Assessment of the E-value in the presence of bias amplification: a simulation study.

BACKGROUND: The E-value, a measure that has received recent attention in the comparative effectiveness literature, reports the minimum strength of association between an unmeasured confounder and the treatment and outcome that would explain away the estimated treatment effect. This study contributes to the literature on the applications and interpretations of E-values by examining how the E-value is impacted by data with varying levels of association of unobserved covariates with the treatment and outcome measure when covariate adjustment is applied. We calculate the E-value after using regression and propensity score methods (PSMs) to adjust for differences in observed covariates. Propensity score methods are a common observational research method used to balance observed covariates between treatment groups. In practice, researchers may assume propensity score methods that balance treatment groups across observed characteristics will extend to balance of unobserved characteristics. However, that assumption is not testable and has been shown to not hold in realistic data settings. We assess the E-value when covariate adjustment affects the imbalance in unobserved covariates. METHODS: Our study uses Monte Carlo simulations to evaluate the impact of unobserved confounders on the treatment effect estimates and to evaluate the performance of the E-Value sensitivity test with the application of regression and propensity score methods under varying levels of unobserved confounding. Specifically, we compare observed and unobserved confounder balance, odds ratios of treatment vs. control, and E-Value sensitivity test statistics from generalized linear model (GLM) regression models, inverse-probability weighted models, and propensity score matching models, over correlations of increasing strength between observed and unobserved confounders. RESULTS: We confirm previous findings that propensity score methods - matching or weighting - may increase the imbalance in unobserved confounders. The magnitude of the effect depends on the strength of correlation between the confounder, treatment, and outcomes. We find that E-values calculated after applying propensity score methods tend to be larger when unobserved confounders result in more biased treatment effect estimates. CONCLUSIONS: The E-Value may misrepresent the size of the unobserved effect needed to change the magnitude of the association between treatment and outcome when propensity score methods are used. Thus, caution is warranted when interpreting the E-Value in the context of propensity score methods.

Outcomes of patients implanted with an atrioventricular synchronous leadless ventricular pacemaker in the Medicare population.

BACKGROUND: The Micra AV Coverage with Evidence Development study is a novel analysis of utilization and outcomes associated with Micra AV leadless pacing in US Medicare patients. OBJECTIVE: The purpose of this study was to describe patient characteristics, complications, and outcomes of patients implanted with a Micra AV leadless pacemaker compared with a contemporaneous cohort of patients implanted with a dual chamber transvenous pacemaker. METHODS: Patients implanted with Micra AV (n = 7471) or a dual chamber transvenous pacemaker (n = 107,800) from February 5, 2020, through December 1, 2021, were identified using device registry-linked Medicare claims data. Acute complications were assessed at 30 days, and chronic complications, reinterventions, and all-cause mortality were assessed at 6 months. RESULTS: Patients implanted with Micra AV had higher rates of end-stage renal disease (14.9% vs 2.0%; P < .0001) and overall comorbidity burden (mean Charlson Comorbidity Index 4.9 vs 3.8; P < .0001). There was no difference in the unadjusted rate of complications at 30 days (9.1% vs 8.7%; P = .61), and patients implanted with Micra AV had a significantly lower adjusted rate of complications (8.6% vs 11.0%; P < .0001). At 6 months, patients implanted with Micra AV had significantly lower rates of complications (adjusted hazard ratio 0.50; 95% confidence interval 0.43-0.57; P < .0001) and reinterventions (adjusted hazard ratio 0.46; 95% confidence interval 0.36-0.58; P < .0001). Patients implanted with Micra AV had higher all-cause mortality at 30 days and 6 months, likely because of differences in the underlying risk of mortality. CONCLUSION: Patients implanted with Micra AV had similar rates of complications at 30 days and significantly lower rates of complications and reinterventions at 6 months, despite being sicker than patients implanted with a transvenous pacemaker.

Leadless versus transvenous single-chamber ventricular pacemakers: 3 year follow-up of the Micra CED study.

INTRODUCTION: The Micra Coverage with Evidence Development (CED) Study is a novel comparative analysis of Micra (leadless VVI) and transvenous single-chamber ventricular pacemakers (transvenous VVI) using administrative claims data. To compare chronic complications, device reinterventions, heart failure hospitalizations, and all-cause mortality after 3 years of follow-up. METHODS: US Medicare claims data linked to manufacturer device registration information were used to identify Medicare beneficiaries with a de novo implant of either a Micra VR leadless VVI or transvenous VVI pacemaker from March 9, 2017 to December 31, 2018. Unadjusted and propensity score overlap-weight adjusted Fine-Gray competing risk models were used to compare outcomes at 3 years. RESULTS: Leadless VVI patients (N = 6219) had a 32% lower rate of chronic complications and a 41% lower rate of reintervention compared with transvenous VVI patients (N = 10 212) (chronic complication hazard ratio [HR] 0.68; 95% confidence interval [CI], 0.59-0.78; reintervention HR 0.59; 95% CI 0.44-0.78). Infections rates were significantly lower among patients with a leadless VVI (<0.2% vs. 0.7%, p < .0001). Patients with a leadless VVI also had slightly lower rates of heart failure hospitalization (HR 0.90; 95% CI 0.84-0.97). There was no difference in the adjusted 3-year all-cause mortality rate (HR 0.97; 95% CI, 0.92-1.03). CONCLUSION: This nationwide comparative evaluation of leadless VVI versus transvenous VVI de novo pacemaker implants demonstrated that the leadless group had significantly fewer complications, reinterventions, heart failure hospitalizations, and infections than the transvenous group at 3 years, confirming that the previously reported shorter-term advantages associated with leadless pacing persist and continue to accrue in the medium-to-long-term.

Two-year outcomes of leadless vs. transvenous single-chamber ventricular pacemaker in high-risk subgroups.

AIMS: This study compares clinical outcomes between leadless pacemakers (leadless-VVI) and transvenous ventricular pacemakers (transvenous ventricular permanent-VVI) in subgroups of patients at higher risk of pacemaker complications. METHODS AND RESULTS: This study is based on the Micra Coverage with Evidence Development (CED) study. Patients from the Micra CED study were considered in a high-risk subgroup if they had a diagnosis of chronic kidney disease Stages 4-5 (CKD45), end-stage renal disease, malignancy, diabetes, tricuspid valve disease (TVD), or chronic obstructive pulmonary disease (COPD) 12 months prior to pacemaker implant. A pre-specified set of complications and reinterventions were identified using diagnosis and procedure codes. Competing risks models were used to compare reinterventions and complications between leadless-VVI and transvenous-VVI patients within each subgroup; results were adjusted for multiple comparisons. A post hoc comparison of a composite outcome of reinterventions and device complications was conducted. Out of 27 991 patients, 9858 leadless-VVI and 12 157 transvenous-VVI patients have at least one high-risk comorbidity. Compared to transvenous-VVI patients, leadless-VVI patients in four subgroups [malignancy, HR 0.68 (0.48-0.95); diabetes, HR 0.69 (0.53-0.89); TVD, HR 0.60 (0.44-0.82); COPD, HR 0.73 (0.55-0.98)] had fewer complications, in three subgroups [diabetes, HR 0.58 (0.37-0.89); TVD, HR 0.46 (0.28-0.76); COPD, HR 0.51 (0.29-0.90)) had fewer reinterventions, and in four subgroups (malignancy, HR 0.52 (0.32-0.83); diabetes, HR 0.52 (0.35-0.77); TVD, HR 0.44 (0.28-0.70); COPD, HR 0.55 (0.34-0.89)] had lower rates of the combined outcome. CONCLUSION: In a real-world study, leadless pacemaker patients had lower 2-year complications and reinterventions rates compared with transvenous-VVI pacing in several high-risk subgroups. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT03039712.

Collection of economic data using UB-04s: Is it worth the effort? Evidence from two clinical trials.

Cost collection using UB-04 forms for economic evaluation is challenging, as UB-04 collection is time and effort intensive and compliance is imperfect. Alternative data sources could overcome those challenges. The objective of this study is to determine the usefulness of UB-04 data in estimating hospital costs compared to clinical case report form (CRF) data. Health care utilization costs were compared from financial information in UB-04s and from an assignment process using CRF data, from the WRAP-IT (23 infections) and the Micra IDE trials (61 adverse events and 108 implants). Charge-based costs were calculated by multiplying charges in UB-04s and hospital-specific Cost-to-Charge ratios from the Centers for Medicare and Medicaid Services cost reports. The cost assignment process used clinical information to find comparable encounters in real world data and assigned an average cost. Paired difference tests evaluated whether both methods yield similar results. The mean difference in total infection related costs between methods in the WRAP-IT trial was $152 +/-$22,565. In the Micra IDE trial, the mean difference in total adverse event related costs between methods was -$355 +/-$8,298 while the mean difference in total implant related costs between methods was $-3,488 +/-$13,859. Wilcoxon tests and generalized linear models could not reject the difference in costs between methods in the first two cases. Cost assignment methods achieve results similar to costs obtained through UB-04s, without the additional investment in time and effort. The use of UB-04 information for services that are not mature in a health care system may present unexpected challenges, necessitating a tradeoff with other methods of cost assignment.

Diagnostic sensitivity and cost per diagnosis of ambulatory cardiac monitoring strategies in unexplained syncope patients.

Diagnosing cardiac pauses that could produce syncopal episodes is important to guide appropriate therapy. However, the infrequent nature of these episodes can make detection challenging with conventional monitoring (CM) strategies with short-term ECG monitors. Insertable cardiac monitors (ICMs) continuously monitor for arrhythmias but present a higher up-front cost. It is not well understood whether these higher costs are offset by the costs of repeat evaluation in CM strategies. We simulated the likelihood of diagnostic success and cost-per-diagnosis of pause arrhythmias with CM strategies compared to ICM monitoring. ICM device data from syncope patients diagnosed with pause arrhythmias was utilized to simulate patient pathways and diagnostic success with CM. We assumed that detected true pause episodes (≥5 seconds) were symptomatic and prompted a hospital encounter and further evaluation with CM. Subsequent true pause episodes in yet-undiagnosed patients triggered additional rounds of CM. Costs of monitoring were accrued at each encounter and represent the U.S. payer perspective. Cost per diagnosed patient was calculated as the total costs accrued for all patients divided by the number of patients diagnosed, across 1,000 simulations. During a mean 505±333 days of monitoring ICM detected 2.4±2.7 pause events per patient, with an average of 109±94 days until the first event. CM was projected to diagnose between 13.8% (24-hour Holter) and 30.2% (two 30-day monitors) of the ICM-diagnosed patients. Total diagnostic costs per ICM-diagnosed patient averaged $7,847, whereas in the CM strategies average cost-per-diagnosis ranged from $12,950±2,589 with 24-hour Holter to $32,977±14,749 for two 30-day monitors. Relative to patients diagnosed with pause arrhythmias via ICM, CM strategies diagnose fewer patients and incur higher costs per diagnosed patient.