The George Institute for Global Health, University of Sydney.
Sydney School of Public Health, The University of Sydney.
Nephrology (Carlton). 2016 Nov;21(11):930-937. doi: 10.1111/nep.12694.
To compare comorbidity recording and predictive power of comorbidities for mortality between a clinical renal registry and a state-based hospitalisation dataset.
All patients that started renal replacement therapy (dialysis or transplant - RRT) in New South Wales between 1/07/2001 and 31/7/2010 were identified using the Australia and New Zealand Dialysis and Transplant Registry (ANZDATA) and linked to the State Admitted Patient Data Collection (APDC) and the Death Registry. Comorbidities (diabetes mellitus, coronary artery disease (CAD), chronic lung disease, peripheral vascular disease and cerebrovascular disease) were identified at the start of RRT in both datasets and compared using kappa statistics (κ). Survival was calculated using cox proportional hazards models from the start of RRT to death date or end of study (31/07/2011). Four multivariable models were adjusted for age, gender and comorbidities to estimate the predictive power of the comorbidities as recorded in ANZDATA, APDC, either or both datasets RESULTS: We identified 6285 people (23,845 person-years follow-up). Diabetes recording had excellent agreement (94.5%, κ = 0.88), CAD had fair to good agreement (80. 6, κ = 0.56), with poor agreement between the two datasets for the other comorbidities. Deaths totalled 2594 (41.3%). Median follow up time was 3.3 years (IQR 1.7 to 5.4). All five comorbidities were powerful predictors of poor survival in all four models. All models had a similar predictive ability (Harrell's c = 0.71-0.72).
Variable agreement exists in comorbidity recording between the ANZDATA and APDC. The comorbidities have a similar predictive ability, irrespective of dataset of origin in an End Stage Kidney Disease (ESKD) population.
比较临床肾脏登记处和基于州的住院数据集在记录合并症和预测死亡率方面的差异。
通过澳大利亚和新西兰透析和移植登记处（ANZDATA）确定 2001 年 7 月 1 日至 2010 年 7 月 31 日期间在新南威尔士州开始接受肾脏替代治疗（透析或移植-RRT）的所有患者，并将其与州住院患者数据收集（APDC）和死亡登记处进行链接。在两个数据集的 RRT 开始时均识别合并症（糖尿病、冠状动脉疾病（CAD）、慢性肺部疾病、外周血管疾病和脑血管疾病），并使用 Kappa 统计量（κ）进行比较。从 RRT 开始到死亡日期或研究结束（2011 年 7 月 31 日），使用 Cox 比例风险模型计算生存率。通过四个多变量模型调整年龄、性别和合并症，以估计在 ANZDATA、APDC 或两个数据集记录的合并症的预测能力。
我们确定了 6285 人（23845 人年随访）。糖尿病记录的一致性极好（94.5%，κ=0.88），CAD 的一致性为中等至良好（80.6，κ=0.56），而两个数据集之间其他合并症的一致性较差。死亡总数为 2594 例（41.3%）。中位随访时间为 3.3 年（IQR 1.7 至 5.4）。在所有四个模型中，五种合并症都是不良生存的有力预测因素。所有模型的预测能力相似（Harrell 的 c=0.71-0.72）。
在 ANZDATA 和 APDC 之间，合并症记录存在可变的一致性。在终末期肾病（ESKD）人群中，无论数据集的来源如何，合并症都具有相似的预测能力。
