Abstract 161: Adding Social and Functional Determinants of Health Improves Readmission Risk Prediction in Urban Underserved Adults Hospitalized for Acute Myocardial Infarction

Abstract

Background: Although social and functional determinants of health (SFDoH) are increasingly recognized to contribute to increased 30-day readmission risk for cardiovascular illnesses, most health systems lack individual-level data to enable individual risk stratification and targeted interventions to address these needs. We sought to determine whether electronic health record (EHR) data in SFDoH domains collected as a part of routine inpatient care from a safety-net hospital could be used to enhance prediction of 30-day readmissions beyond traditional clinical risk factors alone among underserved adults hospitalized with acute myocardial infarction (AMI). Methods: We used Epic EHR data on AMI hospitalizations from 2014-2017 in an urban safety-net hospital in Dallas to predict non-elective 30-day readmissions to any of 75 hospitals in north Texas, using a regional all-payer hospitalization database. We started with the AMI READMITS score, our previously validated model consisting of 7 clinical risk factors. Next, we derived and validated an enhanced model incorporating SFDoH predictors. EHR data for candidate SFDoH predictors included structured, semi-structured, and short free-text fields from demographics, encounter types, note types, and case manager, social worker, and nursing inpatient flowsheets. We selected novel predictors using stepwise backward selection with a threshold of p≤0.20, forcing in the composite AMI READMITS score. We conducted sensitivity analyses using forward selection, lasso regression, and random forest which resulted in similar models. We internally validated our final model using 5-fold cross-validation and compared model performance to the AMI READMITS score. Results: Of 1,071 hospitalizations, 154 (14.5%) were readmitted within 30 days. In addition to the composite AMI READMITS score, our final model included 9 SFDoH risk factors, including neighborhood poverty (aOR 1.39, 95% CI 0.91-2.14), address change in the past year (aOR 1.53, 95% CI 0.96-2.46), any opioid use in the past year (aOR 2.35, 95% CI 1.36-4.05), history of durable medical equipment use in the past year (aOR 1.70, 95% CI 1.02-2.82), and ADL impairments in feeding, bathing, and toileting in the past year (aOR 1.81 for all three impairments, 95% CI 0.99-3.29). The AMI READMITS+SFDoH model had better discrimination than AMI READMITS alone (optimism-corrected C-statistic 0.69 vs 0.62, p<0.001), was able to predict a broader range of probabilities for readmission risk (3.6-72.7% vs. 8.2-37.2%), and improved net reclassification (categorical NRI of highest 2 risk quintiles vs. lowest 3 of 0.09, 95% CI 0.00-0.18). Conclusions: Incorporating existing EHR data on social and functional determinants of health improved readmission risk prediction after an AMI hospitalization in an urban underserved population.