Development and Comparative Analysis of an Early Prediction Model for Acute Kidney Injury within 72-Hours Post-ICU Admission Using Evidence from the MIMIC-III Database

Abstract

Background: Prompt recognition of patients predisposed to acute kidney injury (AKI) within 72 hours of intensive care unit (ICU) admission holds significant clinical importance as it can considerably lower mortality rates. However, existing AKI prediction models often require complex data collection yet yield only moderate performance. This study aims to develop a straightforward and efficient AKI prediction model, providing ICU physicians with a powerful tool to expedite the detection of AKI patients.

Methods: This study proposed a novel generative adversarial imputation networks-least absolute shrinkage and selection operator-extreme gradient boosting (Gain-Lasso-XGBoost) framework and developed an AKI prediction model on the basis of the medical information mart for intensive care (MIMIC-III) database. All the steps, including data preprocessing, feature selection, development, and optimization of prediction models, are organically integrated into the framework which has strong scalability. To compare the performance of our model with current models, we conducted a systematic review to collect all studies on the basis of the MIMIC-III database with similar objectives.

Results: From 15 demographic and clinical variables, 8 features and 5 features were identified as the optimal group of features and processed into the model development. The model optimization further improved the performance of our proposed framework, and the area under curve (AUC) results with 8 and 5 feature vectors achieved 0.849 and 0.830, respectively. Compared with other studies, our method extracted only 8 or 5 feature vectors and obtained superior performance, with an average AUC 1.9% higher than the state-of-the-art approaches in the same type.

Conclusions: Our study suggested that the onset of AKI be effectively and quickly predicted using simplified features, and not just for more specific patient groups. It may help clinicians accurately identify patients at risk of AKI after ICU admission and provide timely monitoring and treatment.

Keywords: acute kidney injury; clinical decision support; intensive care unit; model interpretation; prediction model.