Machine Learning Models for Predicting Acute Kidney Injury in Patients With Sepsis-Associated Acute Respiratory Distress Syndrome

Abstract

Background: Acute kidney injury (AKI) is a prevalent and serious complication among patients with sepsis-associated acute respiratory distress syndrome (ARDS). Prompt and accurate prediction of AKI has an important role in timely intervention, ultimately improving the patients' survival rate. This study aimed to establish machine learning models to predict AKI via thorough analysis of data derived from electronic medical records. Method: The data of eligible patients were retrospectively collected from the Medical Information Mart for Intensive Care III database from 2001 to 2012. The primary outcome was the development of AKI within 48 hours after intensive care unit admission. Four different machine learning models were established based on logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The performance of all predictive models was evaluated using the area under receiver operating characteristic curve, precision-recall curve, confusion matrix, and calibration plot. Moreover, the discrimination ability of the machine learning models was compared with that of the Sequential Organ Failure Assessment (SOFA) model. Results; Among 1,085 sepsis-associated ARDS patients included in this research, 375 patients (34.6%) developed AKI within 48 hours after intensive care unit admission. Twelve predictive variables were selected and further used to establish the machine learning models. The XGBoost model yielded the most accurate predictions with the highest area under receiver operating characteristic curve (0.86) and accuracy (0.81). In addition, a novel shiny application based on the XGBoost model was established to predict the probability of developing AKI among patients with sepsis-associated ARDS. Conclusions: Machine learning models could be used for predicting AKI in patients with sepsis-associated ARDS. Accordingly, a user-friendly shiny application based on the XGBoost model with reliable predictive performance was released online to predict the probability of developing AKI among patients with sepsis-associated ARDS.

Man Versus Machine: Provider Directed Versus Precision Automated Critical Care Management in a Porcine Model of Distributive Shock

“…At present, multiple machine learning techniques are being employed in critical care studies. Specific applications include early detection of acute kidney injury, pulmonary embolism, gene expression in sepsis, and leukocyte phenotyping when trying to understand the pathophysiology of sepsis (45)(46)(47)(48)(49). Reinforcement learning has also been used to formulate ICU electrolyte replacement protocols and determine treatment decisions in sepsis (50,51).…”

Machine learning-based prediction model of acute kidney injury in patients with acute respiratory distress syndrome

“…These results indicate that ML has good predictive value for in-hospital mortality rate in ARDS patients caused by trauma. In addition, a study constructed a prognostic model for sepsis-induced ARDS patients using ML to predict the occurrence of AKI within 48 hours of admission to the ICU, achieving a high AUC of 0.86 and accuracy of 0.81 [23]. Moreover, several other studies have demonstrated the ability of ML to predict the duration of mechanical ventilation in ARDS, indicating that it can provide early and accurate predictions for MV duration in ARDS [13,24].…”

Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury

“…In recent years, there has been a growing interest in utilizing machine learning (ML) algorithms for diagnostic and prognostic disease studies. These ML models have shown to surpass traditional scoring methods in terms of predictive accuracy ( 15 , 16 ). In our study, we also observed that machine learning models outperformed conventional scoring systems in all 28-day mortality prediction for S-AKI patients.…”

“…Zhou et al utilized data from the MIMIC III database to create a machine learning model for predicting AKI within 48 h of sepsis-related ARDS cases. Their model outperformed the discriminatory ability of SOFA ( 16 ). This highlights the potential of machine learning algorithms in accurately predicting the development of S-AKI.…”