Development and validation of a risk stratification model for predicting the mortality of acute kidney injury in critical care patients

Background: This study aimed to develop and validate a model for mortality risk stratification of intensive care unit (ICU) patients with acute kidney injury (AKI) using the machine learning technique. Methods: Eligible data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Calibration, discrimination, and risk classification for mortality prediction were evaluated using conventional scoring systems and the new algorithm. A 10-fold cross-validation was performed. The predictive models were externally validated using the eICU database and also patients treated at the Second People's Hospital of Shenzhen between January 2015 to October 2018. Results: For the new model, the areas under the receiver operating characteristic curves (AUROCs) for mortality during hospitalization and at 28 and 90 days after discharge were 0.91, 0.87, and 0.87, respectively, which were higher than for the Simplified Acute Physiology Score (SAPS II) and Sequential Organ Failure Assessment (SOFA). For external validation, the AUROC was 0.82 for in-hospital mortality, higher than SOFA, SAPS II, and Acute Physiology and Chronic Health Evaluation (APACHE) IV in the eICU database, but for the 28- and 90-day mortality, the new model had AUROCs (0.79 and 0.80, respectively) similar to that of SAPS II in the SZ2 database. The reclassification indexes were superior for the new model compared with the conventional scoring systems. Conclusions: The new risk stratification model shows high performance in predicting mortality in ICU patients with AKI.

Automated summary

This study developed and validated a model for mortality risk stratification of ICU patients with AKI using machine learning techniques. The new model showed higher AUROCs for mortality prediction compared to conventional scoring systems, and its accuracy was further confirmed through external validation.