Geographically Derived Socioeconomic Factors to Improve Risk Prediction in Patients Having Aortic Valve Replacement.

McCarthy FH, Zhang L, Tam V, Chen J, Brown C, Patrick WL, Clark Hargrove W, Szeto WY, Desai ND, Wiebe DJ, Groeneveld PW, Williams ML. Geographically Derived Socioeconomic Factors to Improve Risk Prediction in Patients Having Aortic Valve Replacement. The American journal of cardiology. 2019 Jan 1; 123(1):116-122.

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Abstract:

Socioeconomic status (SES) has been associated with adverse outcomes after cardiac surgery, but is not included in commonly applied risk adjustment models. This study evaluates whether inclusion of SES improves aortic valve replacement (AVR) risk prediction models, as this is the most common elective operation performed at our institution during the study period. All patients who underwent AVR at a single institution from 2005 to 2015 were evaluated. SES measures included unemployment, poverty, household income, home value, educational attainment, housing density, and a validated SES index score. 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 SES covariate and multivariable models for mortality, any complication, and prolonged length of stay (PLOS). A total of 1,386 patients underwent AVR with a 2.7% mortality, 15.1% complication rate, and 9.7% PLOS. In univariate models, higher education was associated with decreased mortality (odds ratio [OR] 0.96, p = 0.04) and complications (OR 0.97, p < 0.01). Poverty was associated with increased length of stay (OR 1.02, p = 0.02). In the multivariable models, the inclusion of SES covariates increased the area under the curve 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 = 0.12). The inclusion of census-tract-level socioeconomic factors into the the Society of Thoracic Surgeons risk predication models is new and shows potential to improve risk prediction for outcomes after cardiac surgery. With the possibility of reimbursement and institutional ranking based on these outcomes, this study represents an improvement in risk prediction model.