Abstract:
:The two best known approaches to predicting cardiovascular risk are Framingham and QRISK. Both methods correctly predict less than 70% of cases, with a high ratio of false positive predictions to true predictions. Each uses a combination of predictors that is applied to the data only once. The present approach uses the Discriminant Function with multiple applications. A British sample of data on cardiovascular risk was analysed. Principal Components analysis was used to reveal the underlying structure of the data-it identified four independent determinants of the data. Discriminant Function analysis in three stages was then used to accommodate the difficulties of dealing with multiple determinants. Ninety-four percent of the cases with cardiovascular incidents (CVI) were predicted correctly up to more than 20 years ahead, with a misclassification rate overall of 2.8 errors for every one correct. When checked for likely shrinkage from sample to sample using the Jacknife method 92% of CVI's were correctly predicted. Instead of a single application of a linear combination of predictors to find those people most likely to have cardiovascular events a repeated application of the predictors to the residuals from the previous prediction stage is likely to find a much higher proportion of true predictions and with much less error. The results also allow for a simple way of conveying the risk of CVI to individual patients.
journal_name
Eur J Epidemioljournal_title
European journal of epidemiologyauthors
Marsh RWdoi
10.1007/s10654-011-9634-xsubject
Has Abstractpub_date
2011-12-01 00:00:00pages
915-8issue
12eissn
0393-2990issn
1573-7284journal_volume
26pub_type
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