Pacemaker risk following transcatheter aortic valve replacement - A Bayesian reanalysis
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.
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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.