A new scoring model for the prediction of mortality in patients with acute kidney injury

• Main Authors: Min Luo, Yuan Yang, Jun Xu, Wei Cheng, Xu-Wei Li, Mi-Mi Tang, Hong Liu, Fu-You Liu, Shao-Bin Duan
• Format: Article
• Language: English
• Published: Nature Publishing Group 2017-08-01
• Series: Scientific Reports
• Online Access: https://doi.org/10.1038/s41598-017-08440-w

Abstract Currently, little information is available to stratify the risks and predict acute kidney injury (AKI)-associated death. In this present cross-sectional study, a novel scoring model was established to predict the probability of death within 90 days in patients with AKI diagnosis. For establishment of predictive scoring model, clinical data of 1169 hospitalized patients with AKI were retrospectively collected, and 731 patients of them as the first group were analyzed by the method of multivariate logistic regression analysis to create a scoring model and further predict patient death. Then 438 patients of them as the second group were used for validating this prediction model according to the established scoring method. Our results showed that Patient’s age, AKI types, respiratory failure, central nervous system failure, hypotension, and acute tubular necrosis-individual severity index (ATN-ISI) score are independent risk factors for predicting the death of AKI patients in the created scoring model. Moreover, our scoring model could accurately predict cumulative AKI and mortality rate in the second group. In conclusion, this study identified the risk factors of 90-day mortality for hospitalized AKI patients and established a scoring model for predicting 90-day prognosis, which could help to interfere in advance for improving the quality of life and reduce mortality rate of AKI patients.