection algorithm was programmed less frequently in patients receiving inappropriate shocks (17% vs. 36%, p = 0.030), whereas other programming parameters did not differ significantly from those without inappropriate shocks. Importantly, patients with inappropriate shocks had a greater likelihood of all-cause mortality in follow-up (hazard ratio 2.29, p = 0.025). Inappropriate ICD shocks occurred commonly in the MADIT II study, and were associated with increased risk of all-cause mortality.
Model-based Clinical Trials for Implantable Cardiac Defibrillators
• H Abbas
• Z Jiang
• K J Jang
• M Beccani
• J Liang
• S Dixit
• R Mangharam