Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
The George Institute for Global Health, The University of New South Wales, Sydney, Australia.
Int J Cardiovasc Imaging. 2020 Jun;36(6):983-991. doi: 10.1007/s10554-019-01730-6. Epub 2020 Apr 13.
The majority of prediction models for contrast-induced nephropathy (CIN) have moderate performance. Therefore, we aimed to develop a better pre-procedural prediction tool for CIN following contemporary percutaneous coronary intervention (PCI) or coronary angiography (CAG). A total of 3469 patients undergoing PCI/CAG between January 2010 and December 2013 were randomly divided into a training (n = 2428, 70%) and validation data-sets (n = 1041, 30%). Random forest full models were developed using 40 pre-procedural variables, of which 13 variables were selected for a reduced CIN model. CIN developed in 78 (3.21%) and 37 of patients (3.54%) in the training and validation datasets, respectively. In the validation dataset, the full and reduced models demonstrated improved discrimination over classic Mehran, ACEF CIN risk scores (AUC 0.842 and 0.825 over 0.762 and 0.701, respectively, all P < 0.05) and common estimated glomerular filtration rate. Compared to that for the Mehran risk score model, the full and reduced models had significantly improved fit based on the net reclassification improvement (all P < 0.001) and integrated discrimination improvement (P = 0.001, 0.028, respectively). Using the above models, 2462 (66.7%), 661, and 346 patients were categorized into low (< 1%), moderate (1% to 7%), and high (> 7%) risk groups, respectively. Our pre-procedural CIN risk prediction algorithm (http://cincalc.com) demonstrated good discriminative ability and was well calibrated when validated. Two-thirds of the patients were at low CIN risk, probably needing less peri-procedural preventive strategy; however, the discriminative ability of CIN risk requires further external validation. TRIAL REGISTRATION: ClinicalTrials.gov NCT01400295.
大多数对比剂肾病（CIN）预测模型的性能均为中等。因此，我们旨在为当代经皮冠状动脉介入治疗（PCI）或冠状动脉造影（CAG）后 CIN 建立一种更好的术前预测工具。2010 年 1 月至 2013 年 12 月间共有 3469 例接受 PCI/CAG 的患者被随机分为训练数据集（n = 2428，70%）和验证数据集（n = 1041，30%）。使用 40 个术前变量建立随机森林全模型，其中 13 个变量被选入简化的 CIN 模型。训练数据集和验证数据集中分别有 78（3.21%）和 37 例患者（3.54%）发生 CIN。在验证数据集中，全模型和简化模型在区分能力上优于经典的 Mehran、ACEF CIN 风险评分（AUC 分别为 0.842 和 0.825，而分别为 0.762 和 0.701，均 P < 0.05）和常见的估算肾小球滤过率。与 Mehran 风险评分模型相比，全模型和简化模型在净重新分类改善（均 P < 0.001）和综合鉴别改善（P = 0.001，0.028）方面具有显著改善的拟合度。使用以上模型，2462（66.7%）、661 和 346 例患者分别被归入低危（< 1%）、中危（1%至 7%）和高危（> 7%）风险组。我们的术前 CIN 风险预测算法（http://cincalc.com）在验证时具有良好的判别能力和良好的校准度。三分之二的患者 CIN 风险较低，可能需要较少的围手术期预防策略；然而，CIN 风险的判别能力还需要进一步的外部验证。临床试验注册：ClinicalTrials.gov NCT01400295。
