Impact of Social Determinants of Health on Predictive Models for Outcomes After Congenital Heart Surgery

Background Despite documented associations between social determinants of health and outcomes post–congenital heart surgery, clinical risk models typically exclude these factors. Objectives The study sought to characterize associations between social determinants and operative and longitudinal mortality as well as assess impacts on risk model performance. Methods Demographic and clinical data were obtained for all congenital heart surgeries (2006-2021) from locally held Congenital Heart Surgery Collaborative for Longitudinal Outcomes and Utilization of Resources Society of Thoracic Surgeons Congenital Heart Surgery Database data. Neighborhood-level American Community Survey and composite sociodemographic measures were linked by zip code. Model prediction, discrimination, and impact on quality assessment were assessed before and after inclusion of social determinants in models based on the 2020 Society of Thoracic Surgeons Congenital Heart Surgery Database Mortality Risk Model. Results Of 14,173 total index operations across New York State, 12,321 cases, representing 10,271 patients at 8 centers, had zip codes for linkage. A total of 327 (2.7%) patients died in the hospital or before 30 days, and 314 children died by December 31, 2021 (total n = 641; 6.2%). Multiple measures of social determinants of health explained as much or more variability in operative and longitudinal mortality than clinical comorbidities or prior cardiac surgery. Inclusion of social determinants minimally improved models’ predictive performance (operative: 0.834-0.844; longitudinal 0.808-0.811), but significantly improved model discrimination; 10.0% more survivors and 4.8% more mortalities were appropriately risk classified with inclusion. Wide variation in reclassification was observed by site, resulting in changes in the center performance classification category for 2 of 8 centers. Conclusions Although indiscriminate inclusion of social determinants in clinical risk modeling can conceal inequities, thoughtful consideration can help centers understand their performance across populations and guide efforts to improve health equity.