Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis

Abstract

Acute kidney injury (AKI) is commonly present in critically ill patients with sepsis. Early prediction of short-term reversibility of AKI is beneficial to risk stratification and clinical treatment decision. The study sought to use machine learning methods to discriminate between transient and persistent sepsis-associated AKI. Septic patients who developed AKI within the first 48 h after ICU admission were identified from the Medical Information Mart for Intensive Care III database. AKI was classified as transient or persistent according to the Acute Disease Quality Initiative workgroup consensus. Five prediction models using logistic regression, random forest, support vector machine, artificial neural network and extreme gradient boosting were constructed, and their performance was evaluated by out-of-sample testing. A simplified risk prediction model was also derived based on logistic regression and features selected by machine learning algorithms. A total of 5984 septic patients with AKI were included, 3805 (63.6%) of whom developed persistent AKI. The artificial neural network and logistic regression models achieved the highest area under the receiver operating characteristic curve (AUC) among the five machine learning models (0.76, 95% confidence interval [CI] 0.74–0.78). The simplified 14-variable model showed adequate discrimination, with the AUC being 0.76 (95% CI 0.73–0.78). At the optimal cutoff of 0.63, the sensitivity and specificity of the simplified model were 63% and 76% respectively. In conclusion, a machine learning-based simplified prediction model including routine clinical variables could be used to differentiate between transient and persistent AKI in critically ill septic patients. An easy-to-use risk calculator can promote its widespread application in daily clinical practice.

Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury

“…Advanced machine learning methods are adept at handling high-order interactions and fitting complex non-linear relationships, which can be used to integrate large amounts of data from electronic health records (EHRs). The application of data-driven analytics by machine learning has shown promise to improve predictive performance in medical fields ( 12 – 15 ).…”

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

“…The prediction models established by these studies were limited by only using the logistic regression method rather than other machine learning models to improve the predictive ability. Moreover, a study including 5984 septic patients with AKI established five prediction models, including logistic regression, random forest, support vector machine, artificial neural network, and extreme gradient boosting to predict persistent AKI [22]. The artificial neural network and logistic regression models achieved the highest AUC of 0.76.…”

“…Machine learning models have already been applied in many fields, such as outcome prediction [17][18][19], and these models may potentially be used to identify high-risk patients. Machine learning models have been mostly described to predict episodes of the occurrence of AKI during sepsis [20][21][22]. However, there is no study to evaluate their effects on rehospitalization with AKI after patients who survived to discharge from sepsis.…”

Artificial Intelligence for Risk Prediction of End-Stage Renal Disease in Sepsis Survivors with Chronic Kidney Disease

“…We used clinical indicators to predict the future risk of ESRD by using machine learning models. In previous studies that used AI to predict the progression of renal function, the primary endpoints were the occurrence of AKI or acute kidney disease following ICU admission, cardiovascular surgery, or sepsis [17][18][19]. Nevertheless, the follow-up period of 1-2 years in these studies was too short to effectively assess the long-term eGFR decline or progression to ESRD.…”

“…The application of artificial intelligence (AI) in the early prediction and risk stratification of AKI has attracted considerable research attention. Previous studies have demonstrated the use of machine learning algorithms for the early prediction of AKI 48 h after intensive care unit (ICU) admission and for distinguishing between transient and persistent AKI in patients following sepsis or cardiac surgery [17][18][19]. Nevertheless, these algorithms could not predict subsequent ESRD development.…”