A time-aware attention model for prediction of acute kidney injury after pediatric cardiac surgery. (26th October 2022)

• Record Type: Journal Article
• Authors: Zeng, Xian; Shi, Shanshan; Sun, Yuhan; Feng, Yuqing; Tan, Linhua; Lin, Ru; Li, Jianhua; Duan, Huilong; Shu, Qiang; Li, Haomin
• Is Part Of: Journal of the American Medical Informatics Association. Volume 30:Number 1(2023)
• Page Start: 94
• Page End: 102
• Publication Date: 2022-10-26
• Subjects: pediatric cardiac surgery -- acute kidney injury -- prediction model -- deep machine learning -- multi-perspectives evaluation; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac202
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Acute kidney injury (AKI) is a common complication after pediatric cardiac surgery, and the early detection of AKI may allow for timely preventive or therapeutic measures. However, current AKI prediction researches pay less attention to time information among time-series clinical data and model building strategies that meet complex clinical application scenario. This study aims to develop and validate a model for predicting postoperative AKI that operates sequentially over individual time-series clinical data. Materials and Methods: A retrospective cohort of 3386 pediatric patients extracted from PIC database was used for training, calibrating, and testing purposes. A time-aware deep learning model was developed and evaluated from 3 clinical perspectives that use different data collection windows and prediction windows to answer different AKI prediction questions encountered in clinical practice. We compared our model with existing state-of-the-art models from 3 clinical perspectives using the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision-recall curve (PR AUC). Results: Our proposed model significantly outperformed the existing state-of-the-art models with an improved average performance for any AKI prediction from the 3 evaluation perspectives. This model predicted 91% of all AKI episodes using data collected at 24 h after surgery, resulting in a ROC AUC of 0.908 and a PR AUC of 0.898. On average, our model predicted 83% of all AKI episodes that occurred within the different time windows in the 3 evaluation perspectives. The calibration performance of the proposed model was substantially higher than the existing state-of-the-art models. Conclusions: This study showed that a deep learning model can accurately predict postoperative AKI using perioperative time-series data. It has the potential to be integrated into real-time clinical decision support systems to support postoperative care planning.