capabilities.
Electronic health records have been rapidly adopted in recent years and potentially offer a wealth of data for use in risk prediction research. However, much of this information is stored as unstructured data. This has led to the development of information extraction and data mining systems which search electronic records and identify relevant cardiovascular risk factor data [54]. These systems utilise a combination of machine learning and rule based clinical text mining techniques.
Cardiovascular risk prediction models utilising machine learning are yet to be adopted into routine clinical practice and international guidelines. Nevertheless, there is ever increasing research using non-traditional data sources and potential novel prognostic biomarkers which is likely to impact clinical risk prediction and treatment decision making in the coming years.
Conclusions
The prevalence of diabetes, particularly type 2 diabetes, continues to grow worldwide, contributing greatly to the global burden of cardiovascular disease. Despite substantial improvements in rates of cardiovascular events and mortality in recent decades, the absolute number of premature deaths due to cardiovascular disease continues to rise in low- and middle-income countries in the setting of population growth and ageing. People with diabetes have a significantly increased risk of cardiovascular disease, but the gap compared to those without diabetes has narrowed in some populations, likely as a result of improved management of multiple risk factors and better systems of care. The management of cardiovascular risk remains a cornerstone of diabetes care. While ischaemic heart disease remains the most common cause of mortality in diabetes, heart failure and peripheral arterial disease are now the most common presenting cardiovascular complications.
Strong evidence from clinical trials supports aggressive risk factor modification in patients with established cardiovascular disease, so-called secondary prevention. Multifactorial therapy to modify cardiovascular risk in diabetes has also been shown to be effective as primary prevention. Such interventions generally target glycaemia, blood pressure, lipids and use of anti-platelet agents. However, there is controversy surrounding how best to approach risk prediction and primary prevention therapy in patients with diabetes. Some guidelines recommend that the degree of risk inferred by diabetes itself is sufficient to warrant pharmacotherapy targeting tight control of cardiovascular risk factors such as hypertension and dyslipidaemia in most patients. Increasingly, the assessment of absolute cardiovascular risk is being incorporated into clinical care and decision making around the risk modifying therapy.
Numerous risk scores have been developed and they give an estimate of absolute cardiovascular risk. The evidence base for using these scores in patients with diabetes is not as strong as in the general population and there are few trials investigating their clinical efficacy. The addition of novel biomarkers and genetic risk scores to cardiovascular risk prediction models may strengthen statistical associations but at this stage have not been shown to substantially alter clinical practice among diabetes patients. Understanding risk at the individual level has the potential to guide decisions regarding use of lipid-lowering agents, antihypertensives and antiplatelet agents. It may also help guide choice of antihyperglycaemic therapy in light of growing evidence regarding the cardiovascular benefits and possible risks of some agents. Knowledge of individual-level risk can also be used to educate and motivate patients.
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18Colagiuri
