急性肾衰（ARF）死亡率：预后分层和风险调整模型

htchb

Mortality after acute renal failure: Models for prognostic stratification and risk adjustment
急性肾衰（ARF）死亡率：预后分层和风险调整模型

Abstract
摘要

To adjust adequately for comorbidity and severity of illness in quality improvement efforts and prospective clinical trials, predictors of death after acute renal failure (ARF) must be accurately identified.
为了在ARF诊断改善研究和预期的临床试验中， 对ARF的死亡率和严重程度进行适当调整，ARF死亡预测因子必须被正确确定。

Most epidemiological studies of ARF in the critically ill have been based at single centers, or have examined exposures at single time points using discrete outcomes (e.g., in-hospital mortality).
危重病ARF大部分流行病学研究都建立在单中心研究基础上，或者利用不连续资料结果（如患者住院死亡率）在单个时间点暴露获得。

We analyzed data from the Program to Improve Care in Acute Renal Disease (PICARD), a multi-center observational study of ARF.
我们分析了来自改善急性肾脏疾病照护计划（PICARD）——一个ARF多中心观察研究的数据。

We determined correlates of mortality in 618 patients with ARF in intensive care units using three distinct analytic approaches.
对ICU中618例ARF患者，采用3种不同的分析方法，得出死亡率中的相互关系。

The predictive power of models using information obtained on the day of ARF diagnosis was extremely low.
运用这些资料得到的模型，对于ARF诊断的预测力非常低。

At the time of consultation, advanced age, oliguria, hepatic failure, respiratory failure, sepsis, and thrombocytopenia were associated with mortality.
研究的同时发现，老年，少尿，肝衰竭，呼吸衰竭，败血症和血小板减少症与死亡率相关。

Upon initiation of dialysis for ARF, advanced age, hepatic failure, respiratory failure, sepsis, and thrombocytopenia were associated with mortality; higher blood urea nitrogen and lower serum creatinine were also associated with mortality in logistic regression models.
ARF开始透析前，老年，少尿，肝衰竭，呼吸衰竭，败血症和血小板减少症与死亡率相关；而logistic回归模型显示，较高的血尿素氮和较低的血肌酐也和死亡率相关。

Models incorporating time-varying covariates enhanced predictive power by reducing misclassification and incorporating day-to-day changes in extra-renal organ system failure and the provision of dialysis during the course of ARF.
通过减少错误分类，加入肾外器官衰竭逐日变化和ARF期间透析的提供，这些时间变化因素的加入增加了模型预测力。

Using data from the PICARD multi-center cohort study of ARF in critically ill patients, we developed several predictive models for prognostic stratification and risk-adjustment.
利用来自危重患者中PICARD ARF多中心队列研究的资料，我们得出了一些预后分层和风险调整预测模型。

By incorporating exposures over time, the discriminatory power of predictive models in ARF can be significantly improved.
随时间加入暴露因素，ARF预测模型的区分能力能明显改善。