Division of Cardiology, Department of Medicine, David Geffen School of Medicine.
Department of Medicine Statistics Core.
J Heart Lung Transplant. 2018 Aug;37(8):956-966. doi: 10.1016/j.healun.2018.03.006. Epub 2018 Mar 22.
Survival after heart transplantation (HTx) is limited by complications related to alloreactivity, immune suppression, and adverse effects of pharmacologic therapies. We hypothesize that time-dependent phenomapping of clinical and molecular data sets is a valuable approach to clinical assessments and guiding medical management to improve outcomes.
We analyzed clinical, therapeutic, biomarker, and outcome data from 94 adult HTx patients and 1,557 clinical encounters performed between January 2010 and April 2013. Multivariate analyses were used to evaluate the association between immunosuppression therapy, biomarkers, and the combined clinical end point of death, allograft loss, retransplantation, and rejection. Data were analyzed by K-means clustering (K = 2) to identify patterns of similar combined immunosuppression management, and percentile slopes were computed to examine the changes in dosages over time. Findings were correlated with clinical parameters, human leucocyte antigen antibody titers, and peripheral blood mononuclear cell gene expression of the AlloMap (CareDx, Inc., Brisbane, CA) test genes. An intragraft, heart tissue gene coexpression network analysis was performed.
Unsupervised cluster analysis of immunosuppressive therapies identified 2 groups, 1 characterized by a steeper immunosuppression minimization, associated with a higher likelihood for the combined end point, and the other by a less pronounced change. A time-dependent phenomap suggested that patients in the group with higher event rates had increased human leukocyte antigen class I and II antibody titers, higher expression of the FLT3 AlloMap gene, and lower expression of the MARCH8 and WDR40A AlloMap genes. Intramyocardial biomarker-related coexpression network analysis of the FLT3 gene showed an immune system-related network underlying this biomarker.
Time-dependent precision phenotyping is a mechanistically insightful, data-driven approach to characterize patterns of clinical care and identify ways to improve clinical management and outcomes.
心脏移植（HTx）后的存活率受到同种异体反应、免疫抑制和药物治疗副作用相关并发症的限制。我们假设，临床和分子数据集的时变现象图谱是一种有价值的临床评估方法，可以指导医疗管理以改善结果。
我们分析了 94 例成年 HTx 患者和 2010 年 1 月至 2013 年 4 月期间进行的 1557 次临床就诊的临床、治疗、生物标志物和结局数据。多变量分析用于评估免疫抑制治疗、生物标志物与死亡、移植物丢失、再次移植和排斥的复合临床终点之间的关联。通过 K 均值聚类（K=2）分析数据，以识别相似的联合免疫抑制管理模式，并计算百分位数斜率以检查剂量随时间的变化。将发现与临床参数、人类白细胞抗原抗体滴度以及 AlloMap（CareDx，Inc.，Brisbane，CA）检测基因的外周血单核细胞基因表达相关联。进行了心脏组织基因共表达网络分析。
免疫抑制治疗的无监督聚类分析确定了 2 组，1 组以更陡峭的免疫抑制最小化特征为特征，与复合终点的可能性更高，另 1 组以不太明显的变化为特征。时变现象图谱表明，高事件发生率患者的人类白细胞抗原 I 类和 II 类抗体滴度更高，FLT3 AlloMap 基因表达更高，MARCH8 和 WDR40A AlloMap 基因表达更低。FLT3 基因的心肌内生物标志物相关共表达网络分析显示，该生物标志物的基础是一个免疫系统相关网络。
时变精准表型是一种具有机制洞察力的数据驱动方法，可用于描述临床护理模式，并确定改善临床管理和结局的方法。
