Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 1 Deaconess Rd. CC-470, Boston, MA 02215, USA.
Intensive Care Med. 2013 Mar;39(3):414-9. doi: 10.1007/s00134-012-2767-x. Epub 2012 Dec 7.
The observation periods and thresholds of serum creatinine and urine output defined in the Acute Kidney Injury Network (AKIN) criteria were not empirically derived. By continuously varying creatinine/urine output thresholds as well as the observation period, we sought to investigate the empirical relationships among creatinine, oliguria, in-hospital mortality, and receipt of renal replacement therapy (RRT).
Using a high-resolution database (Multiparameter Intelligent Monitoring in Intensive Care II), we extracted data from 17,227 critically ill patients with an in-hospital mortality rate of 10.9 %. The 14,526 patients had urine output measurements. Various combinations of creatinine/urine output thresholds and observation periods were investigated by building multivariate logistic regression models for in-hospital mortality and RRT predictions. For creatinine, both absolute and percentage increases were analyzed. To visualize the dependence of adjusted mortality and RRT rate on creatinine, the urine output, and the observation period, we generated contour plots.
Mortality risk was high when absolute creatinine increase was high regardless of the observation period, when percentage creatinine increase was high and the observation period was long, and when oliguria was sustained for a long period of time. Similar contour patterns emerged for RRT. The variability in predictive accuracy was small across different combinations of thresholds and observation periods.
The contour plots presented in this article complement the AKIN definition. A multi-center study should confirm the universal validity of the results presented in this article.
急性肾损伤网络(AKIN)标准中定义的血清肌酐和尿量的观察期和阈值不是通过经验推导得出的。通过不断改变肌酐/尿量的阈值和观察期,我们研究了肌酐、少尿、住院死亡率和接受肾脏替代治疗(RRT)之间的经验关系。
使用高分辨率数据库(多参数智能监护 II),我们从 17227 名住院死亡率为 10.9%的危重病患者中提取数据。其中 14526 名患者有尿量测量数据。通过建立多变量逻辑回归模型来预测住院死亡率和 RRT,研究了肌酐/尿量阈值和观察期的各种组合。对于肌酐,分析了绝对值和百分比的增加。为了可视化调整后的死亡率和 RRT 率与肌酐、尿量和观察期的依赖关系,我们生成了等高线图。
无论观察期如何,当绝对肌酐增加较高时,死亡率风险较高;当肌酐百分比增加较高且观察期较长时,以及当少尿持续较长时间时,死亡率风险也较高。RRT 也出现了类似的等高线模式。不同阈值和观察期组合的预测准确性变化较小。
本文介绍的等高线图补充了 AKIN 定义。一项多中心研究应证实本文结果的普遍有效性。