Development of mortality prediction model in the elderly hospitalized AKI patients

Author:

Peng Jing-Cheng,Wu Ting,Wu Xi,Yan Ping,Kang Yi-Xin,Liu Yu,Zhang Ning-Ya,Liu Qian,Wang Hong-Shen,Deng Ying-Hao,Wang Mei,Luo Xiao-Qin,Duan Shao-Bin

Abstract

AbstractAcute kidney injury (AKI) correlates with increased health-care costs and poor outcomes in older adults. However, there is no good scoring system to predict mortality within 30-day, 1-year after AKI in older adults. We performed a retrospective analysis screening data of 53,944 hospitalized elderly patients (age > 65 years) from multi-centers in China. 944 patients with AKI (acute kidney disease) were included and followed up for 1 year. Multivariable regression analysis was used for developing scoring models in the test group (a randomly 70% of all the patients). The established models have been verified in the validation group (a randomly 30% of all the patients). Model 1 that consisted of the risk factors for death within 30 days after AKI had accurate discrimination (The area under the receiver operating characteristic curves, AUROC: 0.90 (95% CI 0.875–0.932)) in the test group, and performed well in the validation groups (AUROC: 0.907 (95% CI 0.865–0.949)). The scoring formula of all-cause death within 1 year (model 2) is a seven-variable model including AKI type, solid tumor, renal replacement therapy, acute myocardial infarction, mechanical ventilation, the number of organ failures, and proteinuria. The area under the receiver operating characteristic (AUROC) curves of model 2 was > 0.80 both in the test and validation groups. Our newly established risk models can well predict the risk of all-cause death in older hospitalized AKI patients within 30 days or 1 year.

Funder

National Natural Science Foundation of China

Development and Reform Commission of Hunan Province

Scientific Foundation of Hunan Province, China

Clinical Medical Technology Innovation Guide Project of Hunan Province

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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