Doumouras Barbara S, Lee Douglas S, Levy Wayne C, Alba Ana C
Heart Failure and Transplant Program, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.
Institute for Clinical Evaluative Sciences, Peter Munk Cardiac Centre and Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, ON, Canada.
Curr Heart Fail Rep. 2018 Feb;15(1):24-36. doi: 10.1007/s11897-018-0375-y.
While prediction models incorporating biomarkers are used in heart failure, these have shown wide-ranging discrimination and calibration. This review will discuss externally validated biomarker-based risk models in chronic heart failure patients assessing their quality and relevance to clinical practice.
Biomarkers may help in determining prognosis in chronic heart failure patients as they reflect early pathologic processes, even before symptoms or worsening disease. We present the characteristics and describe the performance of 10 externally validated prediction models including at least one biomarker among their predictive factors. Very few models report adequate discrimination and calibration. Some studies evaluated the additional predictive value of adding a biomarker to a model. However, these have not been routinely assessed in subsequent validation studies. New and existing prediction models should include biomarkers, which improve model performance. Ongoing research is needed to assess the performance of models in contemporary patients.
虽然纳入生物标志物的预测模型用于心力衰竭，但这些模型的区分度和校准度差异很大。本综述将讨论在慢性心力衰竭患者中经过外部验证的基于生物标志物的风险模型，评估其质量及与临床实践的相关性。
生物标志物可反映慢性心力衰竭患者早期病理过程，甚至在症状出现或疾病恶化之前，有助于确定预后。我们介绍了10个经过外部验证的预测模型的特征，并描述了其性能，这些模型的预测因素中至少包括一种生物标志物。很少有模型报告有足够的区分度和校准度。一些研究评估了在模型中添加生物标志物的额外预测价值。然而，在后续验证研究中尚未对其进行常规评估。新的和现有的预测模型应纳入生物标志物，以改善模型性能。需要开展正在进行的研究来评估模型在当代患者中的性能。
