心脏计分卡：一种用于预测一系列心血管疾病的诊断多变量指标检测系统。
Cardiac ScoreCard: A Diagnostic Multivariate Index Assay System for Predicting a Spectrum of Cardiovascular Disease.
作者信息
McRae Michael P, Bozkurt Biykem, Ballantyne Christie M, Sanchez Ximena, Christodoulides Nicolaos, Simmons Glennon, Nambi Vijay, Misra Arunima, Miller Craig S, Ebersole Jeffrey L, Campbell Charles, McDevitt John T
机构信息
Department of Bioengineering, Rice University, Houston, TX, USA.
Michael E. DeBakey VA Medical Center, Houston, TX, USA.
出版信息
Expert Syst Appl. 2016 Jul 15;54:136-147. doi: 10.1016/j.eswa.2016.01.029. Epub 2016 Jan 25.
Clinical decision support systems (CDSSs) have the potential to save lives and reduce unnecessary costs through early detection and frequent monitoring of both traditional risk factors and novel biomarkers for cardiovascular disease (CVD). However, the widespread adoption of CDSSs for the identification of heart diseases has been limited, likely due to the poor interpretability of clinically relevant results and the lack of seamless integration between measurements and disease predictions. In this paper we present the Cardiac ScoreCard-a multivariate index assay system with the potential to assist in the diagnosis and prognosis of a spectrum of CVD. The Cardiac ScoreCard system is based on lasso logistic regression techniques which utilize both patient demographics and novel biomarker data for the prediction of heart failure (HF) and cardiac wellness. Lasso logistic regression models were trained on a merged clinical dataset comprising 579 patients with 6 traditional risk factors and 14 biomarker measurements. The prediction performance of the Cardiac ScoreCard was assessed with 5-fold cross-validation and compared with reference methods. The experimental results reveal that the ScoreCard models improved performance in discriminating disease versus non-case (AUC = 0.8403 and 0.9412 for cardiac wellness and HF, respectively), and the models exhibit good calibration. Clinical insights to the prediction of HF and cardiac wellness are provided in the form of logistic regression coefficients which suggest that augmenting the traditional risk factors with a multimarker panel spanning a diverse cardiovascular pathophysiology provides improved performance over reference methods. Additionally, a framework is provided for seamless integration with biomarker measurements from point-of-care medical microdevices, and a lasso-based feature selection process is described for the down-selection of biomarkers in multimarker panels.
临床决策支持系统（CDSSs）有潜力通过对心血管疾病（CVD）的传统风险因素和新型生物标志物进行早期检测和频繁监测来挽救生命并降低不必要的成本。然而，CDSSs在心脏病识别方面的广泛应用受到限制，这可能是由于临床相关结果的可解释性较差以及测量与疾病预测之间缺乏无缝集成。在本文中，我们介绍了心脏计分卡——一种多变量指标检测系统，有潜力协助诊断和预测一系列心血管疾病。心脏计分卡系统基于套索逻辑回归技术，该技术利用患者人口统计学数据和新型生物标志物数据来预测心力衰竭（HF）和心脏健康状况。套索逻辑回归模型是在一个合并的临床数据集上进行训练的，该数据集包含579名患者的6种传统风险因素和14种生物标志物测量值。通过5折交叉验证评估了心脏计分卡的预测性能，并与参考方法进行了比较。实验结果表明，计分卡模型在区分疾病与非病例方面的性能有所提高（心脏健康状况和HF的AUC分别为0.8403和0.9412），并且模型具有良好的校准。以逻辑回归系数的形式提供了对HF和心脏健康状况预测的临床见解，这表明用跨越多种心血管病理生理学的多标志物面板增强传统风险因素比参考方法具有更好的性能。此外，还提供了一个与即时医疗微型设备的生物标志物测量进行无缝集成的框架，并描述了一种基于套索的特征选择过程，用于在多标志物面板中向下选择生物标志物。
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