A Novel Risk Prediction Model for Severe Acute Kidney Injury in Intensive Care Unit Patients Receiving Fluid Resuscitation

被引:1
作者
Feng, Yunlin
Li, Qiang
Finfer, Simon
Myburgh, John
Bellomo, Rinaldo
Perkovic, Vlado
Jardine, Meg
Wang, Amanda Y.
Gallagher, Martin
机构
[1] Renal Division, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu
[2] The George Institute for Global Health, University of New South Wales (UNSW), Sydney, NSW
[3] Department of Critical Care, University of Melbourne, Melbourne, VIC
[4] NHMRC Clinical Trials Centre, University of Sydney, Sydney, NSW
[5] Concord Clinical School, University of Sydney, Sydney, NSW
[6] South Western Sydney Clinical School, University of New South Wales (UNSW), Sydney, NSW
来源
FRONTIERS IN CARDIOVASCULAR MEDICINE | 2022年 / 9卷
基金
英国医学研究理事会;
关键词
acute kidney injury; risk prediction; model; ICU; fluids resuscitation; ACUTE-RENAL-FAILURE; CRITICALLY-ILL PATIENTS; HYDROXYETHYL STARCH; DEFINITIONS; MULTICENTER; OUTCOMES; SALINE; SCORE;
D O I
10.3389/fcvm.2022.840611
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
BackgroundTo develop a risk prediction model for the occurrence of severe acute kidney injury (AKI) in intensive care unit (ICU) patients receiving fluid resuscitation. MethodsWe conducted a secondary analysis of the Crystalloid vs. Hydroxyethyl Starch Trial (CHEST) trial, a blinded randomized controlled trial that enrolled ICU patients who received intravenous fluid resuscitation. The primary outcome was the first event in a composite outcome of doubling of serum creatinine and/or treatment with renal replacement treatment (RRT) within 28 days of randomization. The final model developed using multivariable logistic regression with backwards elimination was validated internally and then translated into a predictive equation. ResultsSix thousand seven hundred twenty-seven ICU participants were studied, among whom 745 developed the study outcome. The final model having six variables, including admission diagnosis of sepsis, illness severity score, mechanical ventilation, tachycardia, baseline estimated glomerular filtration rate and emergency admission. The model had good discrimination (c-statistic = 0.72, 95% confidence interval 0.697-0.736) and calibration (Hosmer-Lemeshow test, chi(2) = 14.4, p = 0.07) for the composite outcome, with a c-statistic after internal bootstrapping validation of 0.72, which revealed a low degree of over-fitting. The positive predictive value and negative predictive value were 58.8 and 89.1%, respectively. The decision curve analysis indicates a net benefit in prediction of severe AKI using the model across a range of threshold probabilities between 5 and 35%. ConclusionsOur model, using readily available clinical variables to identify ICU patients at high risk of severe AKI achieved good predictive performance in a clinically relevant population.
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页数:10
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