Predictors of renal replacement therapy in acute kidney injury.

Nephron Extra Pub Date : 2012-09-21 Print Date: 2012-01-01 DOI:10.1159/000342257

Michael J Koziolek, Rabi R Datta, Harry Mattes, Klaus Jung, Daniel Heise, Jan H Streich, Johannes Mühlhausen, Gerhard A Mueller, Hassan Dihazi

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引用次数: 3

Abstract

Backgrounds: Criteria that may guide early renal replacement therapy (RRT) initiation in patients with acute kidney injury (AKI) currently do not exist.

Methods: In 120 consecutive patients with AKI, clinical and laboratory data were analyzed on admittance. The prognostic power of those parameters which were significantly different between the two groups was analyzed by receiver operator characteristic curves and by leave-1-out cross validation.

Results: Six parameters (urine albumin, plasma creatinine, blood urea nitrogen, daily urine output, fluid balance and plasma sodium) were combined in a logistic regression model that estimates the probability that a particular patient will need RRT. Additionally, a second model without daily urine output was established. Both models yielded a higher accuracy (89 and 88% correct classification rate, respectively) than the best single parameter, cystatin C (correct classification rate 74%).

Conclusions: The combined models may help to better predict the necessity of RRT using clinical and routine laboratory data in patients with AKI.

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急性肾损伤肾替代治疗的预测因素。

背景:目前尚不存在指导急性肾损伤(AKI)患者早期肾替代治疗(RRT)启动的标准。方法:对120例连续住院的AKI患者的临床和实验室资料进行分析。采用受试者操作者特征曲线和留1交叉验证法分析两组间差异显著的预后指标。结果:6个参数(尿白蛋白、血浆肌酐、血尿素氮、日尿量、体液平衡和血浆钠)结合在一个逻辑回归模型中，估计特定患者需要RRT的概率。此外，建立了第二个无日尿量的模型。两种模型的准确率(正确分类率分别为89%和88%)均高于最佳单一参数胱抑素C(正确分类率为74%)。结论:结合AKI患者的临床和常规实验室数据，联合模型可能有助于更好地预测RRT的必要性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nephron Extra

自引率

0.00%

发文量

审稿时长

12 weeks

期刊介绍： An open-access subjournal to Nephron. ''Nephron EXTRA'' publishes additional high-quality articles that cannot be published in the main journal ''Nephron'' due to space limitations.