Risk prediction models for mortality in patients with cardiovascular disease: The BioBank Japan project

心血管疾病患者死亡率风险预测模型:日本生物银行项目

阅读:1

Abstract

BACKGROUND: Cardiovascular disease (CVD) is a leading cause of death in Japan. The present study aimed to develop new risk prediction models for long-term risks of all-cause and cardiovascular death in patients with chronic phase CVD. METHODS: Among the subjects registered in the BioBank Japan database, 15,058 patients aged ≥40 years with chronic ischemic CVD (ischemic stroke or myocardial infarction) were divided randomly into a derivation cohort (n = 10,039) and validation cohort (n = 5019). These subjects were followed up for 8.55 years in median. Risk prediction models for all-cause and cardiovascular death were developed using the derivation cohort by Cox proportional hazards regression. Their prediction performances for 5-year risk of mortality were evaluated in the validation cohort. RESULTS: During the follow-up, all-cause and cardiovascular death events were observed in 2962 and 962 patients from the derivation cohort and 1536 and 481 from the validation cohort, respectively. Risk prediction models for all-cause and cardiovascular death were developed from the derivation cohort using ten traditional cardiovascular risk factors, namely, age, sex, CVD subtype, hypertension, diabetes, total cholesterol, body mass index, current smoking, current drinking, and physical activity. These models demonstrated modest discrimination (c-statistics, 0.703 for all-cause death; 0.685 for cardiovascular death) and good calibration (Hosmer-Lemeshow χ(2)-test, P = 0.17 and 0.15, respectively) in the validation cohort. CONCLUSIONS: We developed and validated risk prediction models of all-cause and cardiovascular death for patients with chronic ischemic CVD. These models would be useful for estimating the long-term risk of mortality in chronic phase CVD.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。