Mortality after acute renal failure: Models for prognostic stratification and risk adjustment
Mortality after acute renal failure: Models for prognostic stratification and risk adjustment
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DOI：
10.1038/sj.ki.5001579
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发表时间：
2006-09-01
影响因子：
19.6
通讯作者：
Mehta, R. L.
中科院分区：
文献类型：
--
作者：
Chertow, G. M.;Soroko, S. H.;Mehta, R. L.
为了在质量改进工作和前瞻性临床试验中充分调整疾病的共病和严重程度，必须准确识别急性肾功能衰竭(ARF)后死亡的预测因素。大多数危重患者ARF的流行病学研究都是基于单个中心，或使用离散结果(例如，住院死亡率)来检查单个时间点的暴露情况。我们分析了急性肾疾病改善护理项目(PICARD)的数据，该项目是一项针对ARF的多中心观察性研究。我们用三种不同的分析方法确定了重症监护病房中618名ARF患者死亡率的相关性。使用ARF诊断当天获得的信息的模型的预测能力极低。在会诊时，高龄、少尿、肝功能衰竭、呼吸衰竭、败血症和血小板减少与死亡率相关。在ARF开始透析时，高龄、肝功能衰竭、呼吸衰竭、败血症和血小板减少与死亡率相关；在Logistic回归模型中，较高的血尿素氮和较低的血肌酐也与死亡率相关。纳入时变协变量的模型通过减少错误分类、纳入肾外器官系统衰竭的日常变化以及在ARF过程中提供透析来增强预测能力。使用Picard多中心队列研究中危重患者ARF的数据，我们开发了几个预测模型，用于预后分层和风险调整。通过纳入随时间推移的暴露，ARF中预测模型的区分力可以显著提高。
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.