Derivation and validation of 10-year all-cause and cardiovascular disease mortality prediction model for middle-aged and elderly community-dwelling adults in Taiwan

Abstract

Prediction model mainly focused on specific diseases, such as diabetes, hypertension, cardiovascular disease, or patients with cancer, or populations of Europe and America, thereby limiting its generalization. This study aimed to develop and validate a 10-year mortality risk score by using data from a population-representative sample of adults. Data were collected from 2,221 Taichung Community Health study participants aged ≥40 years. The baseline period of the study was 2004, and all participants were followed up until death or in 2016. Cox's proportional hazards regression analyses were used to develop the prediction model. A total of 262 deaths were ascertained during the 10-year follow-up. The all-cause mortality prediction model calculated the significant risk factors, namely, age, sex, marital status, physical activity, tobacco use, estimated glomerular filtration rate, and albumin-to-creatinine ratio, among the baseline risk factors. The expanded cardiovascular disease (CVD) mortality prediction model consisted of six variables: age, sex, body mass index, heart disease plus heart disease medication use, stroke plus medication use, and ankle-brachial index. The areas under receiver operating curves of the 3-, 5- and 10-year predictive models varied between 0.97, 0.96, and 0.88 for all-cause mortality, and between 0.97, 0.98, and 0.84 for expanded CVD mortality. These mortality prediction models are valid and can be used as tools to identify the increased risk for mortality.

Fig 1

Kaplan-Meier survival curves for (A) all-cause and (B) expanded CVD mortality according to the low, medium, and high-risk groups.