ODP233 Predicting Hospital Readmission for Patients with Diabetes: A Comparison of Models

Abstract

Abstract In patients with diabetes, current models for predicting the risk of readmission within 30 days of hospital discharge vary in performance. We previously published the Diabetes Early Readmission Risk Indicator (DERRI TM), a logistic regression (LR) model based on 10 simple features with modest predictive performance (C-statistic 0.69). The current study aims to develop a more accurate model using deep learning on electronic health record (EHR) data. We electronically abstracted data from 36,563 patients with diabetes and at least 1 hospitalization at an urban, academic medical center between 7/1/2010 and 12/31/2020. One hospitalization per patient (index hospitalization) was randomly selected for analysis. A deep learning long short-term memory Recurrent Neural Network (RNN) was developed and compared to traditional linear and non-linear models: LR, AdaBoost, and Random Forest (RF). Models to predict unplanned, all-cause readmission were developed using demographics, vital signs, diagnostic and procedure codes, medications, laboratory tests, and administrative data as defined by the National Patient-Centered Clinical Research Network (PCORnet) Common Data Model. Unplanned readmissions were identified according to the Centers for Medicare and Medicaid (CMS) definition. A look-back time of 1 year before the index hospitalization and up to 60 previous ambulatory and hospital visits were used for learning and inference. Data dimensionality was reduced to 3,000 features by Singular Value Decomposition. The RNN model C-statistic is significantly greater than those of the traditional models (RNN 0.78, AdaBoost 0.71, RF 0.73, and LR 0.71, p<0. 0001). Likewise, the F1-score is numerically greater for the RNN model (RNN 0.76, AdaBoost 0.75, RF 0.75, LR 0.72). Direct comparison to the DERRI TM is limited by lack of EHR data on two of the component variables (employment status and zip code). The deep learning RNN model outperforms the DERRI TM and is based on more generalizable EHR data. This new model may provide the basis for a more useful readmission risk prediction tool for patients with diabetes. Deep learning models may outperform traditional models at predicting readmission risk in this population. Presentation: No date and time listed