Complex Systems Research Group, Project Management Program, The University of Sydney, Sydney, NSW, Australia; Health Market Quality Research Stream, Capital Markets CRC, Level 3, 55 Harrington Street, Sydney, NSW, Australia.
Complex Systems Research Group, Project Management Program, The University of Sydney, Sydney, NSW, Australia.
Int J Med Inform. 2018 Jul;115:1-9. doi: 10.1016/j.ijmedinf.2018.04.001. Epub 2018 Apr 9.
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.
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.
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 reduce hospital 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.
在昂贵的医院环境之外管理慢性病已成为政府、资助者和医疗服务提供者的主要目标。众所周知，2 型糖尿病（T2D）等慢性病并非孤立发生，而是与许多其他疾病和障碍共有的病因。澳大利亚糖尿病协会报告称，它与无数并发症有关，这些并发症影响到脚、眼睛、肾脏和心血管健康。例如，下肢神经损伤影响约 13%的糖尿病患者，糖尿病视网膜病变发生在超过 15%的糖尿病患者中，而糖尿病现在是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，这反过来又可以增强我们对 2 型糖尿病等慢性病的多因素风险因素的理解。我们的研究方法基于利用现有行政医疗保健数据中有用的指标，这些指标是常规收集的，但在健康研究中经常被忽视。其中一种行政医疗保健数据是住院和出院数据，其中包含有关诊断的信息，这些信息以 ICD-10 诊断代码的形式呈现。诊断代码及其关系的分析有助于我们构建合并症网络，这些网络可以提供有助于了解慢性病在人群层面上的进展模式和合并症网络的见解。这种理解可以随后使医疗保健提供者能够制定针对高风险慢性病的适当预防保健政策。
该研究利用网络理论原则应用于行政医疗保健数据。鉴于患病率高，我们选择 2 型糖尿病作为范例慢性病。我们已经开发了一个研究框架，以利用图论和社交网络分析技术来理解和表示 2 型糖尿病的进展。我们提出了“合并症网络”的概念，该概念可以有效地对慢性病合并症及其转化模式进行建模，从而代表慢性病的进展。我们进一步考虑了合并症的归因效应，同时生成网络；也就是说，我们不仅关注慢性疾病患者的疾病模式，还将该疾病模式与非慢性患者的疾病模式进行比较，以了解哪些合并症对慢性疾病途径有更高的影响。该研究框架使我们能够为两个队列中的每一个构建基线合并症网络。然后，它将这两个网络进行比较并合并为单个合并症网络，以发现仅存在于糖尿病患者中的合并症。该框架应用于来自澳大利亚医疗保健背景的行政数据。总体数据集包含大约 140 万条来自 75 万患者的入院记录，我们从中筛选并采样了 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存在显著差异。糖尿病队列表现出更高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，心脏和肝脏相关疾病、白内障等更为常见。随着时间的推移，糖尿病患者健康轨迹中的疾病患病率几乎是非糖尿病患者的两倍，表明疾病的发展方式完全不同。
本文提出了一个基于网络理论的研究框架，用于了解随时间推移表现出的慢性病进展及其相关合并症。分析方法提供了见解，可以使医疗保健提供者能够制定有针对性的预防保健管理计划，以减少住院治疗和相关高额费用。基线合并症网络有可能被用作开发慢性病风险预测模型的基础。
