Abstract
BackgroundPrevious scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing continuous renal replacement therapy (CRRT) for severe acute kidney injury. Accordingly, the present study applies machine learning algorithms to improve prediction accuracy for this patient subset.MethodsWe randomly divided a total of 1571 adult patients who started CRRT for acute kidney injury into training (70%, n = 1094) and test (30%, n = 477) sets. The primary output consisted of the probability of mortality during admission to the intensive care unit (ICU) or hospital. We compared the area under the receiver operating characteristic curves (AUCs) of several machine learning algorithms with that of the APACHE II, SOFA, and the new abbreviated mortality scoring system for acute kidney injury with CRRT (MOSAIC model) results.ResultsFor the ICU mortality, the random forest model showed the highest AUC (0.784 [0.744–0.825]), and the artificial neural network and extreme gradient boost models demonstrated the next best results (0.776 [0.735–0.818]). The AUC of the random forest model was higher than 0.611 (0.583–0.640), 0.677 (0.651–0.703), and 0.722 (0.677–0.767), as achieved by APACHE II, SOFA, and MOSAIC, respectively. The machine learning models also predicted in-hospital mortality better than APACHE II, SOFA, and MOSAIC.ConclusionMachine learning algorithms increase the accuracy of mortality prediction for patients undergoing CRRT for acute kidney injury compared with previous scoring models.
Highlights
Acute kidney injury (AKI) is an important issue because of its related morbidities and mortality rates [1, 2]
Once we developed the models using the training set, we calculated the F1 score, accuracy, and Area under the receiver operating characteristic curve (AUC) on the test set to measure the performance of each model
When the patients in the training set were categorized according to the intensive care unit (ICU) mortality, most of the baseline variables differed between the groups with and without death
Summary
Acute kidney injury (AKI) is an important issue because of its related morbidities and mortality rates [1, 2]. These prediction models did not focus on patients requiring CRRT for AKI Conventional scoring systems such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) have shown suitable performance for predicting the mortality of ICU patients [13, 14], but the predictive power appeared insufficient. Previous scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing continuous renal replacement therapy (CRRT) for severe acute kidney injury. The present study applies machine learning algorithms to improve prediction accuracy for this patient subset
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