Bivona Derek J, Tallavajhala Srikar, Abdi Mohamad, Oomen Pim J A, Gao Xu, Malhotra Rohit, Darby Andrew E, Monfredi Oliver J, Mangrum J Michael, Mason Pamela K, Mazimba Sula, Salerno Michael, Kramer Christopher M, Epstein Frederick H, Holmes Jeffrey W, Bilchick Kenneth C
Department of Medicine, University of Virginia Health System, Charlottesville, Virginia.
Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, Virginia.
Heart Rhythm O2. 2022 Jun 17;3(5):542-552. doi: 10.1016/j.hroo.2022.06.005. eCollection 2022 Oct.
Cardiac resynchronization therapy (CRT) response is complex, and better approaches are required to predict survival and need for advanced therapies.
The objective was to use machine learning to characterize multidimensional CRT response and its relationship with long-term survival.
Associations of 39 baseline features (including cardiac magnetic resonance [CMR] findings and clinical parameters such as glomerular filtration rate [GFR]) with a multidimensional CRT response vector (consisting of post-CRT left ventricular end-systolic volume index [LVESVI] fractional change, post-CRT B-type natriuretic peptide, and change in peak VO) were evaluated. Machine learning generated response clusters, and cross-validation assessed associations of clusters with 4-year survival.
Among 200 patients (median age 67.4 years, 27.0% women) with CRT and CMR, associations with more than 1 response parameter were noted for the CMR CURE-SVD dyssynchrony parameter (associated with post-CRT brain natriuretic peptide [BNP] and LVESVI fractional change) and GFR (associated with peak VO and post-CRT BNP). Machine learning defined 3 response clusters: cluster 1 (n = 123, 90.2% survival [best]), cluster 2 (n = 45, 60.0% survival [intermediate]), and cluster 3 (n = 32, 34.4% survival [worst]). Adding the 6-month response cluster to baseline features improved the area under the receiver operating characteristic curve for 4-year survival from 0.78 to 0.86 ( = .02). A web-based application was developed for cluster determination in future patients.
Machine learning characterizes distinct CRT response clusters influenced by CMR features, kidney function, and other factors. These clusters have a strong and additive influence on long-term survival relative to baseline features.
心脏再同步治疗（CRT）反应复杂，需要更好的方法来预测生存率以及对高级治疗的需求。
目的是使用机器学习来描述多维CRT反应及其与长期生存的关系。
评估了39个基线特征（包括心脏磁共振成像[CMR]结果和临床参数，如肾小球滤过率[GFR]）与多维CRT反应向量（由CRT后左心室收缩末期容积指数[LVESVI]的分数变化、CRT后B型利钠肽以及峰值VO的变化组成）之间的关联。机器学习生成反应簇，交叉验证评估簇与4年生存率之间的关联。
在200例接受CRT和CMR检查的患者（中位年龄67.4岁，27.0%为女性）中，CMR CURE-SVD不同步参数（与CRT后脑利钠肽[BNP]和LVESVI分数变化相关）和GFR（与峰值VO和CRT后BNP相关）与多个反应参数存在关联。机器学习定义了3个反应簇：簇1（n = 123，生存率90.2%[最佳]）、簇2（n = 45，生存率60.0%[中等]）和簇3（n = 32，生存率34.4%[最差]）。将6个月时的反应簇添加到基线特征中，使4年生存率的受试者工作特征曲线下面积从0.78提高到0.86（P = 0.02）。开发了一个基于网络的应用程序，用于确定未来患者的簇。
机器学习描述了受CMR特征、肾功能和其他因素影响的不同CRT反应簇。相对于基线特征，这些簇对长期生存有强烈且累加的影响。
