Development and validation of quick Acute Kidney Injury-score (q-AKI) to predict acute kidney injury at admission to a multidisciplinary intensive care unit

et al. 2020

Preprint

Background Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. Our previous study has shown that patients who will progress to AKI 3 stage are considered to receive RRT. This study aimed to develop a prediction model that can predict whether progression to AKI stage 3. Methods Patients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care (MIMIC-III), were included. Patients who receive RRT or progress to AKI 3 stage within 72 hours of first AKI diagnosis were excluded. We build two predictive models, respectively using machine learning extreme gradient boosting (XGBoost) and logistic regression, to predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation and area under receiver operating characteristic curve (AU-ROC). Results Of the 29238 patients included in the analysis, 3237 (11.1%) patients progressed to AKI stage 3. Creatinine, blood urea nitrogen (BUN), sepsis and respiratory failure were the important predictors of AKI progression. The machine learning XGBoost model has a better performance than the Cox regression model on predicting AKI stage 3 progression (AU-ROC, 0.860 vs. 0.728, respectively). Conclusions The XGBoost model was able to identify patients with AKI progression better than the Cox regression model. Machine learning techniques may improve predictive modeling in medical research.

Section: The Stepwise Cox Regression Modelsupporting

confidence: 75%

WITHDRAWN: Machine Learning for the Prediction of Progression in Patients With Acute Kidney Injury in Critical Care

et al. 2020

Preprint

“…After excluding the variables with high colinearity through the variance inflation factor (VIF), the final variables included in the analysis are as follows. As expected, with MODS (odds ratio[OR], 1.55; 95% confidence interval [14], 1.50 to 1.60), sepsis(OR, 1.71; 95% CI, 1.60 to 1.82), respiratory failure (OR, 1.47; 95% CI, 1.41 to 1.54), and creatinine (OR, 1.20; 95% CI, 1.15 to 1.25) were associated with increased probability of AKI progression(Table 1). Besides, BUN, lactate, and so on are also considered to be associated with AKI progression.…”

Section: The Logistic Regression Modelsupporting

confidence: 75%

Machine Learning for the Prediction of Progression in Patients with Acute Kidney Injury in Critical Care

Feng

et al. 2021

Preprint

Background: Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3. Methods: Patients with AKI stage 1/2, when they were ﬁrst diagnosed with AKI in the Medical Information Mart for Intensive Care (MIMIC-III), were included. We excluded patients who had underwent RRT or progressed to AKI stage 3 within 72 hours of the ﬁrst AKI diagnosis. We also excluded patients with chronic kidney disease (CKD). We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve (ROC), and precision-recall curves (PRC). Results: We included 25711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes (MODS), blood urea nitrogen (BUN), sepsis, and respiratory failure were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression (AU-ROC, 0.926; 95%CI, 0.917 to 0.931 vs. 0.784; 95%CI, 0.771 to 0.796, respectively). Conclusions: The XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research. Keywords: Acute kidney injury; Critical care; Logistic Models; Extreme gradient boosting

“…S1. After excluding the variables with high colinearity through the variance inflation factor (VIF) [ 26 ], the final variables included in the analysis are as follows. As expected, with MODS (odds ratio [OR] 1.55; 95% confidence interval 1.50 to 1.60), sepsis (OR 1.71; 95% CI 1.60 to 1.82), respiratory failure (OR 1.47; 95% CI 1.41 to 1.54), and creatinine (OR 1.20; 95% CI 1.15 to 1.25) were associated with increased probability of AKI progression(Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

Machine learning model for predicting acute kidney injury progression in critically ill patients

BMC Med Inform Decis Mak

Feng

et al. 2022

Background Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. Comparing to the patients with AKI stage 1/2, the patients with AKI stage 3 have higher in-hospital mortality and risk of progression to chronic kidney disease. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3. Methods Patients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care, were included. We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve, and precision–recall curves. Results We included 25,711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression. Thus, we build a software based on our data which can predict AKI progression in real time. Conclusions The XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research.