- 著者
- Rachel R. Huxley Yochiro Hirakawa Mohammad Akhtar Hussain Wichai Aekplakorn Xin Wang Sanne AE Peters Abdullah Mamun Mark Woodward
- 出版者
- 日本循環器学会
- 雑誌
- Circulation Journal (ISSN:13469843)
- 巻号頁・発行日
- pp.CJ-15-0661, (Released:2015-07-07)
- 参考文献数
- 36
- 被引用文献数
- 8 33
Cardiovascular disease (CVD) is the leading cause of mortality worldwide, causing an estimated 18 million deaths annually. Much of the burden of CVD resides in lower- and middle-income countries, particularly those Asian countries comprising the Western Pacific Region. Epidemiological studies have convincingly shown that up to 90% of all CVD can be explained by a small number of modifiable risk factors, including blood pressure, smoking, diabetes, total cholesterol and excess body weight. However, the relationship between these risk factors and coronary artery disease and stroke often differ by age and sex, and yet these differences are often overlooked in burden of disease estimations. As such, that can result in either an over- or under-estimation of the disease burden in specific population subgroups, which may affect resource allocation of healthcare. In this review, we derive the most reliable and previously unpublished estimates of the age- and sex-specific burden of vascular disease attributable to the aforementioned risk factors for 10 of the most populous Asian countries in the Western Pacific Region. Understanding how the burden of vascular disease is distributed within and between populations is crucial for developing appropriate health policies and effective treatment strategies, particularly in resource-poor settings.
- 著者
- Kunihiro Matsushita Yingying Sang Jingsha Chen Shoshana H. Ballew Michael Shlipak Josef Coresh Carmen A. Peralta Mark Woodward
- 出版者
- The Japanese Circulation Society
- 雑誌
- Circulation Journal (ISSN:13469843)
- 巻号頁・発行日
- pp.CJ-19-0320, (Released:2019-07-19)
- 参考文献数
- 21
- 被引用文献数
- 10
Background:Cardiovascular guidelines include risk prediction models for decision making that lack the capacity to include novel predictors.Methods and Results:We explored a new “predictor patch” approach to calibrating the predicted risk from a base model according to 2 components from outside datasets: (1) the difference in observed vs. expected values of novel predictors and (2) the hazard ratios (HRs) for novel predictors, in a scenario of adding kidney measures for cardiovascular mortality. Using 4 US cohorts (n=54,425) we alternately chose 1 as the base dataset and constructed a base prediction model with traditional predictors for cross-validation. In the 3 other “outside” datasets, we developed a linear regression model with traditional predictors for estimating expected values of glomerular filtration rate and albuminuria and obtained their adjusted HRs of cardiovascular mortality, together constituting a “patch” for adding kidney measures to the base model. The base model predicted cardiovascular mortality well in each cohort (c-statistic 0.78–0.91). The addition of kidney measures using a patch significantly improved discrimination (cross-validated ∆c-statistic 0.006 [0.004–0.008]) to a similar degree as refitting these kidney measures in each base dataset.Conclusions:The addition of kidney measures using our new “predictor patch” approach based on estimates from outside datasets improved cardiovascular mortality prediction based on traditional predictors, providing an option to incorporate novel predictors to an existing prediction model.