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% 31.40%, respectively, <0.001) and 7-day (3.96% 13.93%, respectively, <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% 13.60%, respectively, <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.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10391576PMC
http://dx.doi.org/10.1016/j.jointm.2023.02.005DOI Listing

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