Targeted screening for cardiovascular disease (CVD) can be carried out using existing data from patient medical records. However, electronic medical records often contain missing data for which values must be estimated to produce risk scores. In a paper published in the journal Informatics in Primary Care, Andrew Dalton and colleagues compared two methods of substituting missing risk factor data: multiple imputation and the use of default National Health Survey values. They used patient-level data from patients in 70 general practices in Ealing, London and substituted missing risk factor data using the two methods.
They reported that using multiple imputation, mean CVD risk scores were similar to those using default national survey values, a simple method of imputation. There were fewer patients designated as high risk (>20%) using multiple imputation, although differences were again small (10.3% compared with 11.7%; 3.0% compared with 3.4% in women). Agreement in high-risk classification between methods was high (Kappa = 0.91 in men; 0.90 in women). Dalton and colleagues concluded that a simple method of substituting missing risk factor data can produce reliable estimates of CVD risk scores.
They reported that using multiple imputation, mean CVD risk scores were similar to those using default national survey values, a simple method of imputation. There were fewer patients designated as high risk (>20%) using multiple imputation, although differences were again small (10.3% compared with 11.7%; 3.0% compared with 3.4% in women). Agreement in high-risk classification between methods was high (Kappa = 0.91 in men; 0.90 in women). Dalton and colleagues concluded that a simple method of substituting missing risk factor data can produce reliable estimates of CVD risk scores.
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