Supplementary Material for: One heartbeat away from a prediction model for cardiovascular diseases in patients with chronic kidney disease: a systematic review

Background: 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.
Methods: 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 were assessed with, respectively, the QUIPS and PROBAST tool.
Results: 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 (eGFR) (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.
Discussion: 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, evaluate the addition of new predictors, and assessment of the clinical impact.

背景：慢性肾脏病（CKD）患者罹患心血管疾病（CVD）的风险极高。结合临床与实验室特征的预测模型，常被用于评估个体的心血管疾病发病风险。然而，此类模型并非针对CKD患者专门开发，因此预测准确性或有所不足。本综述旨在系统梳理现有针对CKD患者的CVD预后研究，并评价其方法学质量。
方法：本研究检索了MEDLINE数据库中2012年至2021年间发表的、针对CKD患者的CVD预后相关研究文献。对纳入研究的患者特征、研究设计、结局测量方式及预测模型等维度进行对比分析。针对预后因素研究以及预测模型开发/验证研究，分别采用QUIPS工具与PROBAST工具评估其偏倚风险。
结果：本研究共纳入134项相关研究，其中123项探讨了1种或多种预测因素对现有模型或常见风险因素的增量价值，仅11项报告了预测模型的开发或验证工作。纳入研究的队列及研究特征（如样本量、事件发生率、结局测量定义等）存在显著异质性。最常用的预测因素依次为年龄（87%）、性别（75%）、糖尿病（70%）及估算肾小球滤过率（eGFR）（69%）。多数预后因素研究存在方法学缺陷，主要源于临床与方法学信息报告不全。在11项预测模型相关研究中，6项完成了模型开发与内部验证，4项对现有或自研模型进行了外部验证，仅1项预测模型研究展现出较低的偏倚风险与较高的适用性。
讨论：目前已发表大量CKD患者CVD预后相关研究，但由于研究结果呈现不完整、且缺乏预后模型的外部验证，其临床实用价值仍不明确。本综述可根据患者人群、结局指标及偏倚风险，帮助研究者选择最适宜的预后模型。未来应开展多中心协作，通过外部验证优化现有模型、评估新增预测因素的价值，并评价模型的临床应用价值。