Red blood cell distribution width improves the prediction of 28-day mortality for patients with sepsis-induced acute kidney injury: A retrospective analysis from MIMIC-IV database using propensity score matching

Honghao Lai; Guosheng Wu; Yu Zhong; Guangping Chen; Wei Zhang; Shengjun Shi; Zhaofan Xia

doi:10.1016/j.jointm.2023.02.005

Red blood cell distribution width improves the prediction of 28-day mortality for patients with sepsis-induced acute kidney injury: A retrospective analysis from MIMIC-IV database using propensity score matching

J Intensive Med. 2023 Apr 20;3(3):275-282. doi: 10.1016/j.jointm.2023.02.005. eCollection 2023 Jul 31.

Authors

Honghao Lai^{1

2

3}, Guosheng Wu², Yu Zhong⁴, Guangping Chen¹, Wei Zhang², Shengjun Shi¹, Zhaofan Xia^{1

2}

Affiliations

¹ Department of Burns, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, Guangdong, China.
² Department of Burns, Changhai Hospital, Naval Medical University, Shanghai 200433, China.
³ Research Institute, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510060, China.
⁴ Department of Burns, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China.

Abstract

Background: The predictive value of red blood cell distribution width (RDW) for mortality in patients with sepsis-induced acute kidney injury (SI-AKI) remains unclear. The present study aimed to investigate the potential association between RDW at admission and outcomes in patients with SI-AKI.

Methods: The Medical Information Mart for Intensive Care (MIMIC)-IV (version 2.0) database, released in June of 2022, provides medical data of SI-AKI patients to conduct our related research. Based on propensity score matching (PSM) method, the main risk factors associated with mortality in SI-AKI were evaluated using Cox proportional hazards regression analysis to construct a predictive nomogram. The concordance index (C-index) and decision curve analysis were used to validate the predictive ability and clinical utility of this model. Patients with SI-AKI were classified into the high- and low-RDW groups according to the best cut-off value obtained by calculating the maximum value of the Youden index.

Results: A total of 7574 patients with SI-AKI were identified according to the filter criteria. Compared with the low-RDW group, the high-RDW group had higher 28-day (9.49% vs. 31.40%, respectively, P <0.001) and 7-day (3.96% vs. 13.93%, respectively, P <0.001) mortality rates. Patients in the high-RDW group were more prone to AKI progression than those in the low-RDW group (20.80% vs. 13.60%, respectively, P <0.001). Based on matched patients, we developed a nomogram model that included age, white blood cells, RDW, combined hypertension and presence of a malignant tumor, treatment with vasopressor, dialysis, and invasive ventilation, sequential organ failure assessment, and AKI stages. The C-index for predicting the probability of 28-day survival was 0.799. Decision curve analysis revealed that the model with RDW offered greater net benefit than that without RDW.

Conclusion: The present findings demonstrated the importance of RDW, which improved the predictive ability of the nomogram model for the probability of survival in patients with SI-AKI.

Keywords: Medical Information Mart for Intensive Care IV (MIMIC-IV); Mortality; Propensity score matching; Red blood cell distribution width; Sepsis-induced acute kidney injury.