Cardiovascular disease screening in general practice: General practitioner recording of common risk factors
Introduction
Cardiovascular disease (CVD) is a major cause of morbidity and mortality. In Australia, approximately 16% of the total projected burden of disease and injury are attributable to CVD (Waters et al., 2013). Despite improvements in diagnosis and treatment leading to reduced mortality, it is still responsible for reduced quality of life and is the largest contributor to death out of all disease groups, forming a significant proportion of total healthcare expenditure (Australian Institute of Health and Welfare, 2011).
CVD encompasses several different conditions, including ischaemic heart disease, cardiac failure, cerebrovascular disease and stroke. Aside from factors such as age, gender and family history, these conditions are closely associated with a variety of modifiable risk factors including weight, smoking and alcohol consumption, a diet high in saturated fats, and stress (Australian Institute of Health and Welfare, 2011). These modifiable risk factors play a major role in the development of CVD, and it has been well established that most of the current burden of CVD is a result of lifestyle and related factors (Yusuf et al., 2004). In addition, these risk factors can lead to the development of a number of other chronic conditions including obesity, diabetes and musculoskeletal disease, therefore contributing to the burden of multimorbidity in patients with CVD. Consequently, appropriate management of these risk factors is critical in the prevention of CVD and other comorbid chronic conditions (Australian Institute of Health and Welfare, 2012).
Approximately 85% of the Australian population visit a general practice (GP) each year, making it an ideal setting for the implementation of CVD prevention strategies (Britt et al., 2014). Australian guidelines for the management of absolute cardiovascular disease risk in the GP setting have been developed, recommending screening for certain patient groups using the Framingham risk equation, and the monitoring of the risk factors necessary for screening (National Vascular Disease Prevention Alliance, 2012). The guidelines also recommend measurement of a broader range and collection of risk factor information beyond what is required to calculate the Framingham risk score.
The lack of recording of CVD risk factors can negatively impact on CVD screening in the GP setting, since the data necessary to calculate CVD risk scores is not available (McManus et al., 2002). While it has been shown that preventive care is generally not experienced equally across the population, (Dryden et al., 2012, Harris et al., 2013) the extent of CVD risk factor recording (necessary for CVD screening) is not known. This study sought to estimate current GP recording of risk factors as recommended by the guidelines for the management of absolute CVD risk (National Vascular Disease Prevention Alliance, 2012).
Section snippets
Study population
We performed a retrospective analysis of GP patient data collected between July 2011 and September 2014, derived from the Melbourne East Monash General Practice Database (MAGNET) (Mazza et al., 2016). MAGNET is comprised of routinely collected data, extracted from the computerised medical records of practices in the inner east Melbourne region. Originally extracted for the purposes of quality improvement initiatives within practices, the data provides a regionally representative snapshot of the
Results
A total of 149,306 active patients were identified, with 118,255 (79.2%) patients between 45 and 74 years of age and 85,882 (57.5%) females (Table 1). 57,211 (38.3%) patients were identified as suffering from cardiovascular problems, with 14,163 (9.5%) of patients identified as having diabetes.
Discussion
This analysis of routinely collected GP data found CVD risk factor recording was below that recommended by the Australian guidelines for the management of absolute cardiovascular disease risk across sociodemographic and clinical groups. While most patients had at least one risk factor recorded (smoking and blood pressure being the most common across groups), less than half had the set necessary to calculate the patient's Framingham score for absolute CVD risk, and only around 1% had all risk
Conflict of interest disclosure
None reported, financial or otherwise.
Acknowledgments
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We thank Adam McLeod, who oversees the MEGPN data program, for facilitating acquisition of the data and for his comments during the drafting of the manuscript.
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