Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements

Abstract

Background: The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality.

Methods: Our objective was to develop and validate a data driven multivariable clinical predictive model for early detection of AKI among a large cohort of adult critical care patients. We utilized data form the Medical Information Mart for Intensive Care III (MIMIC-III) for all patients who had a creatinine measured for 3 days following ICU admission and excluded patients with pre-existing condition of Chronic Kidney Disease and Acute Kidney Injury on admission. Data extracted included patient age, gender, ethnicity, creatinine, other vital signs and lab values during the first day of ICU admission, whether the patient was mechanically ventilated during the first day of ICU admission, and the hourly rate of urine output during the first day of ICU admission.

Results: Utilizing the demographics, the clinical data and the laboratory test measurements from Day 1 of ICU admission, we accurately predicted max serum creatinine level during Day 2 and Day 3 with a root mean square error of 0.224 mg/dL. We demonstrated that using machine learning models (multivariate logistic regression, random forest and artificial neural networks) with demographics and physiologic features can predict AKI onset as defined by the current clinical guideline with a competitive AUC (mean AUC 0.783 by our all-feature, logistic-regression model), while previous models aimed at more specific patient cohorts.

Conclusions: Experimental results suggest that our model has the potential to assist clinicians in identifying patients at greater risk of new onset of AKI in critical care setting. Prospective trials with independent model training and external validation cohorts are needed to further evaluate the clinical utility of this approach and potentially instituting interventions to decrease the likelihood of developing AKI.

Keywords: Acute kidney injury; Artificial neural networks; Intensive care unit; Multivariate logistic regression; Physiological measurements; Predictive modeling; Random forest.

Fig. 1

Scatter plots comparing the measured and predicted creatinine values using linear regression models with a) backward selection variable model and b) all variables model after cross-validation. Note that the axes are on a logarithmic scale or plot log transformed data

Fig. 2

ROC curves for logistic regression, random forest, and multilayer perceptron models using a) backward selection model and b) all-feature model using cross-validation. We repeat the 5-fold cross validation 10 times, each time using stratified 5-fold split with different random initializations. We use different colors for ROC curves from different cross validations. Note that for both for both the all-variable and backward selection models, the model performance is insensitive to stratified 5-fold splits with different random initializations. Thus, the ROC curves are almost identical to each other