Clinical Laboratory, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.
Department of Clinical Laboratory Center, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing 100029, China.
Chin Med J (Engl). 2019 Apr 5;132(7):819-826. doi: 10.1097/CM9.0000000000000149.
The early identification of heart failure (HF) risk may favorably affect outcomes, and the combination of multiple biomarkers may provide a more comprehensive and valuable means for improving the risk of stratification. This study was conducted to assess the importance of individual cardiac biomarkers creatine kinase MB isoenzyme (CK-MB), B-type natriuretic peptide (BNP), galectin-3 (Gal-3) and soluble suppression of tumorigenicity-2 (sST2) for HF diagnosis, and the predictive performance of the combination of these four biomarkers was analyzed using random forest algorithms.
A total of 193 participants (80 patients with HF and 113 age- and gender-matched healthy controls) were included from June 2017 to December 2017. The correlation and regression analysis were conducted between cardiac biomarkers and echocardiographic parameters. The accuracy and importance of these predictor variables were assessed using random forest algorithms.
Patients with HF exhibited significantly higher levels of CK-MB, BNP, Gal-3, and sST2. BNP exhibited a good independent predictive capacity for HF (AUC 0.956). However, CK-MB, sST2, and Gal-3 exhibited a modest diagnostic performance for HF, with an AUC of 0.709, 0.711, and 0.777, respectively. BNP was the most important variable, with a remarkably higher mean decrease accuracy and Gini. Furthermore, there was a general increase in predictive performance using the multi-marker model, and the sensitivity, specificity was 91.5% and 96.7%, respectively.
The random forest algorithm provides a robust method to assess the accuracy and importance of predictor variables. The combination of CK-MB, BNP, Gal-3, and sST2 achieves improvement in prediction accuracy for HF.
早期识别心力衰竭（HF）风险可能会对预后产生有利影响，而多种生物标志物的联合应用可能提供更全面、更有价值的分层风险评估手段。本研究旨在评估肌酸激酶同工酶（CK-MB）、B 型利钠肽（BNP）、半乳糖凝集素-3（Gal-3）和可溶性肿瘤抑制物 2（sST2）等单个心 脏生物标志物对 HF 诊断的重要性，并采用随机森林算法分析这 4 种生物标志物联合应用的预测效能。
2017 年 6 月至 12 月共纳入 193 例患者（HF 患者 80 例，年龄和性别相匹配的健康对照者 113 例）。采用相关性和回归分析对心脏生物标志物与超声心动图参数之间的关系进行分析。采用随机森林算法评估这些预测变量的准确性和重要性。
HF 患者的 CK-MB、BNP、Gal-3 和 sST2 水平显著升高。BNP 对 HF 具有良好的独立预测能力（AUC 为 0.956）。然而，CK-MB、sST2 和 Gal-3 对 HF 的诊断效能较低，AUC 分别为 0.709、0.711 和 0.777。BNP 是最重要的变量，其平均降低精度和基尼系数均显著较高。此外，多标志物模型的预测性能普遍提高，敏感性和特异性分别为 91.5%和 96.7%。
随机森林算法为评估预测变量的准确性和重要性提供了一种稳健的方法。CK-MB、BNP、Gal-3 和 sST2 的联合应用可提高 HF 预测的准确性。
