Tangri Navdeep, Ferguson Thomas W, Bamforth Ryan J, Sood Manish M, Ravani Pietro, Clarke Alix, Bosi Alessandro, Carrero Juan J
Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada.
Department of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada.
CJC Open. 2025 Feb 26;7(5):686-694. doi: 10.1016/j.cjco.2025.02.016. eCollection 2025 May.
Accurate cardiovascular (CV) risk prediction tools may heighten awareness and monitoring, improve the use of evidence-based therapies and help inform shared decision making for patients with chronic kidney disease (CKD). The purpose of this study was to develop and externally validate a risk prediction model for incident and recurrent CV events across all stages of CKD using commonly available demographics and laboratory data.
A series of models were developed using administrative and laboratory data (n=36,317) from Manitoba, Canada, between April 1, 2006, and December 31, 2018, with external validation in health system's data from Alberta, Canada (n=95,191), and Stockholm, Sweden (n=83,000). Adults with incident CKD stages G1-G4 were followed for the occurrence of major adverse cardiovascular events (MACE) (myocardial infraction, stroke, and CV death), and MACE including hospitalization for heart failure (MACE+). Discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUC), Brier scores, and plots of observed vs predicted risk, and the models were compared to an existing model from the Chronic Renal Insufficiency Cohort (CRIC).
In the Alberta cohort, the AUCs for predicting MACE and MACE+ were 0.77 (0.77-0.77) and 0.80 (0.79-0.80), respectively. In the Stockholm cohort, the model achieved an AUC of 0.87 (0.86-0.87) for predicting MACE and 0.88 (0.88-0.88) for MACE+. Overall performance was improved relative to CRIC.
A model including commonly available administrative data and laboratory results can predict the risk of MACE and MACE+ outcomes among individuals with CKD.
准确的心血管（CV）风险预测工具可能会提高人们的认识并加强监测，改善基于证据的治疗方法的使用，并有助于为慢性肾脏病（CKD）患者提供共同决策依据。本研究的目的是使用常见的人口统计学和实验室数据，开发并外部验证一个针对CKD各阶段发生和复发CV事件的风险预测模型。
利用加拿大曼尼托巴省2006年4月1日至2018年12月31日期间的行政和实验室数据（n = 36,317）开发了一系列模型，并在加拿大艾伯塔省（n = 95,191）和瑞典斯德哥尔摩（n = 83,000）的卫生系统数据中进行了外部验证。对新发生CKD 1-4期的成年人随访主要不良心血管事件（MACE）（心肌梗死、中风和CV死亡）以及包括因心力衰竭住院的MACE（MACE+）的发生情况。使用受试者操作特征曲线（AUC）下的面积、Brier评分以及观察到的风险与预测风险的图来评估区分度和校准度，并将这些模型与慢性肾功能不全队列（CRIC）的现有模型进行比较。
在艾伯塔省队列中，预测MACE和MACE+的AUC分别为0.77（0.77 - 0.77）和0.80（0.79 - 0.80）。在斯德哥尔摩队列中，该模型预测MACE的AUC为0.87（0.86 - 0.87），预测MACE+的AUC为0.88（0.88 - 0.88）。总体表现相对于CRIC有所改善。
一个包含常见行政数据和实验室结果的模型可以预测CKD个体发生MACE和MACE+结局的风险。
