A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality.
A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality.
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DOI：
10.1097/mat.0000000000000209
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发表时间：
2015-05
期刊：
影响因子：
--
通讯作者：
Antaki JF
中科院分区：
文献类型：
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作者：
Loghmanpour NA;Kanwar MK;Druzdzel MJ;Benza RL;Murali S;Antaki JF
用于选择左心室辅助器械（LVAD）患者的现有风险评估工具，如目的地治疗风险评分（DTRS）和HeartMate II风险评分（HMRS）的预测能力有限。本研究的目的是克服传统的统计方法的局限性，进行贝叶斯分析的综合INTERMACS数据集的第一个应用程序，并将其与HMRS。我们回顾性分析了8，050名连续流（CF）LVAD患者和226个植入前变量。然后，我们推导了植入后五个时间终点的死亡率的贝叶斯模型（30天、90天、6个月、1年和2年），准确度分别为95、90、90、83和78%，Kappa值分别为0.43、0.37、0.37、0.45和0.43，ROC下面积分别为91、82、82，80%和81%。与HMRS相比，90天和1年时的ROC分别为57%和60%。植入前干预（如透析、ECMO和呼吸机）是主要的风险标志。贝叶斯模型能够可靠地表示多个变量对临床结果的复杂因果关系。他们的潜力，开发一个可靠的风险分层工具，用于临床决策LVAD患者鼓励进一步的调查。
Existing risk assessment tools for patient selection for left ventricular assist devices (LVADs) such as the Destination Therapy Risk Score (DTRS) and HeartMate II Risk Score (HMRS) have limited predictive ability. This study aims to overcome the limitations of traditional statistical methods by performing the first application of Bayesian analysis to the comprehensive INTERMACS dataset and comparing it to HMRS. We retrospectively analyzed 8,050 continuous flow (CF) LVAD patients and 226 pre-implant variables. We then derived Bayesian models for mortality at each of five time endpoints post-implant (30 day, 90 day, 6 month, 1 year, and 2 year), achieving accuracies of 95, 90, 90, 83, and 78%, Kappa values of 0.43, 0.37, 0.37, 0.45, and 0.43, and area under the ROC of 91, 82, 82, 80 and 81% respectively. This was in comparison to the HMRS with an ROC of 57 and 60% at 90-days and 1-year, respectively. Pre-implant interventions such as dialysis, ECMO, and ventilators were major contributing risk markers. Bayesian models have the ability to reliably represent the complex causal relationships of multiple variables on clinical outcomes. Their potential to develop a reliable risk stratification tool for use in clinical decision making on LVAD patients encourages further investigation.