A Holistic Clustering Methodology for Liver Transplantation Survival.
A Holistic Clustering Methodology for Liver Transplantation Survival.
复制标题
肝移植存活率的整体聚类方法。
DOI：
10.1097/nnr.0000000000000289
复制
发表时间：
2018
期刊：
影响因子：
2.5
通讯作者：
Westra,BonnieL
中科院分区：
文献类型：
--
作者：
Pruinelli,Lisiane;Simon,GyörgyJ;Monsen,KarenA;Pruett,Timothy;Gross,CynthiaR;Radosevich,DavidM;Westra,BonnieL
背景肝移植占了大量的程序与主要投资的所有利益相关者参与;然而，有限的研究解决了肝移植人群异质性移植前预测移植后生存率。该研究的目的是确定新的和有意义的患者群预测死亡率，解释肝移植人群的异质性，MethodsA回顾性队列研究的344例成人患者谁接受肝移植2008年至2014年之间。预测因素包括合并症和其他次优健康状态（分为11个身体系统）的严重程度评分、移植的主要原因、人口统计学/环境因素和终末期肝病模型评分。Logistic回归被用来计算的严重程度评分，聚类加权欧氏距离的分层聚类，Lasso惩罚回归特征的集群，Kaplan-Meier分析比较生存率跨clusters.ResultsCluster 1包括更严重的循环系统问题的患者。第2组代表原发病更严重的老年患者，而第3组包含最健康的患者。聚类4和聚类5分别代表肌肉骨骼（例如，疼痛）和内分泌问题（例如，营养不良）患者。各组之间的死亡率存在统计学显著差异（p<. 001).结论：本研究开发了一种新的方法，以解决异质性和高维肝移植人群的特点，在一个单一的研究预测生存。数据建模和其他心理社会风险因素的整体方法有可能解决肝移植护理和研究的整体护理挑战。
BackgroundLiver transplants account for a high number of procedures with major investments from all stakeholders involved; however, limited studies address liver transplant population heterogeneity pretransplant predictive of posttransplant survival.ObjectiveThe aim of the study was to identify novel and meaningful patient clusters predictive of mortality that explains the heterogeneity of liver transplant population, taking a holistic approach.MethodsA retrospective cohort study of 344 adult patients who underwent liver transplantation between 2008 through 2014. Predictors were summarized severity scores for comorbidities and other suboptimal health states grouped into 11 body systems, the primary reason for transplantation, demographics/environmental factors, and Model for End Liver Disease score. Logistic regression was used to compute the severity scores, hierarchical clustering with weighted Euclidean distance for clustering, Lasso-penalized regression for characterizing the clusters, and Kaplan–Meier analysis to compare survival across the clusters.ResultsCluster 1 included patients with more severe circulatory problems. Cluster 2 represented older patients with more severe primary disease, whereas Cluster 3 contained healthiest patients. Clusters 4 and 5 represented patients with musculoskeletal (eg, pain) and endocrine problems (eg, malnutrition), respectively. There was a statistically significant difference for mortality between clusters (p<. 001).ConclusionsThis study developed a novel methodology to address heterogeneous and high-dimensional liver transplant population characteristics in a single study predictive of survival. A holistic approach for data modeling and additional psychosocial risk factors has the potential to address holistically nursing challenges on liver transplant care and research.