The development and implementation of stroke risk prediction model in National Health Insurance Service's personal health record

Background and objective The purpose of this study was to build a 10-year stroke prediction model and categorize a probability of stroke using the Korean national health examination data. Then it intended to develop the algorithm to provide a personalized warning on the basis of each user's level of stroke risk and a lifestyle correction message about the stroke risk factors. Methods Subject to national health examinees in 2002–2003, the stroke prediction model identified when stroke was first diagnosed by following-up the cohort until 2013 and estimated a 10-year probability of stroke. It sorted the user's individual probability of stroke into five categories – normal, slightly high, high, risky, very risky, according to the five ranges of average probability of stroke in comparison to total population – less than 50 percentile, 50–70, 70–90, 90–99.9, more than 99.9 percentile, and constructed the personalized warning and lifestyle correction messages by each category. Results Risk factors in stroke risk model include the age, BMI, cholesterol, hypertension, diabetes, smoking status and intensity, physical activity, alcohol drinking, past history (hypertension, coronary heart disease) and family history (stroke, coronary heart disease). The AUC values of stroke risk prediction model from the external validation data set were 0.83 in men and 0.82 in women, which showed a high predictive power. The probability of stroke within 10 years for men in normal group (less than 50 percentile) was less than 3.92% and those in very risky group (top 0.01 percentile) was 66.2% and over. The women's probability of stroke within 10 years was less than 3.77% in normal group (less than 50 percentile) and 55.24% and over in very risky group. Conclusions This study developed the stroke risk prediction model and the personalized warning and the lifestyle correction message based on the national health examination data and uploaded them to the personal health record service called My Health Bank in the health information website - Health iN . By doing so, it urged medical users to strengthen the motivation of health management and induced changes in their health behaviors.