Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands,
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Cardiorenal Med. 2023;13(1):109-142. doi: 10.1159/000529791. Epub 2023 Feb 20.
Patients with chronic kidney disease (CKD) have a high risk of cardiovascular disease (CVD). Prediction models, combining clinical and laboratory characteristics, are commonly used to estimate an individual's CVD risk. However, these models are not specifically developed for patients with CKD and may therefore be less accurate. In this review, we aim to give an overview of CVD prognostic studies available, and their methodological quality, specifically for patients with CKD.
MEDLINE was searched for papers reporting CVD prognostic studies in patients with CKD published between 2012 and 2021. Characteristics regarding patients, study design, outcome measurement, and prediction models were compared between included studies. The risk of bias of studies reporting on prognostic factors or the development/validation of a prediction model was assessed with, respectively, the QUIPS and PROBAST tool.
In total, 134 studies were included, of which 123 studies tested the incremental value of one or more predictors to existing models or common risk factors, while only 11 studies reported on the development or validation of a prediction model. Substantial heterogeneity in cohort and study characteristics, such as sample size, event rate, and definition of outcome measurements, was observed across studies. The most common predictors were age (87%), sex (75%), diabetes (70%), and estimated glomerular filtration rate (69%). Most of the studies on prognostic factors have methodological shortcomings, mostly due to a lack of reporting on clinical and methodological information. Of the 11 studies on prediction models, six developed and internally validated a model and four externally validated existing or developed models. Only one study on prognostic models showed a low risk of bias and high applicability.
A large quantity of prognostic studies has been published, yet their usefulness remains unclear due to incomplete presentation, and lack of external validation of prognostic models. Our review can be used to select the most appropriate prognostic model depending on the patient population, outcome, and risk of bias. Future collaborative efforts should aim at improving existing models by externally validating them, evaluating the addition of new predictors, and assessment of the clinical impact.
We have registered the protocol of our systematic review on PROSPERO (CRD42021228043).
患有慢性肾脏病 (CKD) 的患者患心血管疾病 (CVD) 的风险较高。预测模型，结合临床和实验室特征，通常用于估计个体的 CVD 风险。然而，这些模型并非专门针对 CKD 患者开发，因此可能不太准确。在本次综述中，我们旨在概述可用于 CKD 患者的 CVD 预后研究，并评估其方法学质量。
在 2012 年至 2021 年间，我们在 MEDLINE 上搜索了报道 CKD 患者 CVD 预后研究的论文。比较了纳入研究中患者、研究设计、结局测量和预测模型的特征。使用 QUIPS 和 PROBAST 工具分别评估了报告预后因素或开发/验证预测模型的研究的偏倚风险。
共纳入 134 项研究，其中 123 项研究测试了一个或多个预测因素对现有模型或常见危险因素的增量价值，而仅有 11 项研究报告了预测模型的开发或验证。研究之间存在明显的队列和研究特征的异质性，例如样本量、事件发生率和结局测量的定义。最常见的预测因素是年龄 (87%)、性别 (75%)、糖尿病 (70%) 和估算肾小球滤过率 (69%)。大多数预后因素研究都存在方法学上的缺陷，主要是因为缺乏对临床和方法学信息的报告。在预测模型的 11 项研究中，有 6 项开发并内部验证了一个模型，有 4 项外部验证了现有的或开发的模型。只有一项关于预后模型的研究显示出低偏倚风险和高适用性。
已经发表了大量的预后研究，但由于缺乏完整的报告以及对预后模型缺乏外部验证，其有用性仍不清楚。我们的综述可用于根据患者人群、结局和偏倚风险选择最合适的预后模型。未来的合作努力应旨在通过外部验证来改进现有模型，评估新预测因素的添加，并评估其临床影响。
我们已在 PROSPERO 上注册了我们的系统综述方案 (CRD42021228043)。
