Multicenter Development and Validation of a Multimodal Deep Learning Model to Predict Moderate to Severe AKI

Abstract

Key Points We developed and validated a multimodal (structured and unstructured data) model to predict moderate to severe AKI using multicenter data. This multimodal AKI risk score accurately identifies patients who will develop stage 2 AKI over 2 days earlier than serum creatinine alone. The multimodal model performed better than a model based solely on structured data and performed similarly during temporal and site-based validation. Background Prior models for the early identification of AKI have used structured data ( e.g ., vital signs and laboratory values). We aimed to develop and validate a deep learning model to predict moderate to severe AKI by combining structured data and information from unstructured notes. Methods Adults (18 years or older) admitted to the University of Wisconsin (2009–2020) and the University of Chicago Medicine (2016–2022) were eligible for inclusion. Patients were excluded if they had no documented serum creatinine (SCr), ESKD, an admission SCr ≥3.0 mg/dl, developed ≥stage 2 AKI before reaching the wards or intensive care unit, or required dialysis (KRT) within the first 48 hours. Text from unstructured notes was mapped to standardized concept unique identifiers to create predictor variables, and structured data variables were also included. An intermediate fusion deep learning recurrent neural network architecture was used to predict ≥stage 2 AKI within the next 48 hours. This multimodal model was developed in the first 80% of the data and t…