Influence of the trajectory of the urine output for 24\xa0h on the occurrence of AKI in patients with sepsis in intensive care unit

Luming Zhang,Didi Han,Shaojin Li,Tao Huang,Fengshuo Xu,Jun Lyu,Haiyan Yin

doi:10.1186/s12967-021-03190-w

Abstract

BackgroundSepsis-associated acute kidney injury (S-AKI) is a common and life-threatening complication in hospitalized and critically ill patients. This condition is an independent cause of death. This study was performed to investigate the correlation between the trajectory of urine output within 24 h and S-AKI.MethodsPatients with sepsis were studied retrospectively based on the Medical Information Mart for Intensive Care IV. Latent growth mixture modeling was used to classify the trajectory of urine output changes within 24 h of sepsis diagnosis. The outcome of this study is AKI that occurs 24 h after sepsis. Cox proportional hazard model, Fine–Gray subdistribution proportional hazard model, and doubly robust estimation method were used to explore the risk of AKI in patients with different trajectory classes.ResultsA total of 9869 sepsis patients were included in this study, and their 24-h urine output trajectories were divided into five classes. The Cox proportional hazard model showed that compared with class 1, the HR (95% CI) values for classes 3, 4, and 5 were 1.460 (1.137–1.875), 1.532 (1.197–1.961), and 2.232 (1.795–2.774), respectively. Competing risk model and doubly robust estimation methods reached similar results.ConclusionsThe trajectory of urine output within 24 h of sepsis patients has a certain impact on the occurrence of AKI. Therefore, in the early treatment of sepsis, close attention should be paid to changes in the patient's urine output to prevent the occurrence of S-AKI.

Highlights

Sepsis 3.0 defines sepsis as a life-threatening organ dysfunction caused by the host’s dysfunctional response to infection
Zhang [6] mentioned in a narrative review that neutrophil gelatinase-associated lipocalin (NGAL), cystatin C, b2-microglobulin and microalbuminuria are potential biomarkers that can predict the occurrence and development of Sepsis-associated acute kidney injury (S-AKI)
These biomarkers still need a lot of prospective studies and trials to prove, and there is still a gap in clinical application and promotion [11].Most of the existing AKI diagnoses are based on the Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guidelines [12], in which diagnosis and staging are based on changes in the urine output and creatinine

Summary

Introduction

Sepsis 3.0 defines sepsis as a life-threatening organ dysfunction caused by the host’s dysfunctional response to infection. Another study has used deep learning methods to continuously predict the occurrence of severe AKI based on changes in the urine output in critically ill patients [14]. The effect of the trajectory of the urine output on the occurrence of AKI in patients with sepsis has not been investigated. Sepsis-associated acute kidney injury (S-AKI) is a common and life-threatening complication in hospitalized and critically ill patients. This condition is an independent cause of death. This study was performed to investigate the correlation between the trajectory of urine output within 24 h and S-AKI

Methods

Results

Conclusion