Objective: Low socioeconomic status (SES) is associated with worse surgical outcomes. Current risk adjustment models for aortic valve replacement (AVR) surgery do not include (SES), and therefore centers that treat large numbers of low-SES patients may be disadvantaged in hospital outcomes comparisons. This study evaluates whether inclusion of SES improves AVR risk prediction models.
Methods: All patients undergoing isolated aortic valve replacement (AVR) at a single institution from 2005-2015 were evaluated. We estimated patients’ SES using census-tract-level data, which are more precise than ZIP-code-level data. We excluded patients (~5%) with addresses that could not be geolocated to census tracts. SES covariates were available for 95% of the study population. SES measures included mean rates of unemployment, poverty, household income, home value, educational attainment, and housing density. The risk scores for mortality, complications and increased length of stay were generated using models published by the Society for Thoracic Surgeons. Univariate models were fitted for each SE covariate with a cut-off of p <0.2 for inclusion in the multivariable models for (a) mortality, (b) any complication, and (c) prolonged length of stay (PLOS) in addition to the expected risk. We evaluated the incremental value of SES covariates using area under the curve (AUC).
Results: Amongst the 1,386 patients undergoing AVR included in the study, the overall mortality was 2.8%, any complication rate was 15.1% and PLOS was 9.7%. In univariate models, higher education quartile was associated with decreased mortality (OR 0.96, p = 0.04) and complications (OR 0.97, p <0.01). Poverty was associated with increased length of stay (LOS) (OR 1.02, p =0.02). In the multivariable models, the inclusion of SES covariates increased the area under the curve (AUC) for mortality (0.735 to 0.750, p=0.14), for any complications (0.663 to 0.680, p<0.01), and for PLOS (0.749 to 0.751, p=.12)
Conclusions: The inclusion of census-tract-level socioeconomic factors into the STS risk predication models is new and shows potential to improve risk prediction for outcomes following AVR, particularly for predictions of any complications following AVR. With the possibility of reimbursement and institutional ranking based on these outcomes, this study represents an improvement in risk predication model even when limited to census tracts and a single institution’s experience.