Pacemaker risk following transcatheter aortic valve replacement - A Bayesian reanalysis

https://doi.org/10.1016/j.ijcard.2022.03.008Get rights and content

Highlights

  • Many patients undergoing transcatheter aortic valve replacement (TAVR) require a permanent pacemaker

  • A study of TAVR patients concluded that a peri-procedural pacemaker was not associated with increased mortality

  • This Bayesian reanalysis shows a modest to high probability of increased mortality in the 4 years following the pacemaker

Abstract

Objectives

To estimate the probability of increased total mortality risk in patients receiving a cardiac pacemaker following transcatheter aortic valve replacement (TAVR).

Background

A recent publication of a nationwide Swedish, population-based cohort study found no statistically significant difference for all-cause mortality. It is unknown if a Bayesian reanalysis would provide additional insights and lead to the same conclusion.

Methods

A digitalized approach to the published Kaplan – Meier curves was used to reconstruct the individual patient dataset. Bayesian survival analyses of this data using both vague, thereby allowing the posterior probability to be completely dominated by the observed data, as well as skeptical and informative priors, based on the mortality risk of pacemaker implantation following surgical aortic valve replacement, were performed.

Results

The individual patient data set was reliably reconstructed and showed a 4 year follow-up hazard ratio (HR) = 1.08, 95% credible interval (CrI) 0.85–1.36. The Bayesian analysis using a vague prior revealed a 74.9% probability of increased mortality in the pacemaker group. Using a skeptical, semi-informative, and fully informative priors, the posterior probabilities of increased mortality following pacemaker insertion was increased to 68.9%, 93.9% and 98.4%, respectively.

Conclusions

This Bayesian reanalysis suggests a moderate to high probability of an increased total mortality in TAVR patients requiring post procedural pacemaker implantation.

Introduction

Recently, a nationwide Swedish, population-based cohort study found no statistically significant difference for all-cause mortality (hazard ratio [HR] 1.03; 95% CI: 0.88–1.22; P = 0.692) in patients who underwent permanent pacemaker implantation after transcatheter aortic valve replacement (TAVR) between 2008 and 2018 [1]. While the study included a large unselected sample of 3420 TAVR patients, there are a number of reasons why it is of interest to query the strength of the evidence supporting the conclusion that long-term survival between patients who did and did not undergo permanent pacemaker implantation after TAVR is not different.

First, their central Kaplan–Meier curve shows survival curves crossing, raising the possibility of a time-varying HR such that the proportional hazards assumptions underlying their analysis may not be valid. Second given this is an elderly population (mean age > 81), the performed comparative lifetime analysis with some patients followed up to 10 years may not be the most informative and clinically relevant. As eventually we all die, this analysis perhaps obscures some earlier clinically pertinent mortality differences among those receiving and not receiving pacemakers peri-TAVR. Thirdly, the same nationwide databases have examined the mortality impact of pacemaker implantation in a contemporary population of aortic stenosis patients undergoing surgical aortic valve replacement (SAVR) [2] and the inclusion of all or some of this additional evidence may be informative.

A Bayesian analysis [3] can directly estimate the probability of increased mortality post pacemaker insertion and allows the incorporation of past knowledge which may be helpful in furthering our understanding of this data and in presenting actionable probabilities.

Section snippets

Data source

To gain approximate access to this dataset, we digitalized the reported Kaplan–Meier mortality curve in the propensity score-matched cohort [1]. We extracted this data instead of from the full cohort since propensity score matching yields more balanced comparative groups than provided by the crude data. This was operationalized by following the technique of Guyot [4], utilizing the website WebPlotDigitalizer and the R programming language [5]. Specifically, the R package IPDfromKM [6] created

Verifying individual data extraction

Quality assessment of our Kaplan–Meier-derived IPD data extraction was performed analytically by comparing our extracted overall hazard ratio and 95% CI with the published values, and graphically by checking the derived Kaplan–Meier (KM) curves (Fig. 1) with the published propensity-matched KM curve (Original Supplemental Fig. 2 [1]). Not only is the data extraction judged to be adequate graphically but also numerically with a calculated HR = 1.02, 95% CI 0.84–1.24 which compares favorably with

Discussion

Bayesian approaches to survival analysis can provide a number of benefits over the classical frequentist approach, including the ability to make direct probability statements about parameters of interest (the risk of pacemaker implantation), and to incorporate prior knowledge. In this Bayesian reanalysis of the recent SWEDEHEART registry publication [1], after reliably extracting the individual patient data, we demonstrated, in contrast to the original publication [1], a moderately high

Funding

JMB is a research scholar supported by Les Fonds de Recherche Québec Santé which had no influence on the choice of topic, the results, or conclusions.

Declaration of Competing Interest

Both authors do not report any potential conflicts of interest.

References (17)

There are more references available in the full text version of this article.

Cited by (0)

1

This author takes responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.

2

This author takes responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.

View full text