Risk Assessment in Patients with a Left Ventricular Assist Device Across INTERMACS Profiles Using Bayesian Analysis.
Risk Assessment in Patients with a Left Ventricular Assist Device Across INTERMACS Profiles Using Bayesian Analysis.
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使用贝叶斯分析对 INTERMACS 档案中左心室辅助装置患者进行风险评估。
DOI：
10.1097/mat.0000000000000910
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Antaki,JamesF
中科院分区：
文献类型：
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作者：
Kanwar,ManreetK;Lohmueller,LisaC;Teuteberg,Jeffrey;Kormos,RobertL;Rogers,JosephG;Benza,RaymondL;Lindenfeld,Joann;McIlvennan,Colleen;Bailey,StephenH;Murali,Srinivas;Antaki,JamesF
目前预测左心室辅助装置（LVAD）后结局的风险分层模型范围有限。我们评估了贝叶斯模型在各种机械辅助循环支持国际登记系统（INTERMACS或IM）配置文件、器械类型和植入策略中对LVAD后死亡率进行分层的性能。我们对2012年至2016年IM登记研究中记录的10，206例LVAD患者进行了回顾性分析。使用来自8，222名患者（推导队列）的推导贝叶斯算法，我们将风险预测算法应用于剩余的2，055名患者（验证队列）。根据疾病严重程度（IM特征）、器械类型（轴向与离心）和策略（桥接至移植或目标治疗），在植入后1、3和12个月评估死亡风险。在验证队列中，15%（n= 308）被归类为IM特征1，36%（n= 752）被归类为特征2，33%（n= 672）被归类为特征3，15%（n= 311）被归类为特征4-7。贝叶斯算法显示出对重度HF患者的短期（1和3个月）和长期（1年）死亡率的良好区分（曲线1-3），受试者工作特征曲线下面积（AUC）在0.63和0.74之间。算法在轴向和离心器械（AUC，0.68-0.74）以及移植或目标治疗适应症的桥接（AUC，0.66-0.73）中表现相当好。贝叶斯模型在1年时的性能优于现有风险模型的上级性能。Bayesian算法允许在不同IM配置文件、器械类型和植入策略中对LVAD植入后进行风险分层。
Current risk stratification models to predict outcomes after a left ventricular assist device (LVAD) are limited in scope. We assessed the performance of Bayesian models to stratify post-LVAD mortality across various International Registry for Mechanically Assisted Circulatory Support (INTERMACS or IM) Profiles, device types, and implant strategies. We performed a retrospective analysis of 10,206 LVAD patients recorded in the IM registry from 2012 to 2016. Using derived Bayesian algorithms from 8,222 patients (derivation cohort), we applied the risk-prediction algorithms to the remaining 2,055 patients (validation cohort). Risk of mortality was assessed at 1, 3, and 12 months post implant according to disease severity (IM profiles), device type (axial versus centrifugal) and strategy (bridge to transplantation or destination therapy). Fifteen percentage (n= 308) were categorized as IM profile 1, 36%(n= 752) as profile 2, 33%(n= 672) as profile 3, and 15%(n= 311) as profile 4–7 in the validation cohort. The Bayesian algorithms showed good discrimination for both short-term (1 and 3 months) and long-term (1 year) mortality for patients with severe HF (Profiles 1–3), with the receiver operating characteristic area under the curve (AUC) between 0.63 and 0.74. The algorithms performed reasonably well in both axial and centrifugal devices (AUC, 0.68–0.74), as well as bridge to transplantation or destination therapy indication (AUC, 0.66–0.73). The performance of the Bayesian models at 1 year was superior to the existing risk models. Bayesian algorithms allow for risk stratification after LVAD implantation across different IM profiles, device types, and implant strategies.