Andrew B Kirke1, Rita A Barbour2, Sally Burrows3, Damon A Bell4, Alistair W Vickery2, Jon Emery5, Gerald F Watts6. 1. School of Primary, Aboriginal and Rural Health Care, University of Western Australia, Perth, Australia. Electronic address: andrew.kirke@rcswa.edu.au. 2. School of Primary, Aboriginal and Rural Health Care, University of Western Australia, Perth, Australia. 3. School of Medicine & Pharmacology, University of Western Australia, Perth, Australia. 4. School of Medicine & Pharmacology, University of Western Australia, Perth, Australia; Lipid Disorders Clinic, Cardiovascular Medicine, Royal Perth Hospital, Perth, Australia; Department of Clinical Biochemistry, PathWest Royal Perth Hospital, Perth, Australia. 5. School of Primary, Aboriginal and Rural Health Care, University of Western Australia, Perth, Australia; General Practice and Primary Care Academic Centre, University of Melbourne, Melbourne, Australia. 6. School of Medicine & Pharmacology, University of Western Australia, Perth, Australia; Lipid Disorders Clinic, Cardiovascular Medicine, Royal Perth Hospital, Perth, Australia.
Abstract
BACKGROUND: Familial hypercholesterolaemia (FH), a co-dominantly inherited disease of cholesterol that markedly increases risk of premature coronary artery disease (CAD), is significantly under-diagnosed. Primary health care is increasingly seen as a setting in which to increase the detection rate of index cases. We report a prospective study of three methods of case detection using pre-existing primary health care services in one community. METHODS: Three methods of case detection were tested: pathology laboratory database search, workplace health checks and general practice database search. People identified at risk by each of the three screening methods were offered detailed assessment for FH using the Dutch Lipid Clinic Network Criteria score (DLCNCS). RESULTS: 1316 participants underwent detailed assessment for FH. The proportion of at risk people identified for further assessment was in decreasing order: GP (659 of 2494, 26.4%), workplace assessment (60 of 268, 22.4%) and pathology database (597 of 4517, 13.2%) p<0.001. Eight-six (6.5%) were identified as clinical FH (DLCNCS>5) of which 59 had genetic testing and 11 of 59, 18.6%, were confirmed to have a mutation causing FH. Pathology database detected the greatest number of clinical FH (51 of 86, 59.3%) and mutation positive participants (8 of 11, 72.7%). CONCLUSION: Screening within primary health care was successful in detecting participants with FH. An integrated case detection model combining screening of pathology and GP databases is proposed.
BACKGROUND:Familial hypercholesterolaemia (FH), a co-dominantly inherited disease of cholesterol that markedly increases risk of premature coronary artery disease (CAD), is significantly under-diagnosed. Primary health care is increasingly seen as a setting in which to increase the detection rate of index cases. We report a prospective study of three methods of case detection using pre-existing primary health care services in one community. METHODS: Three methods of case detection were tested: pathology laboratory database search, workplace health checks and general practice database search. People identified at risk by each of the three screening methods were offered detailed assessment for FH using the Dutch Lipid Clinic Network Criteria score (DLCNCS). RESULTS: 1316 participants underwent detailed assessment for FH. The proportion of at risk people identified for further assessment was in decreasing order: GP (659 of 2494, 26.4%), workplace assessment (60 of 268, 22.4%) and pathology database (597 of 4517, 13.2%) p<0.001. Eight-six (6.5%) were identified as clinical FH (DLCNCS>5) of which 59 had genetic testing and 11 of 59, 18.6%, were confirmed to have a mutation causing FH. Pathology database detected the greatest number of clinical FH (51 of 86, 59.3%) and mutation positive participants (8 of 11, 72.7%). CONCLUSION: Screening within primary health care was successful in detecting participants with FH. An integrated case detection model combining screening of pathology and GP databases is proposed.
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