Comorbidity network for chronic disease: A novel approach to understand type 2 diabetes progression
Comorbidity network for chronic disease: A novel approach to understand type 2 diabetes progression
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DOI：
10.1016/j.ijmedinf.2018.04.001
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发表时间：
2018-07-01
影响因子：
4.9
通讯作者：
Srinivasan, Uma
中科院分区：
文献类型：
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作者：
Khan, Arif;Uddin, Shahadat;Srinivasan, Uma
背景：在昂贵的医院环境之外进行慢性病管理已成为政府、资助者和卫生保健服务提供者的主要目标。众所周知，慢性疾病如2型糖尿病（T2D）并不是孤立发生的，它与许多其他疾病和失调有共同的病因。澳大利亚糖尿病协会报告称，糖尿病与多种并发症有关，这些并发症会影响足部、眼睛、肾脏和心血管健康。例如，大约13%的澳大利亚糖尿病患者患有下肢神经损伤，超过15%的澳大利亚糖尿病患者患有糖尿病性视网膜病变，糖尿病现在是导致终末期肾病的主要原因。因此，我们的研究重点是了解合并症模式，这反过来可以增强我们对2型糖尿病等慢性疾病的多因素危险因素的理解。我们的研究方法是基于利用现有的行政卫生保健数据中存在的有价值的指标，这些数据是常规收集的，但在卫生研究中经常被忽视。其中一种行政保健数据是携带诊断信息的住院和出院数据，这些信息以ICD-10诊断代码的形式表示。对诊断代码及其关系的分析有助于我们构建共病网络，该网络可以为了解慢性疾病的进展模式和人群水平的共病网络提供见解。这种理解可以使医疗保健提供者制定适当的预防卫生政策，针对高危慢性疾病。方法与发现：本研究将网络理论原理应用于行政医疗数据。鉴于2型糖尿病的高患病率，我们选择2型糖尿病作为典型慢性疾病。利用图论和社会网络分析技术，我们开发了一个研究框架来理解和代表2型糖尿病的进展。我们提出了“共病网络”的概念，可以有效地模拟慢性疾病共病及其过渡模式，从而代表慢性疾病的进展。在生成网络时，我们进一步考虑了共病的归因效应；也就是说，我们不仅观察慢性病患者的疾病模式，还将其与非慢性病患者的疾病模式进行比较，以了解哪些合并症对慢性病途径的影响更大。研究框架使我们能够为两个队列中的每一个构建一个基线共病网络。然后将这两个网络进行比较并合并为单一的合并症网络，以发现糖尿病患者独有的合并症。该框架应用于从澳大利亚医疗保健环境中提取的行政数据。整个数据集包含来自75万名患者的约140万份入院记录，我们从中筛选并抽样了2300名糖尿病患者和2300名非糖尿病患者的记录。我们发现糖尿病患者和非糖尿病患者的健康轨迹有显著差异。糖尿病队列显示出更多的合并症患病率和更密集的网络特性。例如，在糖尿病队列中，心肝相关疾病、白内障等更为普遍。随着时间的推移，糖尿病队列健康轨迹中的疾病患病率几乎是非糖尿病队列患病率的两倍，表明疾病进展的方式完全不同。结论：本文提出了一个基于网络理论的研究框架，以了解慢性疾病的进展以及随时间变化的相关合并症。分析方法提供了见解，可以使医疗保健提供者制定有针对性的预防性健康管理计划，以减少住院率和相关的高成本。基线共病网络有潜力作为发展慢性疾病风险预测模型的基础。
Background: Chronic diseases management outside expensive hospital settings has become a major target for governments, funders and healthcare service providers. It is well known that chronic diseases such as Type 2 Diabetes (T2D) do not occur in isolation, and has a shared aetiology common to many other diseases and disorders. Diabetes Australia reports that it is associated with a myriad of complications, which affect the feet, eyes, kidneys, and cardiovascular health. For instance, nerve damage in the lower limbs affects around 13% of Australians with diabetes, diabetic retinopathy occurs in over 15% of Australians with diabetes, and diabetes is now the leading cause of end-stage kidney disease. Our research focus is therefore to understand the comorbidity pattern, which in turn can enhance our understanding of the multifactorial risk factors of chronic diseases like Type 2 Diabetes.Our research approach is based on utilising valuable indicators present in pre-existing administrative healthcare data, which are routinely collected but often neglected in health research. One such administrative healthcare data is the hospital admission and discharge data that carries information about diagnoses, which are represented in the form of ICD-10 diagnosis codes. Analysis of diagnoses codes and their relationships helps us construct comorbidity networks which can provide insights that can be used to understand chronic disease progression pattern and comorbidity network at a population level. This understanding can subsequently enable healthcare providers to formulate appropriate preventive health policies targeted to address high-risk chronic conditions.Methods and findings: The research utilises network theory principles applied to administrative healthcare data. Given the high rate of prevalence, we selected Type 2 Diabetes as the exemplar chronic disease. We have developed a research framework to understand and represent the progression of Type 2 diabetes, utilising graph theory and social network analysis techniques. We propose the concept of a 'comorbidity network' that can effectively model chronic disease comorbidities and their transition patterns, thereby representing the chronic disease progression. We further take the attribution effect of the comorbidities into account while generating the network; that is, we not only look at the pattern of disease in chronic disease patients, but also compare the disease pattern with that of non-chronic patients, to understand which comorbidities have a higher influence on the chronic disease pathway.The research framework enables us to construct a baseline comorbidity network for each of the two cohorts. It then compares and merges these two networks into single comorbidity network to discover the comorbidities that are exclusive to diabetic patients. This framework was applied on administrative data drawn from the Australian healthcare context. The overall dataset contained approximately 1.4 million admission records from 0.75 million patients, from which we filtered and sampled the records of 2300 diabetics and 2300 non-diabetic patients. We found significant difference in the health trajectory of diabetic and non-diabetic cohorts. The diabetic cohort exhibited more comorbidity prevalence and denser network properties. For example, in the diabetic cohort, heart and liver-related disorders, cataract etc. were more prevalent. Over time, the prevalence of diseases in the health trajectory of diabetic cohorts were almost double of the prevalence in the non-diabetic cohort, indicating entirely different ways of disease progression.Conclusions: The paper presents a research framework based on network theory to understand chronic disease progression along with associated comorbidities that manifest over time. The analysis methods provide insights that can enable healthcare providers to develop targeted preventive health management programs to reducehospital admissions and associated high costs. The baseline comorbidity network has the potential to be used as the basis to develop a chronic disease risk prediction model.