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. 2019 Aug 21:264:462-466.
doi: 10.3233/SHTI190264.

Stratified Mortality Prediction of Patients with Acute Kidney Injury in Critical Care

Zhenxing Xu  1 , Yuan Luo  2 , Prakash Adekkanattu  1 , Jessica S Ancker  1 , Guoqian Jiang  3 , Richard C Kiefer  3 , Jennifer A Pacheco  2 , Luke V Rasmussen  2 , Jyotishman Pathak  1 , Fei Wang  1
Affiliations

Stratified Mortality Prediction of Patients with Acute Kidney Injury in Critical Care

Zhenxing Xu et al. Stud Health Technol Inform. .

Abstract

Acute Kidney Injury (AKI) is the most common cause of organ dysfunction in critically ill adults and prior studies have shown AKI is associated with a significant increase of the mortality risk. Early prediction of the mortality risk for AKI patients can help clinical decision makers better understand the patient condition in time and take appropriate actions. However, AKI is a heterogeneous disease and its cause is complex, which makes such predictions a challenging task. In this paper, we investigate machine learning models for predicting the mortality risk of AKI patients who are stratified according to their AKI stages. With this setup we demonstrate the stratified mortality prediction performance of patients with AKI is better than the results obtained on the mixed population.

Keywords: Acute Kidney Injury; Critical Care; Forecasting.

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Figures

Figure 1 -
Figure 1 -
An illustration of mortality prediction for patients with AKI in different stages. PP: Prediction Point; OW: Observation Window; PW: Prediction Window
Figure 2 -
Figure 2 -
The AUC of different methods with 24–144 hours of data observation window in terms of different AKI stages
See this image and copyright information in PMC

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