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Page 4 of 15 Novotny et al. Vessel Plus 2022;6:51 https://dx.doi.org/10.20517/2574-1209.2021.139

compare the unadjusted marginal differences in continuous variables (age, Elixhauser readmission score,
Elixhauser mortality score) by AF/AFL+ or by binary outcomes (MM and READMIT).

As the first step, the cardiac literature was carefully reviewed to identify the patients’ risk factors to be
considered as model-eligible for the MM and READMIT multivariable logistic regression models. Based on
these initial literature-based conceptual variable lists, endpoint-specific bivariate screenings (P-value < 0.10)
were evaluated to identify potential associations; additionally, these variables’ effect sizes were ordered to
identify the optimal multivariable model-eligible variables. Importantly, variables were removed from
model eligibility consideration for any coding completeness issues, clinical interpretability challenges, or
collinearity with well-established AVR risk factors. Given inherent sample size (n = 334) limitations, the
number of MM and READMIT endpoints was divided by 10 to identify the maximum number of
multivariable risk model-eligible variables; thus, there were 6 MM model-eligible variables and 5 READMIT
model-eligible variables pre-screened to be included in multivariable models [18,19] . Similarly, a multivariable
logistic regression model reported the other patient characteristics that were most commonly associated
with pre-operative AF/AFL (n = 152 AF/AFL patients; 15 AF/AFL pre-screened variables were included as
AF/AFL model-eligible).

Based on nested c-index comparisons, the multivariable models containing the Elixhauser score performed
better than models utilizing Elixhauser-related comorbidities; thus, the Elixhauser score’s weighted sub-
components were not further considered as model-eligible variables . In each logistic regression analysis,
[20]
an odds ratio (OR) > 1.00 indicated an adverse outcome impact, while an OR < 1.00 indicated a protective
effect. Observed/expected (O/E) ratios were calculated using baseline regression models without the key
variables of interest compared; however, final study regression models directly assessed the impact of these
key variables of interest. For all analyses performed, the protocol-driven statistical significance threshold
was set at P < 0.05; however, all unadjusted p-values are reported for independent review. Statistical analysis
was performed using SAS 9.4 (SAS Institute Inc., Cary, NC).

RESULTS
Data extraction isolated 74,675 AVR records and identified 73,945 patients undergoing an initial AVR
procedure, among which 36,783 were kept after performing exclusion criteria. Following these AVR
patients subsequently, there were 334 patients with r-AVR records meeting all study inclusion and exclusion
criteria [Figure 1]. Of these, 205 patients underwent r-SAVR; 42.4% of r-SAVR patients had pre-operative
AF/AFL. In the viv-TAVR group of 129 patients, 50.4% had pre-operative AF/AFL. Comparing r-SAVR to
viv-TAVR patients, there was no difference in the baseline AF/AFL rates (P = 0.156).

Baseline patient characteristics
As described in Table 1, r-SAVR AF/AFL+ patients were significantly older (mean ± standard deviation:
69.92 ± 11.06 vs. 59.17 ± 14.00, P < 0.001) and less often Hispanic (1.2% vs. 8.5%, P = 0.029) compared
AF/AFL-patients. For r-SAVR patients with AF/AFL+ versus AF/AFL-, cerebrovascular disease (19.5% vs.
8.5%, P = 0.021), permanent pacemaker or implantable cardiac defibrillator (16.1% vs. 4.2%, P = 0.004),
hyperlipidemia (63.2% vs. 46.6%, P = 0.018), rheumatic heart disease (11.5% vs. 3.4%, P = 0.028), fluid and
electrolyte disorders (4.6% vs. 0.0%, P = 0.032), and pulmonary hypertension (26.4% vs. 11.9%, P = 0.007)
were more frequently reported. Although viv-TAVR AF/AL+ versus AF/AFL- patients had similar
distributions for demographics and comorbidities, there were no statistically significant risk factor
differences other than AF/AFL+ viv-TAVR patients had lower rates of chronic obstructive pulmonary
disease (15.4% vs. 31.3%, P = 0.033).