Duke University, 3475 Erwin Rd, Wallace Clinic Ste #204, Durham, NC 27705, USA.
BMC Med Inform Decis Mak. 2011 Jun 16;11:41. doi: 10.1186/1472-6947-11-41.
Chronic kidney disease (CKD) is the focus of recent national policy efforts; however, decision makers must account for multiple therapeutic options, comorbidities and complications. The objective of the Chronic Kidney Disease model is to provide guidance to decision makers. We describe this model and give an example of how it can inform clinical and policy decisions.
Monte Carlo simulation of CKD natural history and treatment. Health states include myocardial infarction, stroke with and without disability, congestive heart failure, CKD stages 1-5, bone disease, dialysis, transplant and death. Each cycle is 1 month. Projections account for race, age, gender, diabetes, proteinuria, hypertension, cardiac disease, and CKD stage. Treatment strategies include hypertension control, diabetes control, use of HMG-CoA reductase inhibitors, use of angiotensin converting enzyme inhibitors, nephrology specialty care, CKD screening, and a combination of these. The model architecture is flexible permitting updates as new data become available. The primary outcome is quality adjusted life years (QALYs). Secondary outcomes include health state events and CKD progression rate.
The model was validated for GFR change/year -3.0 ± 1.9 vs. -1.7 ± 3.4 (in the AASK trial), and annual myocardial infarction and mortality rates 3.6 ± 0.9% and 1.6 ± 0.5% vs. 4.4% and 1.6% in the Go study. To illustrate the model's utility we estimated lifetime impact of a hypothetical treatment for primary prevention of vascular disease. As vascular risk declined, QALY improved but risk of dialysis increased. At baseline, 20% and 60% reduction: QALYs = 17.6, 18.2, and 19.0 and dialysis = 7.7%, 8.1%, and 10.4%, respectively.
The CKD Model is a valid, general purpose model intended as a resource to inform clinical and policy decisions improving CKD care. Its value as a tool is illustrated in our example which projects a relationship between decreasing cardiac disease and increasing ESRD.
慢性肾脏病（CKD）是当前国家政策重点关注的领域；然而，决策者必须考虑多种治疗选择、合并症和并发症。慢性肾脏病模型的目标是为决策者提供指导。我们描述了这个模型，并给出了一个如何用它来为临床和政策决策提供信息的例子。
对 CKD 自然史和治疗进行蒙特卡罗模拟。健康状态包括心肌梗死、有和无残疾的中风、充血性心力衰竭、CKD 1-5 期、骨骼疾病、透析、移植和死亡。每个周期为 1 个月。预测考虑了种族、年龄、性别、糖尿病、蛋白尿、高血压、心脏病和 CKD 分期。治疗策略包括控制高血压、控制糖尿病、使用 HMG-CoA 还原酶抑制剂、使用血管紧张素转换酶抑制剂、肾脏病专科护理、CKD 筛查以及这些策略的组合。模型架构灵活，可根据新数据的可用性进行更新。主要结果是质量调整生命年（QALYs）。次要结果包括健康状态事件和 CKD 进展率。
该模型在 GFR 变化/年的验证中得到了验证-3.0±1.9 与 -1.7±3.4（在 AASK 试验中），以及每年心肌梗死和死亡率 3.6±0.9%和 1.6±0.5%与 4.4%和 1.6%在 Go 研究中。为了说明模型的实用性，我们估计了一种用于血管疾病一级预防的假设治疗的终生影响。随着血管风险的降低，QALY 得到了改善，但透析的风险增加了。在基线时，20%和 60%的降低：QALYs=17.6、18.2 和 19.0，透析=7.7%、8.1%和 10.4%。
慢性肾脏病模型是一种有效的、通用的模型，旨在为临床和政策决策提供信息，改善 CKD 护理。我们的例子说明了它作为一种工具的价值，该例子预测了心脏病的减少与 ESRD 的增加之间的关系。
