Precision Phenotyping of Dilated Cardiomyopathy Using Multidimensional Data.
Precision Phenotyping of Dilated Cardiomyopathy Using Multidimensional Data.
复制标题
DOI：
10.1016/j.jacc.2022.03.375
复制
发表时间：
2022-06-07
影响因子：
24
通讯作者：
Prasad, Sanjay K.
中科院分区：
文献类型：
--
作者：
Tayal, Upasana;Verdonschot, Job A. J.;Hazebroek, Mark R.;Howard, James;Gregson, John;Newsome, Simon;Gulati, Ankur;Pua, Chee Jian;Halliday, Brian P.;Lota, Amrit S.;Buchan, Rachel J.;Whiffin, Nicola;Kanapeckaite, Lina;Baruah, Resham;Jarman, Julian W. E.;O'Regan, Declan P.;Barton, Paul J. R.;Ware, James S.;Pennell, Dudley J.;Adriaans, Bouke P.;Bekkers, Sebastiaan C. A. M.;Donovan, Jackie;Frenneaux, Michael;Cooper, Leslie T.;Januzzi, James L., Jr.;Cleland, John G. F.;Cook, Stuart A.;Deo, Rahul C.;Heymans, Stephane R. B.;Prasad, Sanjay K.
扩张型心肌病（DCM）是异质性病因的最终常见表现。不良结局强调了对射血分数以外疾病分层的需要。本研究的目的是使用多参数数据识别新的、可重复的扩张型心肌病亚表型，以改善患者分层。DCM患者（2009-2016年入组）的纵向、观察性英国衍生（n = 426;中位年龄54岁; 67%男性）和荷兰验证（n = 239;中位年龄56岁; 64%男性）队列，进行临床、遗传、心血管磁共振和蛋白质组学评估。使用轮廓回归的机器学习识别了新的疾病亚型。惩罚多项式逻辑回归用于验证。巢式考克斯模型比较了新的分组和传统的风险测量。主要复合终点为心血管死亡、心力衰竭或心律失常事件（中位随访4年）。总共确定了3种新的DCM亚型：促纤维化代谢、轻度非纤维化和双心室损害。在推导（P < 0.0001）和验证队列中，亚型之间的预后不同。新的促纤维化代谢亚型有更多的糖尿病，普遍心肌纤维化，保留右心室功能，肌酐升高。对于临床应用，5个变量足以进行分类（左心室和右心室收缩末期容积、左心房容积、心肌纤维化和肌酐）。添加新的DCM亚型将C统计量从0.60改善至0.76。白细胞介素-4受体-α在推导（HR：3.6; 95% CI：1.9-6.5; P = 0.00002）和验证队列（HR：1.94; 95% CI：1.3-2.8; P = 0.00005）中被确定为新型预后生物标志物。利用广泛可用的临床和生物学数据确定了三种可重复的、机制上不同的DCM亚型，为传统风险模型增加了预后价值。它们可以改善患者对新干预措施的选择，从而实现精准医疗。
Dilated cardiomyopathy (DCM) is a final common manifestation of heterogenous etiologies. Adverse outcomes highlight the need for disease stratification beyond ejection fraction. The purpose of this study was to identify novel, reproducible subphenotypes of DCM using multiparametric data for improved patient stratification. Longitudinal, observational UK-derivation (n = 426; median age 54 years; 67% men) and Dutch-validation (n = 239; median age 56 years; 64% men) cohorts of DCM patients (enrolled 2009-2016) with clinical, genetic, cardiovascular magnetic resonance, and proteomic assessments. Machine learning with profile regression identified novel disease subtypes. Penalized multinomial logistic regression was used for validation. Nested Cox models compared novel groupings to conventional risk measures. Primary composite outcome was cardiovascular death, heart failure, or arrhythmia events (median follow-up 4 years). In total, 3 novel DCM subtypes were identified: profibrotic metabolic, mild nonfibrotic, and biventricular impairment. Prognosis differed between subtypes in both the derivation (P < 0.0001) and validation cohorts. The novel profibrotic metabolic subtype had more diabetes, universal myocardial fibrosis, preserved right ventricular function, and elevated creatinine. For clinical application, 5 variables were sufficient for classification (left and right ventricular end-systolic volumes, left atrial volume, myocardial fibrosis, and creatinine). Adding the novel DCM subtype improved the C-statistic from 0.60 to 0.76. Interleukin-4 receptor-alpha was identified as a novel prognostic biomarker in derivation (HR: 3.6; 95% CI: 1.9-6.5; P = 0.00002) and validation cohorts (HR: 1.94; 95% CI: 1.3-2.8; P = 0.00005). Three reproducible, mechanistically distinct DCM subtypes were identified using widely available clinical and biological data, adding prognostic value to traditional risk models. They may improve patient selection for novel interventions, thereby enabling precision medicine.
登录
查看更多内容
影响因子：
18.2
作者：
Tayal, Upasana;Wage, Ricardo;Prasad, Sanjay K.
通讯作者：
Prasad, Sanjay K.
影响因子：
120.7
作者：
Gulati, Ankur;Jabbour, Andrew;Prasad, Sanjay K.
通讯作者：
Prasad, Sanjay K.
影响因子：
18.2
作者：
Merlo, Marco;Cannata, Antonio;Sinagra, Gianfranco
通讯作者：
Sinagra, Gianfranco
新西兰一般实践中的药物风险管理和健康平等：回顾性横断面研究。
影响因子：
4.8
作者：
Leitch S;Zeng J;Smith A;Stokes T
通讯作者：
Stokes T
影响因子：
6.4
作者：
Maceira, A. M.;Prasad, S. K.;Pennell, D. J.
通讯作者：
Pennell, D. J.