A systematic review of predictive risk models for diabetes complications based on large scale clinical studies☆,☆☆,★
Introduction
Risk factors for diabetic complications have been intensively studied during the last decades, and these studies greatly improved the current scientific knowledge about the biological processes underlying diabetes. Risk factors can be used for the definition of risk assessment models to be exploited in the clinical practice. These models are part of the computational core of clinical/medical applications able to stratify diabetes patients according to their probability of developing complications or experiencing adverse events.
A risk assessment model consists of any type of algorithm or mathematical formula (e.g., a set of rules, a decision tree, a weighted sum, etc.) for assessing the overall statistical probability of certain adverse outcomes to occur in the future. Medical risk assessment may provide probabilistic statements as the likelihood that certain complications may occur given the present and historical health status.
When risk assessment models are built upon data collected from large scale, longitudinal clinical studies, they are able to perform predictions in the long term, i.e. on a time horizon spanning up to a decade and beyond. These models are the backbone of risk assessment tools used in clinical practice.
Given the health and social burden caused by diabetes-related complications, it is not surprising that several scientific works have proposed risk assessment models able to evaluate the probability for diabetes patients of developing one or more complications on the long-term period.
The aim of the present review is to compare and summarize the most relevant risk assessment models for diabetes-related complications published in the literature. After an initial screening of the large-scale, longitudinal clinical studies performed for studying diabetes complications, the risk assessment models built on top of these studies are presented and compared with each other.
This review has been performed in the context of the European Union (EU) funded project REACTION (http://www.reactionproject.eu/). The scope of the REACTION project is to design and develop a platform of services that can provide professionals with remote monitoring and therapeutic management of diabetic patients in different healthcare regimes. Part of this effort consists in reviewing, developing and implementing tools able to provide long term risk assessment evaluations about diabetic complications based on patient's current health state and history. The tools will take advantage of integration between instantaneously measured data from sensors, historical data from the Electronic Patient Record (EPR), statistical data from stratification studies, statistical databases and evidence-based case management repositories. The methodological approach followed in the REACTION project for the realisation of the long term risk assessment models started from the evaluation of existing risk assessment studies and models in order to (a) identify available sources of data suitable for deriving long term risk assessment models and (b) exploit the experience gained from previous research. The present review reports and summarizes the results of this systematic literature search.
Section snippets
Methods
A selection of the studies related to diabetes mellitus complications was started. Only studies with at least 1000 subjects and 5 years of follow-up were considered. We included both prospective and retrospective, as well as Type I and Type II diabetes studies. Six major studies were identified, namely DCCT/EDIC(The Diabetes Control and Complications Trial Research Group, 2003, The Epidemiology of Diabetes Interventions and Complications (EDIC) Research Group, 1999), EuroDiab (The EURODIAB IDDM
Results
Large scale clinical studies typically last around a decade, involve thousands of patients in numerous health centres, and measure different aspects of patients' clinical medical profiles. Thus, not surprisingly the data collected in each study can be employed for deriving multiple risk assessment models, differing from each other for predicted outcomes, involved parameters or analytical techniques.
Table 1, Table 2, Table 3 give an overview of the long-term risk assessment studies and models.
Discussion
In the context of the REACTION project, this survey will provide the basis for the selection of the most relevant risk score to be implemented within the REACTION platform. Moreover, published risk models will provide insights and guidelines in a first phase for developing more advanced models based on the available studies, and in a second phase for devising and building new risk models based upon the data collected from the REACTION project.
A principal objective in the clinical management of
Conclusions
Risk factors for diabetic complications have been extensively studied during the last decades, and these studies greatly improved the current scientific knowledge about the pathophysiological processes underlying diabetes. The most common predictive models for diabetes complications are related to cardiovascular disease, coronary heart disease and diabetic retinopathy. However, such studies have important limitations, mainly due to their long temporal duration. Major differences in the medical
Acknowledgments
This work is supported by the European Commission's Seventh Framework Program in the area of Personal Health Systems under Grant Agreement no. 248590 (REACTION FP7-IP-No 248590).
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Grant Support: This work is supported by the European Commission's Seventh Framework Program in the area of Personal Health Systems under Grant Agreement no. 248590 (REACTION FP7-IP-No 248590).
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Authors' contributions: VL and LK were the main authors for the manuscript and conducted the article search, screening, selection and risk factors analysis. FC, EL and IT assisted in writing and reviewing the article.
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Competing interests: The authors declare that they have no competing interests.
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These authors contributed equally to this work.