Machine Learning-Based Prediction of Myocardial Recovery in Patients With Left Ventricular Assist Device Support.
Machine Learning-Based Prediction of Myocardial Recovery in Patients With Left Ventricular Assist Device Support.
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DOI：
10.1161/circheartfailure.121.008711
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发表时间：
2022-01
期刊：
影响因子：
--
通讯作者：
Uriel N
中科院分区：
文献类型：
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作者：
Topkara VK;Elias P;Jain R;Sayer G;Burkhoff D;Uriel N
前瞻性研究表明，积极的药理学疗法与泵速优化可能会导致左心室辅助装置支持的大量患者的心肌恢复（LVAD）建议确定使用基于机器学习的模型是否可以预测LVAD患者的心肌恢复。
在研究中包括20,270名具有耐用的摩擦型lvad的成年患者，使用了98个原始的临床变量。 Kaplan-Meier分析。
Lasso确定了与LVAD引起的心肌恢复相关的28个独特的临床特征，包括年龄，HF的病因，心理社会风险因素，实验室价值，心脏率和节奏和节奏以及Echocartiographic指数在ROC曲线（AUC）中获得0.813 – 0.824型号的效果（AUC）的效果效果（AUC）。和先前确定的LVAD恢复风险得分（I-CARS和I-TOP）为0.744和0.748（P <0.05），预计通过机器学习模型恢复的患者表明，在验证群体中，与未预测的验证者相比，在验证群体中恢复了较高的心肌恢复事件（18.8％Vs Vs pup）。
机器学习可能是确定LVAD患者子集的宝贵工具，这些患者可能更有可能对心肌恢复方案做出反应。
Prospective studies demonstrate that aggressive pharmacological therapy combined with pump speed optimization may result in myocardial recovery in larger numbers of patients supported with left ventricular assist device (LVAD). This study sought to determine whether use of machine learning based models predict LVAD patients with myocardial recovery resulting in pump explant 20,270 adult patients with a durable continuous-flow LVAD in the INTERMACS registry were included in the study. 98 raw clinical variables were screened using least absolute shrinkage and selection operator (LASSO) for selection of features associated with LVAD-induced myocardial recovery. Machine learning models were developed in the training dataset (70%) and were assessed in the validation dataset (30%) by receiver operating curve (ROC) and Kaplan-Meier analysis. LASSO identified 28 unique clinical features associated with LVAD-induced myocardial recovery including, age, etiology of HF, psychosocial risk factors, laboratory values, cardiac rate and rhythm, and echocardiographic indices. Machine learning models achieved areas under the ROC curve (AUCs) of 0.813 – 0.824 in the validation dataset outperforming logistic regression-based new INTERMACS recovery risk score (AUC of 0.796) and previously established LVAD recovery risk scores (I-CARS and I-TOPS) with AUCs of 0.744 and 0.748 (p< 0.05). Patients who were predicted to recover by machine learning model demonstrated significantly higher incidence of myocardial recovery resulting in LVAD explant in the validation cohort compared to those who were not predicted to recover (18.8% vs 2.6% at 4 years of pump support). Machine learning can be a valuable tool to identify subsets of LVAD patients who may be more likely to respond to myocardial recovery protocols.