Artificial Intelligence for Risk Prediction of Rehospitalization with Acute Kidney Injury in Sepsis Survivors.

Shuo-Ming Ou,Der-Cherng Tarng,Ming-Tsun Tsai,Wei-Cheng Tseng,Kuo-Hua Lee,Yuan-Chia Chu

doi:10.3390/jpm12010043

Abstract

Sepsis survivors have a higher risk of long-term complications. Acute kidney injury (AKI) may still be common among sepsis survivors after discharge from sepsis. Therefore, our study utilized an artificial-intelligence-based machine learning approach to predict future risks of rehospitalization with AKI between 1 January 2008 and 31 December 2018. We included a total of 23,761 patients aged ≥ 20 years who were admitted due to sepsis and survived to discharge. We adopted a machine learning method by using models based on logistic regression, random forest, extra tree classifier, gradient boosting decision tree (GBDT), extreme gradient boosting, and light gradient boosting machine (LGBM). The LGBM model exhibited the highest area under the receiver operating characteristic curves (AUCs) of 0.816 to predict rehospitalization with AKI in sepsis survivors and followed by the GBDT model with AUCs of 0.813. The top five most important features in the LGBM model were C-reactive protein, white blood cell counts, use of inotropes, blood urea nitrogen and use of diuretics. We established machine learning models for the prediction of the risk of rehospitalization with AKI in sepsis survivors, and the machine learning model may set the stage for the broader use of clinical features in healthcare.

Highlights

Sepsis is estimated to affect 19.4 million patients, with an annual sepsis-related mortality of approximately 5.3 million cases [1]
23,761 sepsis survivors suffered from rehospitalization with Acute kidney injury (AKI) after discharge
We developed machine learning algorithms using 84 clinical features to predict rehospitalization with AKI and compared the area under the receiver operating characteristic curves (AUCs) of the different machine learning models

Summary

Introduction

Sepsis is estimated to affect 19.4 million patients, with an annual sepsis-related mortality of approximately 5.3 million cases [1]. As there has been significant medical progress in decreasing mortality and morbidity after sepsis, attention to the complications after discharge in sepsis survivors has become more important [4,5,6,7]. Previous studies have found that 40% to 50% of patients with AKI had sepsis [8,11], and approximately 11% to 42% of patients with sepsis developed AKI [12,13,14]. AKI is a common complication in sepsis, the risks of rehospitalization with AKI in sepsis survivors remains unknown. The development of a prediction model for rehospitalization with AKI has become an important therapeutic goal in the management of sepsis survivors

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Discussion

Conclusion