AKIpredictor, an online prognostic calculator for acute kidney injury in adult critically ill patients: development, validation and comparison to serum neutrophil gelatinase-associated lipocalin
AKIpredictor, an online prognostic calculator for acute kidney injury in adult critically ill patients: development, validation and comparison to serum neutrophil gelatinase-associated lipocalin
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DOI：
10.1007/s00134-017-4678-3
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发表时间：
2017-06-01
影响因子：
38.9
通讯作者：
Meyfroidt, Geert
中科院分区：
文献类型：
--
作者：
Flechet, Marine;Guiza, Fabian;Meyfroidt, Geert
目的：急性肾损伤(AKI)的早期诊断仍是一大挑战。我们开发并验证了成人ICU患者的AKI预测模型，并通过在线预后计算器提供了这些模型。我们比较了ICU入院时血清中性粒细胞明胶酶相关脂蛋白(NGAL)水平的预测效果。方法：分析大型多中心EPaNIC数据库。模型开发(n=2123)和验证(n=2367)基于现有的临床信息(1)在ICU入院前和(2)入院后，(3)在ICU入院后1天，(4)包括前24小时的额外监测数据。主要结果是比较模型和NGAL对任何AKI(AKI-123)和AKI 2或3期(AKI-23)发展的预测性能。结果：验证队列患病率AKI-123为29%，AKI-23为15%。ICU入院前AKI-123模型包括年龄、基线血肌酐、糖尿病和入院类型(内科/外科、急诊/计划)，AUC为0.75(95%CI为0.75~0.75)。AKI-23模型还包括身高和体重(AUC 0.77(95%CI 0.77-0.77))。在24小时后，AUCS的AUC数据可用性持续提高，AKI-123和AKI-23的AUC分别为0.82(95%CI 0.82-0.82)和0.84(95%CI 0.83-0.84)。NGAL与AUCS的差别较小，AKI-123和AKI-23的AUC分别为0.74(95%CI 0.74-0.74)和0.79(95%CI 0.79-0.79)。结论：仅使用常规收集的临床信息的模型可以早期预测AKI，并优于在ICU入院时测量的NGAL。AKI-123型可在http://akipredictor.com/.上购买
Purpose: Early diagnosis of acute kidney injury (AKI) remains a major challenge. We developed and validated AKI prediction models in adult ICU patients and made these models available via an online prognostic calculator. We compared predictive performance against serum neutrophil gelatinase-associated lipocalin (NGAL) levels at ICU admission.Methods: Analysis of the large multicenter EPaNIC database. Model development (n = 2123) and validation (n = 2367) were based on clinical information available (1) before and (2) upon ICU admission, (3) after 1 day in ICU and (4) including additional monitoring data from the first 24 h. The primary outcome was a comparison of the predictive performance between models and NGAL for the development of any AKI (AKI-123) and AKI stages 2 or 3 (AKI-23) during the first week of ICU stay.Results: Validation cohort prevalence was 29% for AKI-123 and 15% for AKI-23. The AKI-123 model before ICU admission included age, baseline serum creatinine, diabetes and type of admission (medical/surgical, emergency/planned) and had an AUC of 0.75 (95% CI 0.75-0.75). The AKI-23 model additionally included height and weight (AUC 0.77 (95% CI 0.77-0.77)). Performance consistently improved with progressive data availability to AUCs of 0.82 (95% CI 0.82-0.82) for AKI-123 and 0.84 (95% CI 0.83-0.84) for AKI-23 after 24 h. NGAL was less discriminant with AUCs of 0.74 (95% CI 0.74-0.74) for AKI-123 and 0.79 (95% CI 0.79-0.79) for AKI-23.Conclusions: AKI can be predicted early with models that only use routinely collected clinical information and outperform NGAL measured at ICU admission. The AKI-123 models are available at http://akipredictor.com/.