Risk stratification in pulmonary arteria hypertension using Bayesian analysis
Risk stratification in pulmonary arteria hypertension using Bayesian analysis
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DOI：
10.1183/13993003.00008-2020
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发表时间：
2020-08-01
影响因子：
24.3
通讯作者：
Benza, Raymond L.
中科院分区：
文献类型：
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作者：
Kanwar, Manreet K.;Gomberg-Maitland, Mardi;Benza, Raymond L.
背景资料：目前肺动脉高压（PAH）的风险分层工具在其区分能力方面有限，部分原因是假设预后临床变量与临床结局具有独立和线性关系。我们试图证明基于贝叶斯网络的机器学习在增强现有最先进的风险分层工具REVEAL 2.0的预测能力方面的实用性。方法：我们使用REVEAL 2.0中发现的相同变量和临界点，推导出一个树增强朴素贝叶斯模型（名为PHORA）来预测REVEAL登记研究中纳入的PAH患者的1年生存率。PHORA模型在内部（REVEAL登记中心）和外部（COMPERA和PHSANZ登记中心）进行了确认。根据2015年欧洲心脏病学会/欧洲呼吸学会指南，患者被分为低、中、高风险（12个月死亡率分别为10%）。结果：PHORA预测1年生存率的曲线下面积（AUC）为0.80，比REVEAL 2.0（AUC 0.76）有所改善。当在COMPERA和PHSANZ登记研究中确认时，PHORA的AUC分别为0.74和0.80。1-PHORA预测的年生存率在低风险评分的患者中较高，而在高风险评分的患者中较差（p
Background: Current risk stratification tools in pulmonary arterial hypertension (PAH) are limited in their discriminatory abilities, partly due to the assumption that prognostic clinical variables have an independent and linear relationship to clinical outcomes. We sought to demonstrate the utility of Bayesian network-based machine learning in enhancing the predictive ability of an existing state-of-the-art risk stratification tool, REVEAL 2.0.Methods: We derived a tree-augmented naive Bayes model (titled PHORA) to predict 1-year survival in PAH patients included in the REVEAL registry, using the same variables and cut-points found in REVEAL 2.0. PHORA models were validated internally (within the REVEAL registry) and externally (in the COMPERA and PHSANZ registries). Patients were classified as low-, intermediate- and high-risk (10% 12-month mortality, respectively) based on the 2015 European Society of Cardiology/European Respiratory Society guidelines.Results: PHORA had an area under the curve (AUC) of 0.80 for predicting 1-year survival, which was an improvement over REVEAL 2.0 (AUC 0.76). When validated in the COMPERA and PHSANZ registries, PHORA demonstrated an AUG of 0.74 and 0.80, respectively. 1-year survival rates predicted by PHORA were greater for patients with lower risk scores and poorer for those with higher risk scores (p