Original Article
Increasing the Detection of Familial Hypercholesterolaemia Using General Practice Electronic Databases

https://doi.org/10.1016/j.hlc.2016.09.012Get rights and content

Background

Familial hypercholesterolaemia (FH) is a common autosomal co-dominant condition that causes premature cardiovascular disease. Awareness of FH is poor and only 10–15% of the affected population is identified. Electronic health records provide an opportunity to increase detection and awareness in general practice

Objective

To determine whether a simple electronic extraction tool can increase detection of FH in general practice.

Method

An extraction tool applied to general practice electronic health records (EHR) to screen for FH, total cholesterol and low density lipoprotein cholesterol (LDL-c) levels in association with entered diagnostic criteria and demographic data in five general practices.

Results

Of 157,290 active patients examined, 0.7% (n=1081) had an LDL-c>5.0 mmol/L representing 1 in 146 of active patients. An additional 0.8% (n=1276) patients were at possible risk of FH. Of those with an LDL-c>5.0 mmol/L 43.7% of patients had no record of being prescribed statins. Twenty patients (0.013%) had a clinical diagnosis of FH entered in the EHR.

Conclusions

Patients at high risk of FH can be identified by a simple electronic screening method in general practice. Clinical data entry is variable in general practice. Targeted screening enables clinical assessment of patients at risk of cardiovascular disease and using the DLCNS will enable primary care to increase identification of FH. Approximately one in five patients extracted using this method, are likely to have phenotypically probable FH, making it a useful screening tool.

Introduction

Primary care is central to the increased detection and management of Familial Hypercholesterolaemia (FH) to reduce the corresponding burden of premature coronary artery disease (CAD). [1] Familial hypercholesterolaemia is an autosomal co-dominant disorder associated with elevated low density lipoprotein (LDL-c) cholesterol.[2] Heterozygote FH is thought now to occur in approximately 1 in 250 of the population.[3] There are estimated to be 80,000 people with FH in Australasia with only 10–15% of the affected population being identified and the average general practice likely to encounter 50–100 patients with FH, every year.[4] In Australia, the majority of people with FH are undiagnosed, often not treated to adequate targets, and their families are not tested for the possible 50% of affected first-degree relatives.[5], [6], [7] If FH is left untreated, 50% of men will have CHD by age 50 years and 30% of women by age 60 years.[3]

General practitioners (GP) request over 90% of LDL-c measurements in the community,[8] and are crucial in the management of hypercholesterolaemia and other cardiovascular risk factors for the prevention of cardiovascular disease. Commonly used cardiovascular risk calculators do not accurately assess risk in those with FH. Awareness of FH however is low amongst both GPs and specialist physicians. [9], [10].

We have previously reviewed the role of FH detection in the community by suggesting a multidisciplinary approach including: a broader primary care awareness and education program; auditing of electronic patient information on general practice databases; and, via pathology providers, to highlight people with very elevated LDL cholesterol at risk of FH.[1]

Over the past 25 years, computer use by Australian GPs has increased such that nearly all general practices in Australia use a computer at their practice.[11] Routinely collected health care data in GP electronic health records (GP EHR) can be mined, to allow identification of disease states and population patterns. [12] General practice EHR systems vary across general practices and extraction systems are required to be able to coordinate with different systems. Some GP EHR systems are capable of effectively extracting clinical data although the quality of the data has been found to be of varying consistency. [13] A number of GP EHR approaches have been used in other countries with varying success [14], [15]

We present a simple method to improve detection and to enhance awareness of FH in Australian primary care by using an electronic extraction tool suitably designed for GP EHRs.

Section snippets

Data Source

De-identified data were obtained using electronic extraction tools in five Perth metropolitan practices over two years using the Canning Tool [16]. This extraction tool is able to extract data directly from more than seven types of GP software which is applicable to over 95% of available Australian general practices. We had access to the databases of five general practices with an average of 5.5 full time equivalent GPs (FTE). Full time equivalent is defined as 37 hours worked per week in

Results

We were able to extract data from 157,290 active patients (Table 2).

Of the total number of active patients, 4.84% (n=7605) of extracted patients had been prescribed statins, 1.39% (n=2191) had a diagnosis of vascular disease, 3.73% (n= 5869) had a diagnosis of lipid disorders of which 20 patients (approximately 1 in 7900 active patients) had been coded with a diagnosis of FH. Of those extracted patients prescribed statins 16.8% (n=1276 of 7605) had a persistent measured LDL-c recorded between

Discussion

Familial hypercholesterolaemia is poorly identified in general practice [10]. Applying a simple data extraction tool to general practice software, we were able to detect a cohort of general practice patients at high risk of FH. These patients will require a targeted clinical assessment to assess the likelihood of FH. It is expected that between 30 and 50% of such patients would be found to have phenotypic FH. Clinical assessment of these at risk patients is required using additional

Conclusion

A simple electronic data extraction tool with defined criteria from GP EHRs can successfully identify cases at high risk of FH. Further, it is a useful tool for helping GPs to identify patients at risk of cardiovascular disease to better achieve total cholesterol and LDL-c targets. Identified cases will require further clinical review and the DLCNS assessment to target patients at risk of FH. Such cases on review, can be provided with more intensive treatment and management, appropriate

Conflict of Interest

There is no identifiable conflict of interest.

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