Cardiovascular disease risk prediction models for type 2 diabetes: verification in Chinese patients

Jingyuan LIANG; Qianqian LI; Xiaofei LIU; Jiamin WANG; Peng SHEN; Hongbo LIN; Xun TANG; Pei GAO

doi:10.16016/j.1000-5404.202101028

您当前的位置：

首页 >

文章列表页 >

Cardiovascular disease risk prediction models for type 2 diabetes: verification in Chinese patients

Monographic Reports: | 更新时间：2026-01-09

- Cardiovascular disease risk prediction models for type 2 diabetes: verification in Chinese patients
- Journal of Third Military Medical University Vol. 43, Issue 12, Pages: 1097-1102(2021)
- 作者机构：
  
  1.北京，北京大学：公共卫生学院流行病与卫生统计学系
  2.北京，北京大学：临床研究所真实世界证据评价中心
  3.315100 浙江宁波，宁波市鄞州区疾病预防控制中心
  4.100191 北京，分子心血管学教育部重点实验室
- 作者简介：
- 基金信息：
  
  the National Natural Science Foundation of China(81961128006);the National Natural Science Foundation of China(81973132);the National Key Research and Development Plan of China(2020YFC2003503)
- DOI：10.16016/j.1000-5404.202101028
  CLC： R181.3;R54;R587.1
- Received：04 January 2021，
  
  Revised：2021-02-10，
  
  Published：30 June 2021
- 稿件说明：
移动端阅览
Jingyuan LIANG, Qianqian LI, Xiaofei LIU, et al. Cardiovascular disease risk prediction models for type 2 diabetes: verification in Chinese patients[J]. Journal of Third Military Medical University, 2021, 43(12): 1097-1102.
DOI：

Jingyuan LIANG, Qianqian LI, Xiaofei LIU, et al. Cardiovascular disease risk prediction models for type 2 diabetes: verification in Chinese patients[J]. Journal of Third Military Medical University, 2021, 43(12): 1097-1102. DOI： 10.16016/j.1000-5404.202101028.

摘要

目的

在我国2型糖尿病人群中独立验证并比较美国弗明汉(Framingham)心血管病预测模型和新西兰糖尿病队列研究(diabetes cohort study

DCS)心血管病预测模型的准确性。

方法

研究对象为2010年1月1日至2018年12月31日在中国鄞州电子健康档案研究项目中有医疗记录的48 063名基线时未患有心血管病的30~74岁2型糖尿病患者。采用Kaplan-Meier方法计算研究对象5年心血管病实际发病风险，采用再校准后的Framingham模型和DCS模型分别计算研究对象5年心血管病的预测风险。采用C统计量评价模型的区分度，Hosmer-Lemeshow卡方值和校准图评价模型的校准度。

结果

经过中位5.9年的随访，48 063名研究对象中共出现2 867例新发心血管病事件。Framingham模型和DCS模型均低估了研究对象的发病风险，在男性中分别低估了3.6%和21.1%，而在女性中则分别低估了21.0%和20.9%。在男性中，DCS模型的区分度优于Framingham模型，两模型的C统计量和95%可信区间分别为0.716(0.702~0.730)和0.662(0.648~0.677)；但校准度方面两模型表现均不理想(

0.05)。在女性中DCS模型和Framingham模型的C统计量和95%可信区间分别为0.743(0.730~0.756)和0.686(0.673~0.700)，校准度均较差(

0.05)。

结论

Framingham模型和DCS模型不适用于我国2型糖尿病人群心血管病风险预测。

Abstract

Objective

To independently verify and compare the accuracy of 2 cardiovascular risk prediction models

Framingham model and New Zealand Diabetes Cohort Study (DCS) model in Chinese patients with type 2 diabetes mellitus (T2DM).

Methods

A total of 48 063 T2DM patients aged from 30 to 74 who had medical records but no cardiovascular diseases (CVD) in the Chinese electronic health records research in Yinzhou (CHERRY) were enrolled from January 1

2010 to December 31

2018. Kaplan Meier method was used to calculate the actual risk of CVD in following 5 years

and the recalibrated Framingham model and DCS model were adopted to estimate the predicted 5-year risks of the diseases. The performance of the models was assessed in terms of discrimination and calibration degrees

using C-index and Hosmer-Lemeshow

Chi

-square value respectively.

Results

Through a median follow-up of 5.9 years

2 867 new CVD cases were observed among the subjects. However

both the Framingham and the DCS models underestimated the risk of subjects

by 3.6% (Framingham) and 21.1% (DCS) in males

and by 21.0% (Framingham) and 20.9% (DCS) in females

respectively. For males

the DCS model (C-index=0.716

95%

: 0.702~0.730) had a better discriminative ability than the Framingham one (C-index=0.662

95%

: 0.648~0.677)

though the performance of calibration was not ideal in both models (

0.05). For females

C-index was 0.743 in DCS (95%

: 0.730~0.756) and 0.686 in Framingham models (95%

: 0.673-0.700)

both showing poor performance on calibration (

0.05).

Conclusion

Both Framingham model and DCS model are confirmed inapplicable to CVD risk prediction in Chinese population with T2DM.

关键词

Keywords

references

GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet, 2020, 396(10258): 1204-1222. DOI: 10.1016/S0140-6736(20)30925-9.

LLOYD-JONES D M, BRAUN L T, NDUMELE C E, et al . Use of risk assessment tools to guide decision-making in the primary prevention of atherosclerotic cardiovascular disease: a special report from the American Heart Association and American College of Cardiology[J ] . Circulation, 2019, 139(2 5): e1162-e1177. DOI: 10.1161/CIR.0000000000000638.

WRIGHT A K, SUAREZ-ORTEGON M F, READ S H, et al . Risk factor control and cardiovascular event risk in people with type 2 diabetes in primary and secondary prevention settings[J ] . Circulation, 2020, 142(20): 1925-1936. DOI: 10.1161/CIRCULATIONAHA.120.046783.

唐迅, 张杜丹, 何柳, 等. China-PAR模型在北方农村人群中预测动脉粥样硬化性心血管疾病发病风险的应用[J]. 北京大学学报(医学版), 2017, 49(3): 439-445. DOI: 10.3969/j.issn.1671-167X.2017.03.011.

TANG X, ZHANG D D, HE L, et al . Application of the China- PAR risk prediction model for atherosclerotic cardiovascular disease in a rural northern Chinese population[J ] . J Peking Univ Heal Sci, 2017, 49(3): 439-445. DOI: 10.3969/j.issn.1671-167X.2017.03.011.

D'AGOSTINO R B, VASAN R S, PENCINA M J, et al . General cardiovascular risk profile for use in primary care: the Framingham Heart Study[J ] . Circulation, 2008, 117(6): 743-753. DOI: 10.1161/CIRCULATIONAHA.107.699579.

ELLEY C R, ROBINSON E, KENEALY T, et al . Derivation and validation of a new cardiovascular risk score for people with type 2 diabetes: the New Zealand diabetes cohort study[J ] . Diabetes Care, 2010, 33(6): 1347-1352. DOI: 10.2337/dc09-1444.

LIN H B, TANG X, SHEN P, et al . Using big data to improve cardiovascular care and outcomes in China: a protocol for theChinese Electronic Health Records Research in Yinzhou (CHERRY) Study[J ] . BMJ Open, 2018, 8(2): e019698. DOI: 10.1136/bmjopen-2017-019698.

ALBA A C, AGORITSAS T, WALSH M, et al . Discrimination and calibration of clinical prediction models: users' guides to the medical literature[J ] . JAMA, 2017, 318(14): 1377-1384. DOI: 10.1001/jama.2017.12126.

LITTLE R J A, RUBIN D B. Statistical analysis with missing data[M]. 3rd ed. Hoboken: Wiley, 2020.

李建新, 李莹, 刘芳超, 等. 中国≥40岁糖尿病患者的心血管疾病风险[J]. 中华心血管病杂志, 2020, 48(11): 968-974. DOI: 10.3760/cma.j.cn112148-20191203- 00733.

LI J X, LI Y, LIU F C, et al . Cardiovascular disease risk in diabetes patients aged 40 years old and above in China[J ] . Chin J Cardiol, 2020, 48(11): 968-974. DOI: 10.3760/cma.j.cn112148-20191203-00733.

SUN C Q, XU F, LIU X T, et al . Comparison of validation and application on various cardiovascular disease mortality risk prediction models in Chin ese rural population[J ] . Sci Rep, 2017, 7: 43227. DOI: 10.1038/srep43227.

VAN DIEREN S, BEULENS J W, KENGNE A P, et al . Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review[J ] . Heart, 2012, 98(5): 360-369. DOI: 10.1136/heartjnl-2011-300734.

READ S H, VAN DIEPEN M, COLHOUN H M, et al . Performance of cardiovascular disease risk scores in people diagnosed with type 2 diabetes: external validation using data from the national Scottish diabetes register[J ] . Diabetes Care, 2018, 41(9): 2010-2018. DOI: 10.2337/dc18-0578.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Predictive value of TyG-BMI, AIP, and postprandial glucose excursion for sarcopenia in patients with type 2 diabetes mellitus

Prediction of risk for acute kidney injury and its progression to mortality in obese patients admitted to ICU postoperatively

Risk prediction model of in-hospital heart failure in patients with coronary heart disease after percutaneous coronary intervention: development and validation

Renal protective effect of equol on type 2 diabetic rats

Correlation of cumulative exposure to metabolic factors with progression of lower extremity arterial disease in type 2 diabetic patients

Related Author

CHEN Yue¹

YU Weiwei

GAO Jin

CHEN Xingyu

CHENG Qingfeng

CUI Jian

LI Qiang

MU Guo

Related Institution

Department of Quality Control

Department of Dermatology and Venereology

Department of Endocrinology，Jiangbei Branch/No.9, Hospital of PLA Army, First Affiliated Hospital， Army Medical University （Third Military Medical University）

Department of Endocrinology， the First Affiliated Hospital of Chongqing Medical University

Department of Pain Medicine， First Affiliated Hospital， Army Medical University （Third Military Medical University）

⁰