Predicting mortality in incident dialysis patients: an analysis of the United Kingdom Renal Registry.
Predicting mortality in incident dialysis patients: an analysis of the United Kingdom Renal Registry.
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DOI：
10.1053/j.ajkd.2010.12.023
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发表时间：
2011-06
期刊：
影响因子：
--
通讯作者：
Tangri N
中科院分区：
文献类型：
--
作者：
Wagner M;Ansell D;Kent DM;Griffith JL;Naimark D;Wanner C;Tangri N
透析患者的死亡风险仍然很高，但患者之间差异很大。目前临床实践中没有广泛使用的预测工具。我们的目的是预测事件透析患者的长期死亡率容易获得的变量。英国前瞻性全国多中心队列研究（英国肾脏登记研究）;使用考克斯比例风险开发模型。在2002年至2004年期间开始血液透析或腹膜透析的患者，在透析治疗中存活至少3个月，随访3年。分析仅限于可获得共病疾病和实验室测量信息的受试者（n=5447）。通过随机选择将数据集分为用于模型开发（n=3631，训练）和验证（n=1816）的数据集。患者基本特征、合并症和实验室变量。对肾移植、肾功能恢复和失访的全因死亡率进行删失。在训练数据集中，1078名患者（29.7%）在观察期内死亡。训练数据集的最终模型包括患者特征（年龄、人种、原发性肾脏疾病、治疗方式）、合并症（糖尿病、心血管疾病史、吸烟）和实验室变量（血红蛋白、血清白蛋白、肌酐、钙），并达到0.75的C-统计量（95%CI，0.73-0.77），并能准确区分低（6%）、中（19%）、高（33%）和极高（59%）死亡风险的患者。将该模型进一步应用于验证数据集，并实现了0.73的C-统计量（95% CI，0.71-0.76）。缺失的合并症数据数量和缺乏外部验证数据集。患者的基本特征、合并症和实验室变量可以足够准确地预测透析患者的3年死亡率。根据死亡风险识别患者亚组可以指导未来的研究，并随后针对个体患者做出治疗决策。
The risk of death in dialysis patients remains high, but varies significantly among patients. No prediction tool is widely used in current clinical practice. We aimed to predict long-term mortality in incident dialysis patients with easily obtainable variables. Prospective nationwide multicenter cohort study in the United Kingdom (UK Renal Registry); Models were developed using Cox proportional hazards. Patients initiating hemodialysis or peritoneal dialysis between 2002 and 2004, who survived at least three months on dialysis treatment, were followed for three years. Analyses were restricted to subjects in whom information on comorbid conditions and laboratory measurements were available (n=5447). The dataset was divided into datasets for model development (n=3631, training) and validation (n=1816) by random selection. Basic patient characteristics, comorbidity and laboratory variables. All cause mortality censored for kidney transplant, recovery of kidney function, and loss to follow-up. In the training dataset, 1078 patients (29.7%) died within the observation period. The final model of the training dataset included patient characteristics (age, race, primary kidney disease, treatment modality), comorbidities (diabetes, history of cardiovascular disease, smoking) and laboratory variables (hemoglobin, serum albumin, creatinine, calcium) and reached a C-statistic of 0.75 (95% CI, 0.73–0.77) and could accurately discriminate between patients with low (6%), intermediate (19%), high (33%) and very high (59%) mortality risk. The model was further applied to the validation dataset and achieved a C-statistic of 0.73 (95% CI, 0.71–0.76). Number of missing comorbidity data and lack of an external validation dataset. Basic patient characteristics, comorbidity and laboratory variables can predict three-year mortality in incident dialysis patients with sufficient accuracy. Identification of subgroups of patients according to mortality risk can guide future research and subsequently target treatment decisions in individual patients.
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影响因子：
19.6
作者：
Drechsler, Christiane;Krane, Vera;Wanner, Christoph
通讯作者：
Wanner, Christoph
影响因子：
13.2
作者：
Goodkin, DA;Young, EW;Levin, NW
通讯作者：
Levin, NW
影响因子：
3.6
作者：
Steyerberg, EW;Eijkemans, MJC;Habbema, JDF
通讯作者：
Habbema, JDF
DOI：
10.2215/cjn.00640109
发表时间：
2009-11-01
影响因子：
9.8
作者：
Miskulin, Dana;Bragg-Gresham, Jennifer;Port, Friedrich K.
通讯作者：
Port, Friedrich K.
影响因子：
2.5
作者：
Shah, Dibya S.;Polkinghorne, Kevan R.;Kerr, Peter G.
通讯作者：
Kerr, Peter G.