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referral bias may have occurred, where higher-risk first-time AVR patients did not survive or were not
referred as candidates for r-AVR procedures.

This study has identified several factors putting patients at high risk of adverse r-AVR outcomes, including
black race, Elixhauser mortality score, and cerebrovascular disease. These high-risk groups may require
additional attention from the clinical care team to consider options for mitigating these increased risks for
adverse events following r-AVR procedures and facilitating closer post-discharge monitoring for “at-risk”
patients. Furthermore, clinicians may wish to consider these findings during their pre-operative evaluations
regarding patient referrals for r-AVR interventions as well as during patient-clinician discussions of
informed consent. Importantly, additional research appears warranted to evaluate the impact of pre-
operative AF/AFL patients’ long-term outcomes.

Limitations
As an observational database analysis, this study has several inherent limitations. As with any retrospective
cohort study, there may have been unknown confounding factors impacting the MM composite and/or
READMIT endpoints. To address this limitation, however, all literature-based risk factors that were
previously identified with a potential association with the endpoints were evaluated in this r-AVR study.

Additionally, this study was limited by a small sample size (n = 334). As a follow-up to this study, future
analyses of a much larger r-AVR database should be planned. Given these preliminary findings, future
research should utilize a database containing at least 15,518 r-AVR records to detect a difference in the
READMIT endpoint; for the MM endpoint, a future study should plan to utilize a database of at least 5857
r-AVR records. These future sample size projections were based on a power of 80% and a significance level
of 0.05. Given these findings, however, national databases (e.g., the MEDPAR or Cerner national database)
will be required to address this question more rigorously. Thus, these preliminary findings based on the
New York State AF/AFL patients’ experience should be re-verified by testing these same hypotheses in a
larger r-AVR population.

Across SPARCS hospitals’ r-AVR procedures reported, this study focused on the population of adult New
York State residents; thus, the post-procedural follow-up endpoints (i.e., 30-day readmissions and 30-day
operative death) might be most accurate. Given that children (under age 18) may have other complex
congenital cardiac abnormalities requiring phased sets of cardiac procedures, these were removed. As New
York residents may have differential risks, moreover, their findings may not be reliably generalized to other
populations with a different risk profile.

As this SPARCS billing database was used to drive hospital reimbursement, the billing codes have been
assumed to be reasonably accurate. However, SPARCS administrative billing errors in coding may exist,
particularly for the subgroup of billing codes not directly tied to differential reimbursement. As hospital
billing codes transitioned from ICD-9 to ICD-10 in October 2015, moreover, there may have been
transition-related coding-related inconsistency challenges for patient risk factors, treatment, and outcome
codes. As the transition of ICD-9 codes to the newer ICD-10 codes may have been imperfect, a historical
“look back” period of 2-years was used to augment the new ICD-10 complication classification codes; this
approach assured that comorbidities (i.e., important risk factor diagnoses existing pre-procedure, such as
the patient having a prior stroke pre-procedure) could be differentiated from complications (i.e., new
diagnoses arising post-procedure, such as a patient having a perioperative stroke). Given the SPARCS
database does not contain current procedural terminology codes (i.e., cardiac interventionalist billing codes)
regarding all of the cardiac procedure’s details (e.g., type and size of prosthetic valve implant) or