Derivation and Validation of a Risk Model for Prediction of Cardiovascular Disease Events in UK Indian Asians
CVD rates are roughly 70% higher among UK Indian Asians compared to European whites, representing a major challenge for NHS and the local health economies where minority ethnic groups are centred.
The Imperial College study team have recorded baseline characterisation of 22,623 Indian Asian men and women aged 35-74 years and free from clinically manifest cardiovascular disease (CVD), in the London Life Sciences Prospective Population (LOLIPOP) study.
Indian Asians have been recruited from the lists of 58 GPs in West London, during 2003-7. The Clinical Informatics team while at St. George’s, prior to their move to University of Surrey, identified 19,060 people, 67.4% of the LOLIPOP cohort of approximately 28K, from primary care records.
Possible CVD events identified through follow-up, death certification, routine and clinical databases will be validated against source data.
Suggested data sources include: coroner’s reports, hospital and primary care records where available.
Events were initially coded by a researcher, according to written, internationally accepted criteria. A process of scrutiny was developed to ensure the validity of this process.
- Delivery of an integrated programme of research including: Ascertainment of CVD events using routine and electronic data sources
- Validation of CVD cases through review of source data (Part relating to Primary Care data)
- Health economic analysis of the introduction of the CVD risk prediction calculator for use in Indian Asians Qualitative study to evaluate the utility and acceptability to general practitioners and individuals of implementing the CVD risk prediction model in general practice (Part related to assessment of risk).
We visited and re-established our relationship with participating practices to facilitate the conduct of a data validation exercise. We have previously conducted very similar studies1. We piloted this out of area (Surrey) and then ran it in west London.
Once permissions are in place to access hospital records we will target records where no hospital record of an event; or where there was a diagnosis we are unable to triangulate by finding appropriate therapy/observations.
We will include a sensitivity analysis looking at therapy/test results we would expect to be associated with a vascular diagnosis but one is not present in the records (e.g. Prescription of a nitrate where there is not CVD diagnosis). Further information about how we go about validating diagnoses can be found in our diabetes work, carried out with RCGP and NHS Diabetes
Further Sources of Information:
1 de Lusignan S, van Weel C. The use of routinely collected computer data for research in primary care: opportunities and challenges. Family Practice 2006;23(2):253-63. (Pubmed)
1 Anandarajah S, Tai T, de Lusignan S, Stevens P. O’Donoghue D, Walker M. The validity of searching routinely collected general practice computer data to identify patients with chronic kidney disease (CKD): a manual review of 500 medical records. Nephrology, Dialysis, Transplantation 2005; 20(10):2089-96. (Pubmed)
1 Drummond MF, O’Brien BJ, Stoddard GL, Torrance G. Methods for the economic evaluation of health care programmes. 2nd edition, Oxford Medical Publications, 1997 (Pubmed)
1 National Institute for Clinical Excellence. Guide to methods of technology appraisal, (NO515), 2004 (NICE-PDF)
1 Debar S, Kumarapeli P, Kaski JC, de Lusignan S. Addressing modifiable risk factors for coronary heart disease in primary care: an evidence-base lost in translation Fam Pract. 2010;27(4):370-8. (Pubmed)
1 Crinson I, Gallagher H, Thomas N, de Lusignan S. How ready is general practice to improve quality in chronic kidney disease? A diagnostic analysis. Br J Gen Pract. 2010;60(575):403-9. (Pubmed)