Tan Yen Yi, Montagnese Sara, Mani Ali R
Network Physiology Laboratory, UCL Division of Medicine, University College London, London, United Kingdom.
Department of Medicine, University of Padova, Padua, Italy.
Front Physiol. 2020 Aug 5;11:983. doi: 10.3389/fphys.2020.00983. eCollection 2020.
A healthy individual has a high degree of functional connectivity between organ systems, which can be represented graphically in a network map. Disruption of this system connectivity is associated with mortality in life-threatening acute illnesses, demonstrated by a network approach. However, this approach has not been applied to chronic multisystem diseases and may be more reliable than conventional individual organ prognostic scoring methods. Cirrhosis is a chronic disease of the liver with multisystem involvement. Development of an efficient model for prediction of mortality in cirrhosis requires a profound understanding of the pathophysiologic processes that lead to poor prognosis. In the present study, we use a network approach to evaluate the differences in organ system connectivity between survivors and non-survivors in a group of well-characterized patients with cirrhosis.
201 patients with cirrhosis originally referred to the Clinic five at the University Hospital of Padova, with 13 clinical variables available representing hepatic, metabolic, haematopoietic, immune, neural and renal organ systems were retrospectively enrolled and followed up for 3, 6, and 12 months. Software was designed to compute the correlation network maps of organ system interaction in survivors and non-survivors using analysis indices: A. Bonferroni corrected Pearson's correlation coefficient and B. Mutual Information. Network structure was quantitatively evaluated using the measures of edges, average degree of connectivity and closeness, and qualitatively using clinical significance. Pair-matching was also implemented as a further step after initial general analysis to control for sample size and Model for End-Stage Liver Disease (MELD-Na) score between the groups.
There was a higher number of significant correlations in survivors, as indicated by both the analysis indices of Bonferroni corrected Pearson's correlation coefficient and the Mutual Information analysis. The number of edges, average degree of connectivity and closeness were significantly higher in survivors compared to non-survivors group. Pair-matching for MELD-Na was also associated with increased connectivity in survivors compared to non-survivors over 3 and 6 months follow up.
This study demonstrates the application of a network approach in evaluating functional connectivity of organ systems in liver cirrhosis, demonstrating a significant degree of network disruption in organ systems in non-survivors. Network analysis of organ systems may provide insight in developing novel prognostic models for organ allocation in patients with cirrhosis.
健康个体的器官系统之间具有高度的功能连通性,这可以在网络图中以图形方式表示。通过网络方法已证明,这种系统连通性的破坏与危及生命的急性疾病中的死亡率相关。然而,这种方法尚未应用于慢性多系统疾病,并且可能比传统的单个器官预后评分方法更可靠。肝硬化是一种累及多系统的慢性肝病。要开发一种有效的肝硬化死亡率预测模型,需要深刻理解导致预后不良的病理生理过程。在本研究中,我们使用网络方法评估一组特征明确的肝硬化患者中幸存者和非幸存者之间器官系统连通性的差异。
回顾性纳入最初转诊至帕多瓦大学医院第五诊所的201例肝硬化患者,这些患者有13个代表肝脏、代谢、造血、免疫、神经和肾脏器官系统的临床变量,并进行了3个月、6个月和12个月的随访。设计软件使用分析指标计算幸存者和非幸存者中器官系统相互作用的相关网络图:A. 经Bonferroni校正后的Pearson相关系数和B. 互信息。使用边数、平均连通度和接近度等指标对网络结构进行定量评估,并从临床意义上进行定性评估。在初始的一般分析之后,还进一步实施了配对匹配,以控制两组之间的样本量和终末期肝病模型(MELD-Na)评分。
经Bonferroni校正后的Pearson相关系数分析指标和互信息分析均表明,幸存者中存在更多显著相关性。与非幸存者组相比,幸存者的边数、平均连通度和接近度显著更高。在3个月和6个月的随访中,MELD-Na的配对匹配也显示幸存者的连通性比非幸存者增加。
本研究证明了网络方法在评估肝硬化患者器官系统功能连通性中的应用,表明非幸存者的器官系统存在显著程度的网络破坏。器官系统的网络分析可能为开发用于肝硬化患者器官分配的新型预后模型提供见解。