Kidney International (2006) 70, 1120–1126.
Mortality after acute renal failure: Models for prognostic stratification and risk adjustment
G M Chertow1, S H Soroko2, E P Paganini3, K C Cho1, J Himmelfarb4, T A Ikizler5 and R L Mehta2 for the Program to Improve Care in Acute Renal Disease (PICARD)
Abstract
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. 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). We analyzed data from the Program to Improve Care in Acute Renal Disease (PICARD), a multi-center observational study of ARF. We determined correlates of mortality in 618 patients with ARF in intensive care units using three distinct analytic approaches. The predictive power of models using information obtained on the day of ARF diagnosis was extremely low. At the time of consultation, advanced age, oliguria, hepatic failure, respiratory failure, sepsis, and thrombocytopenia were associated with mortality. 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. 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. 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. By incorporating exposures over time, the discriminatory power of predictive models in ARF can be significantly improved.
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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。
急性肾衰（ARF）死亡率：预后分层和风险调整模型
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。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。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。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。运用这些资料得到的模型，对于ARF诊断的预测力非常低。研究同时发现，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。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。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。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。而随时间加入暴露因素，ARF预测模型的区分能力得到明显改善。
感谢“一碗阳春面”战友的翻译，你的Logo很可爱，是你的女儿吗？我也有一个非常可爱的女儿。下面是我的修改意见，希望批评指正。
comorbidity
共病。其英文解释：
The presence of co-existing or additional diseases with reference to an initial diagnosis or with reference to the index condition that is the subject of study. Comorbidity may affect the ability of affected individuals to function and also their survival; it may be used as a prognostic indicator for length of hospital stay, cost factors, and outcome or survival.
共病是指相对于原始诊断或相对于研究主题所指病情而言，共同存在或附加的疾病。共病可以影响受累个体的功能以及生存率，可用来预测住院时间、医疗费用和转归或生存率。
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.
为了在改善急性肾衰治疗质量和前瞻性临床试验中恰当地调整共存疾病和疾病严重程度等因素的影响，必须准确地判定急性肾衰死亡率的预测因子。
quality improvement如果单单使用词典检索，可能会取其“诊断改善”之意，但是联系到整个文章以及下文中“PICARD”(改善急性肾衰治疗质量计划)，则该词应该翻译为“治疗质量改善”。
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).
大多数关于危重患者伴急性肾衰的流行病学研究都以单中心为基础，或者使用离散的转归来分析单个时间点暴露所获得的资料。
The predictive power of models using information obtained on the day of ARF diagnosis was extremely low.
利用ARF诊断之日所获资料而建立的模型预测强度非常低。
该句的主语是“The predictive power of models”, Using……用来修饰models。
At the time of consultation
利用会诊时所获资料建立的模型
Upon initiation of dialysis for ARF
利用透析开始时资料建立的模型
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.
掺入随时间变化的共变量的模型通过减少分类错误、掺入肾外器官逐日变化和在急性肾衰期间透析的提供增强了其预测强度
By incorporating exposures over time, the discriminatory power of predictive models in ARF can be significantly improved.
通过掺入随时间变化的暴露资料，ARF预测模型的区分能力显著得到改善。
confusing wrote:
感谢“一碗阳春面”战友的翻译，你的Logo很可爱，是你的女儿吗？我也有一个非常可爱的女儿。下面是我的修改意见，希望批评指正。
呵呵，要是我以后有个这么漂亮的女儿就好了！
文献翻译的不错，
本人不是肾病学专业，
学习中！