Active Surveillance of the ICD Registry for Defibrillator Lead Failures

Abstract

Background:

Several defibrillator leads have been recalled due to early lead failure leading to significant patient harm. Confirming the safety of contemporary defibrillator leads is essential to optimizing treatment for patients receiving implantable defibrillators (ICD). We therefore sought to assess the comparative long-term safety of the four most commonly implanted ICD leads within the National Cardiovascular Data Registry ICD Registry.

Methods and Results:

A propensity-matched survival analysis of the ICD Registry was performed evaluating four contemporary ICD leads in patients receiving an ICD system for the first time. All patients in the ICD Registry aged ≥18 years who underwent an implant of an ICD between April 1, 2011 and March 31, 2016 were included. Monitoring of safety began with ICD implant and continued up to five years. A meaningful difference in ICD failure rate was defined as twice (or more) the lead failure rate observed in the propensity matched comparator patients. Among the 374,132 patients who received a new ICD implant, no safety alerts were triggered for the primary safety endpoint of lead failure for any of the high energy leads studied. Estimated rates of freedom from lead failure at 5 years ranged from 97.7% to 98.9% for the four high-energy leads of interest.

Conclusions:

Though limited by incomplete long term outcomes ascertainment, active surveillance of the ICD Registry suggests that there were no meaningful differences in the rate of ICD high energy lead survival for the four most commonly used high energy ICD leads.

Ensuring the safety of medical devices after Food and Drug Administration (FDA) approval and market release is critically important, but challenging for several reasons. Pre-market studies of medical devices are frequently limited by small sample size, highly selected patient and provider populations, and relatively short duration of follow up to ensure post-approval safety ^1–5. Additionally, traditional post-approval safety monitoring relies primarily on voluntary reporting of adverse events by providers and hospitals, resulting in incomplete ascertainment of device safety data ^6–11. Prospective, active surveillance of medical devices utilizing sequential monitoring of large, representative clinical data sources directly addresses many of these gaps, and has been identified as a priority by the FDA^12–16.We developed a suite of active surveillance software tools, denoted as DELTA (Data Extraction and Longitudinal Trend Analysis system), to leverage data from high quality data repositories to support active monitoring of the safety of medical devices ^17–20. The methods and informatics infrastructure of DELTA have been previously validated in evaluation of medical device failure or complication rates at defined time points after initial implantation ^{14, 15, 21}. However, survival methods have not been used for prospective surveillance of an accruing clinical dataset in order to assess the freedom from failure of a medical device.

Previous investigations have identified several examples of early failure of high energy ICD leads due to a variety of failure mechanisms²² that have led to significant morbidity and rare fatalities^{23, 24} and device recalls that affected hundreds of thousands of patients. While contemporary ICD leads have undergone extensive evaluation to ensure that they do not have failure mechanisms that are similar to the previously recalled leads, questions regarding their safety have been raised²⁵. Prospective, active surveillance of ICD lead performance using real-world evidence has been proposed as a promising strategy to detect increased rates of high energy lead failures as quickly as possible in the post-market setting^{11, 26–29}. The ICD Registry DELTA study (ICD-DELTA) was therefore designed to evaluate the active surveillance of a national cardiovascular registry of implantable defibrillator (ICD) lead survival. The primary objective of ICD-DELTAis to validate a strategy of prospective, active, safety surveillance of the NCDR ICD Registry based on prospective, propensity matched survival analysis of contemporary high energy ICD leads.

Methods:

Study Design and Oversight:

In accordance with the AHA Journal’s implementation of the Transparency and Openness Promotion Guidelines, the investigators will share the analytic system used for this study as an open source software tool. However, because of the sensitive nature of the data collected for this study, requests to access the dataset from qualified researchers trained in human subject confidentiality protocols may be sent to the NCDR ICD Registry Research and Publications Committee at ncdrresearch@acc.org.

In 2014, a written protocol was developed pre-specifying the clinical end points, analytic methods, sensitivity analyses, and plans for interim data reviews. As part of the protocol, a study oversight committee was established with representation from NCDR, FDA, and the DELTA analytic center. The institutional review board of the NCDR reviewed and approved the study protocol, and the final study protocol was approved prior to the review of any study data. In addition, the institutional review board of the Lahey Hospital and Medical Center waived the need for approval.

Patient Eligibility, Device Exposures, and Endpoint Definitions:

All patients in the NCDR ICD Registry aged ≥18 years who underwent an implant of an ICD with a high energy defibrillation lead between April 1, 2011 and March 31, 2016 were included in the study. Patients younger than 18 years old at time of implant, or those with prior ICD implantation (as documented in the registry) were excluded. We evaluated the safety of the four most commonly implanted high-energy leads used in contemporary cardiovascular practice. These included the: (1) Durata (Abbott Vascular, formerly St. Jude Medical Inc.), (2) Endotak Reliance (Boston Scientific Corporation), (3) Sprint Quattro Secure (Medtronic), and the (4) Linox (Biotronik) ICD leads.

The primary safety outcome of interest was survival free of “lead failure” for any reason as evidenced by a record in the ICD Registry of a subsequent procedure in which the lead was removed and/or replaced. Lead failures were identified for all records in the registry where a high energy lead was documented to be abnormal or was replaced, with documentation of implantation of a new high energy ICD lead. Lead failure records were then matched to an initial implant case, based on exact match of the ICD Registry institution specific patient identifier and agreement between the follow-up record and the index implantation of the ICD system or lead implanted. The pre-specified secondary endpoint evaluated late lead survival, and included only those patients who did not suffer lead failure within 30 days of initial ICD implantation. The secondary endpoint of freedom from “late lead failure” thereby eliminated a common cause of procedural-related lead placement failures.

Active Surveillance System:

We performed our analysis utilizing an active clinical-data surveillance system, denoted DELTA. DELTA is composed of a collection of integrated software components linking open-source database management and statistical analytical tools. DELTA has the capability to prospectively monitor clinical data repositories for adverse safety signals, and is designed to support risk-adjusted prospective safety surveillance analyses of complex clinical data sets. DELTA has been previously validated for prospective monitoring of clinical registries and clinical data sets and is available as an open source software package with associated technical documentation^13–15.

Patient-level data from the ICD Registry were fully de-identified prior to being provided to DELTA. Data were delivered to DELTA on a predetermined schedule of quarterly updates, and the cumulative safety analysis was automatically regenerated within DELTA at two pre-scheduled times (once after 18 and 36 months of data were available) and for a final analyses after the full 60 months of were available. Final study results are based on the final data set and surveillance outcomes after the full 60 months of data were analyzed.

Propensity Score Matching and Event Rate Estimation:

We developed multivariable adjusted logistic regression models to estimate the probability of being treated with each lead of interest, conditional on the included covariates. The nonparsimonious model included previously identified factors for increased risk of lead failure^{22, 30, 31}, as well as factors considered to influence the selection of a lead. A total of 10 variables were included in the final propensity score model. Demographic and comorbid variables included age, sex, body mass index (BMI), diabetes, end stage kidney disease requiring dialysis, history of coronary bypass surgery, pre-procedural left ventricular ejection fraction, history of ischemic heart disease, hypertrophic cardiomyopathy, NYHA functional classification, and whether the index ICD implantation was indicated for primary versus secondary prevention of sudden death.

For each ICD lead of interest, a propensity score matched control population was identified from the population of patients treated with any of the other ICD leads, resulting in a total of four device specific comparisons. The propensity matched comparison group was selected on the basis of the propensity score (PS) model. Matched controls who underwent initial ICD implant during the same half year as the ICD implantation of the ICD lead of interest were selected in a 1:1 ratio using a fixed caliper width of 0.2 SD of the logit of the propensity score^{9, 14, 32} using a greedy matching algorithm. At each quarterly data upload, the DELTA system generated a new PS from the accumulating data and rematched the case sets and adverse event rates were calculated. Missing data were handled using univariate rules, assuming absence of a condition for dichotomous variables, and using the gender-specific median value for continuous variables. The relative covariate balance between the exposed (ICD lead of interest) and unexposed (alternate ICD lead) groups was assessed using the absolute standardized difference (percentile) in covariate means and proportions, with values greater than 10% considered severely imbalanced ³³.

Because we anticipated that the large expected sample size might result in statistically significant differences in estimated survival despite very small absolute differences, we pre-specified a threshold to determine a “clinically meaningful” difference in lead failure. We defined the “DELTA Hazard Ratio”, as the ratio of estimated failure rates (namely: [ 1- estimated survival for the lead of interest] / [ 1- estimated survival of the matched control population ]), at the end of the period of analysis. DELTA safety alerts were therefore triggered if the PS-matched Kaplan-Meier survival probability curve for the device of interest (“case”) demonstrated a DELTA Hazard Ratio ≥ 2.0, while also requiring the survival analysis Stratified Log Rank test to be significant at the p=0.05 level³⁴.

In addition, pre-specified subgroups were explored for evidence of uniquely increased risk of lead failure. These subgroups included: age ≥60yrs, female gender, primary versus secondary prevention, patients with diabetes, patients with end-stage renal disease, and type of ICD system (CRT, dual chamber or single chamber systems). Separately, the secondary endpoint of freedom from “late lead failure” (>30 days after implantation) was assessed for each of the high-energy leads of interest.

As recommended by the study oversight committee, a pre-specified falsification-hypothesis analysis was performed in order to assess the potential of residual confounding after propensity matching. In a PS-matched study, a falsification-hypothesis analysis evaluates the original matched patient cohorts for the development of postprocedural outcome for which no difference in risk is expected between the groups treated with device versus another. In this study, the original matched patient cohorts were evaluated for the subsequent ‘upgrade’ of the ICD system to a bi-ventricular resynchronization therapy system, a development that was predicted to be independent of the selection of the original high energy lead.

Given the significant limitations, including incomplete ascertainment, in use of the ICD Registry to determine the relative frequency of lead failures, as well as the inability to perform any additional audits of the clinical outcomes using the limited analytic dataset available for analyses, the ICD Registry DELTA study oversight committee recommended that all publicly reported study results be masked. The specific ICD leads are thus denoted as lead “A, B, C or D”. This masking strategy was implemented to protect against the risk of a false positive safety report on the basis of uncertain source data.

Results

A total of 629,326 patients in the ICD Registry underwent a procedure related to an ICD system between 3/1/11 and 3/31/16. Among these, 374,132 cases involved the implantation of a new ICD system and represented the study population for this analysis (see Supplemental Material - Figure S1). New implants of the leads of interest ranged from 20,789 to 145,289 during the study period (Table S1). The mean age of study patients was 65 years, 39% had diabetes and 29% were female. Approximately 58% of study participants had ischemic cardiomyopathy, and 78% received the ICD for primary prevention of sudden death (Table S2).

Overall, propensity score matching resulted 99.9% of high energy leads matched with alternative leads. Post-matching standardized differences were less than 0.10 for each covariate within each of the leads of interest, indicating adequate distribution of risk factors between cohorts. The propensity matched results for “Lead A” are provided in Table 1, with the results of other high energy leads provided in Supplemental Tables S3 – S5.

Table 1:

Prior to Match and Post-Match covariate distribution and standardized differences for Lead “A”

	Prior To Match			After Match			Unmatched
	Lead A (n=145,289)	Alternate ICD (n=228,843)	Std. Diff.	Lead A (n=145,249)	Alternate ICD (n=145,249)	Std. Diff.	Lead A (n=40)	Std. Dff.
Patient Age	65.7 ± 12.9	65.2 ± 13.0	0.0	65.5 ± 12.9	65.5 ± 13.0	0.0	66.9 ± 18.0	0.1
Male	72.0%	71.1%	0.0	72.0%	72.1%	0.0	65.0%	0.2
Diabetes	38.7%	39.1%	0.0	38.7%	38.7%	0.0	35.0%	0.1
Current Dialysis	2.6%	3.0%	0.0	2.3%	2.6%	0.0	25.0%	0.7
Body Mass Index	30.1 ± 11.1	30.0 ± 13.3	0.0	30.0 ± 10.3	30.0 ± 11.1	0.0	136.22 ± 227.5	0.7
Ischemic Cardiomyopathy	56.8%	56.7%	0.0	56.8%	56.8%	0.0	70.0%	0.3
Non-Ischemic Cardiomyopathy	39.4%	39.2%	0.0	39.4%	39.4%	0.0	62.5%	0.5
Hypertrophic Cardiomyopathy	12.1%	2.0%	0.0	2.1%	2.1%	0.0	15.0%	0.5
Prior CABG	27.1%	26.7%	0.0	27.1%	27.2%	0.0	42.5%	0.3
NYHA I-II	50.2%	49.2%	0.0	50.2%	50.2%	0.0	67.5%	0.4
NYHA III	47.1%	47.9%	0.0	47.1%	47.1%	0.0	27.5%	0.4
NYHA IV	2.7%	2.8%	0.0	2.7%	2.7%	0.0	5.0%	0.1
LVEF <=20	27.4%	28.2%	0.0	27.4%	27.2%	0.0	20.0%	0.2
LVEF 21-30	44.0%	44.4%	0.0	44.0%	44.2%	0.0	15.0%	0.7
LVEF 31-34	4.4%	4.3%	0.0	4.4%	4.3%	0.0
LVEF 35-39	10.0%	9.8%	0.0	10.0%	10.0%	0.0	2.5%	0.3
LVEF >=40	14.3%	13.2%	0.0	14.2%	14.3%	0.0	62.5%	1.1
Primary Prevention	77.3%	78.7%	0.0	77.3%	77.5%	0.0	42.5%	0.8

Note: Patient age and body mass index shown as mean ± standard deviation

After a total of 5 years of surveillance representing 20 calendar quarters for data analysis, no DELTA safety alerts were triggered for the primary safety endpoint of lead failure for any of the high energy leads studied (Figure 1, Supplemental Figures S2 – S4). Estimated rates of freedom from lead replacement at 5 years ranged from 97.7% to 98.9% for the four high-energy leads of interest. While the log-rank statistic of the difference in survival curves frequently resulted in a p-value less than 0.05, at no point in the analysis did the absolute failure rate for an individual lead exceed twice the failure rate of the comparator control population (Table 2).

Figure 1:

Quarterly propensity-matched survival analyses through five years, comparing high energy Lead “A” with alternative high energy ICD leads for the primary endpoint of freedom from lead failure. The solid green line indicates survival of Lead “A”. The solid blue line indicates alternative ICD leads. The 95% confidence bands are noted as shaded color regions.

Table 2:

Summary of status of safety alerts for lead failure for each high energy lead.

Lead of Interest	Exposure	Lead Implants at Start	Lead Implants at End	Estimated Survival at End	95% Confidence Band	P-value	DELTA Hazard Ratio	DELTA Alert
Lead A	Lead A Alternative Leads	145,249 145,249	6,890 6,864	98.9% 98.5%	97.8% - 99.7% 97.4% −99.3%	<0.001	0.70	No
Lead B	Lead B Alternative Leads	104,968 104,968	5,868 5,908	98.3% 98.8%	96.9% −99.3% 97.6% −99.6%	<0.001	1.43	No
Lead C	Lead C Alternative Leads	102,340 102,340	4,175 4,178	98.8% 98.6%	97.6% −99.6% 97.1% −99.5%	0.0042	0.87	No
Lead D	Lead D Alternative Leads	20,787 20,787	843 844	97.7% 98.8%	95.0% −99.4% 94.9% −100%	<0.001	1.89	No

Of all lead failures identified, 44% occurred within the first 30 days of initial implant, ranging from 39.5% to 52.3% for the four high energy leads of interest. After 5 years of surveillance there were no DELTA safety alerts triggered for the secondary endpoint of survival free from late lead failure (i.e. > 30 days post implant). Estimated rates of freedom from late lead failure at 5 years ranged from 98.1% to 99.1% for the four high-energy leads of interest (Table S6).

The protocol-specified interim analyses were performed after 18 and 36 months of data collection, and demonstrated no clinically meaningful differences in high-energy lead survival at any point in time (Table S7). Similarly, no differences in freedom from lead replacement were observed in the pre-specified patient groups, including women, age less than or greater than 60 years, primary or secondary prevention, diabetics, or patients with end stage renal disease (results for Lead “A” shown in Table 3).

Table 3:

Subgroup results for Freedom from Lead Failure for Lead “A”

Patient Subgroup	Exposure	Lead Implants at Start	Lead Implants at End	Estimated Survival at End	95% Confidence Band	P-value	DELTA Hazard Ratio	DELTA Alert
Female	Lead A Alternative Leads	40,626 40,626	1,777 1,759	98.7% 98.1%	97.0% −99.8% 96.6% −99.1%	<0.001	0.65	No
Age > = 60	Lead A Alternative Leads	102,102 102,102	4,847 4,828	99.0% 98.6%	97.5% −99.8% 97.5% −99.4%	<0.001	0.75	No
Age < 60	Lead A Alternative Leads	43,043 43,043	2,039 2,024	98.8% 98.2%	96.8% −99.8% 96.3% −99.4%	<0.001	0.65	No
Primary Prevention	Lead A Alternative Leads	112,288 112,288	5,326 5,313	98.9% 98.5%	97.6% −99.7% 96.9% −99.5%	<0.001	0.72	No
Secondary Prevention	Lead A Alternative Leads	32,906 32,906	1,558 1,546	98.9% 98.4%	96.5% −100% 95.8% −99.8%	<0.001	0.67	No
Diabetes	Lead A Alternative Leads	56,209 56,209	2,682 2,675	99.0% 98.6%	96.8% −99.9% 97.0% −99.6%	<0.001	0.75	No
Dialysis	Lead A Alternative Leads	3,734 3,734	178 179	98.4% 98.6%	9.12% −100% 94.2% −100%	0.7640	1.19	No
CRT Device	Lead A Alternative Leads	53,173 53,173	2,497 2,497	98.9% 98.5%	98.6%−99.0% 96.0%−99.8%	<0.001	0.71	No
Single Chamber	Lead A Alternative Leads	39,070 39,070	1,563 1,552	98.8% 98.4%	96.0%−100% 95.8%−99.8%	<0.001	0.75	No
Dual Chamber	Lead A Alternative Leads	52,586 52,586	2,807 2,024	99.0% 98.6%	96.9%−99.9% 97.0%−99.6%	<0.001	0.72	No

Results of the falsification-hypothesis analysis demonstrated similar rates of ICD system upgrade to a biventricular system (Figure 2) for each of the leads of interest, indicating that there was no evidence for significant residual confounding in the propensity matched cohorts analyzed.

Figure 2:

Falsification hypothesis results comparing high energy Lead “A” with alternative high energy ICD leads for freedom from upgrade of ICD system to a CRT-D system. Quarterly propensity-matched survival analyses through five years of surveillance. The solid green line indicates survival of Lead “A”. The solid blue line indicates alternative ICD leads. The 95% confidence bands are noted as shaded color regions.

Discussion

The ICD Registry DELTA study was designed to assess the feasibility of active post-market surveillance to assess the safety of commonly used high energy defibrillation leads using a novel, prospective, propensity matched survival method. During the study period of five years, 374,132 new ICD systems were recorded in the ICD Registry, with over 99% of these procedures involving the implantation of one of the four high-energy leads of interest. Propensity score matching resulted in high match rates with adequate risk differences between groups. Our analysis showed similar high-energy lead survival, as defined as freedom from lead failure, among patients treated with the four most commonly used high-energy leads. Late lead failure rates were also similar among the high energy leads studied. In pre-specified subgroup analyses, no clinically meaningful differences were identified in women, diabetics, patients on chronic dialysis, those receiving the device for primary prevention, or those undergoing initial ICD implant with biventricular pacing systems.

From a medical device safety evaluation perspective, the results of the ICD Registry DELTA study are reassuring, and demonstrate similarly acceptable performance of the four most commonly implanted contemporary high-energy ICD leads. Given several recent examples of failures in high-energy leads identified after widespread adoption following market release^{3, 35–37}, rapid evaluation of high-energy lead safety after market approval is essential in order to minimize the risk of faulty lead design leading to patient harm or the need for lead replacement^{3, 27, 29}.

A novel attribute of the methods used in this analysis was the inclusion of a protocol that defined “clinically meaningful” difference thresholds between patients exposed to one ICD lead as compared with propensity matched controls. Given the very large sample size available in the ICD Registry and other large clinical data sources, it is to be expected that even very small differences in outcomes may achieve statistical significance, as we observed repeatedly in this study. However, the very small differences observed in this study did not meet the pre-defined threshold of a doubling of the risk of failure in the patients treated with the lead of interest relative to the controls. Incorporating pre-defined clinically meaningful differences in safety outcomes within prospective active surveillance studies may provide regulators with objective criteria by which to consider whether a medical device warrants further evaluation (i.e. signal confirmation studies) or even rapid regulatory action.

The ICD Registry DELTA study further validates the strategy of prospective, active surveillance of accruing clinical datasets, which can address many of the limitations in our current understanding of the comparative risk of new and existing medical devices. Early identification of safety signals associated with medical device use may provide opportunities to reduce device associated morbidity, and may allow early intervention to support the need for device specific training or device refinement to improve patient safety. Of equal importance, lack of signal generation indicating equivalence of real-world safety performance, as demonstrated in this study, should reassure providers using technologies initially evaluated through narrow study populations.

Limitations:

There are several important limitations of this study. The ICD Registry, while nearly comprehensive in the capture of implants of new ICD implants in the U.S. during the study period, has inherent limitations with respect to identification of lead failures. Prior to 2018, the Centers for Medicare and Medicaid Services (CMS) required data collection for all ICD implants as a condition for coverage, however there was no requirement for data collection of lead revisions. In February of 2018, after the study period covered in this analysis, CMS removed the data collection requirement all together for ICDs, with an expected subsequent decline in the enrollment in the registry. Therefore, use of the ICD Registry for device safety surveillance will be even more challenging in the future, as there may be inherent bias in analyzing the registry with non-compulsory participation. The analytic dataset did not include unique patient identifiers that were valid across institutions, and we were significantly limited in our ability to match individual patients receiving a replacement ICD lead with the index ICD implant in situations where the two procedures occurred at different institutions. Also, while new ICD implants were comprehensively recorded in the ICD Registry, isolated lead revision or replacement procedures may have been variably documented based on local hospital practice. In addition, without routine longitudinal follow up information, we could not ascertain which patients had died, and we assumed that any such deaths (a competing risk in the Cox model) would have been distributed equally among the exposed groups.

The primary endpoint of freedom from any lead failure is relatively broad, and includes both early lead failures (within 30 days) that are often attributable to device implant procedure issues rather than structural lead failures. However, the secondary endpoint of freedom from “late lead failure” (>30 days) also did not demonstrate any difference in lead performance. The definition of lead failure used here required documentation that the high energy lead was either removed or another high energy lead was implanted. This definition would therefore not capture those patients with an abandoned lead when no additional ICD lead was implanted, and may have undercounted total lead failures. Importantly, lead replacement, used in this analysis as a surrogate for lead failure, is inherently incomplete as a measure of lead performance. Monitoring the electrical performance parameters of implanted leads as well as imaging data represent the gold standard for identifying defibrillator lead dysfunction, but such information was not available within the ICD Registry for the purposes of this study. While ascertaining the cause of lead failure was not a pre-specified outcome for this study, such data would be critical for regulatory application of active surveillance of ICD lead performance. In addition, non-structural reasons for replacing the high energy lead, including sensing function failure or malposition of the lead, would have been captured in the primary endpoint, though such reasons do not indicate an inherent device failure. Finally, this comparative analysis of lead survival is limited to the time window of five years of follow up registry data available for evaluation. If any type of lead had an increased risk of failure which became manifest several years after implantation, it would not have been captured in this study.

As with any active surveillance analyses of post-market device performance, the results of this study must be interpreted with caution, since all such analyses are limited by their inherent observational design. We sought to minimize potential confounding through robust risk adjustment using propensity matching, which resulted in well-balanced distributions of baseline covariates between the high-energy leads studied and their comparator groups. However, we cannot fully exclude residual confounding impacting the observed results. Therefore we also performed pre-specified falsification hypothesis analyses, in which we monitored the registry for the subsequent upgrade of the original ICD system to a cardiac resynchronization (i.e. biventricular pacing) system, although we did not anticipate a difference in the rate of ICD system upgrade with any particular high-energy lead. We found that the risk of ICD system upgrade was no different among the patients receiving the various high-energy leads, supporting a low likelihood of residual risk imbalance between the propensity matched recipients of the four high energy leads. Finally, an inherent limitation of a propensity matched analysis is that inferences can only be drawn based on the population of patients for which a matched control could be identified (referred to as ‘common support’); limiting the conclusions to the subset of patients for which there was relative equipoise for primary and comparator populations

Conclusion:

The findings of this study support the feasibility of prospective, active surveillance of a large, representative ICD registry to monitor high-energy lead failure in near real time. Despite the significant limitations of the source data, the study demonstrated no clinically significant differences in high-energy lead failure among the four most commonly used ICD leads in contemporary practice.

What is known:

• Several defibrillator leads have been recalled due to early failures, however systems to broadly and prospectively compare approved defibrillator lead performance have not been tested.

What the Study Adds:

• Active, post-approval safety surveillance of the most commonly used defibrillator leads through prospective comparative safety monitoring is feasible using a large, representative, clinical registry and prespecified surveillance methods.

• Defining a clinically meaningful difference in defibrillator lead failure, as compared to propensity matched patients receiving alternative leads, facilitated the near real-time comparison of performance of four contemporary leads.

• The study validates strategy of prospective, active surveillance of accruing clinical datasets for the purpose of assuring the safety of high-risk medical devices.