Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings.
Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings.
复制标题
DOI：
10.1007/s00125-021-05640-y
复制
发表时间：
2022-04
期刊：
影响因子：
8.2
通讯作者：
Schmidt AF
中科院分区：
文献类型：
--
作者：
Dziopa K;Asselbergs FW;Gratton J;Chaturvedi N;Schmidt AF
我们的目的是比较2型糖尿病患者CVD（即冠心病和中风）的风险预测评分的表现，以及更广泛的CVD定义，包括心房颤动和心力衰竭（CVD+）。评分是通过文献综述确定的，无论预测心血管结局的类型或2型糖尿病患者的纳入都包括在内。对168,871名英国2型糖尿病患者（年龄≥18岁，无既往CVD+）的当代代表性样本进行了评估。缺失的观测值使用多重插值解决。我们评估了22个评分：13个来自普通人群，9个来自2型糖尿病患者。在普通人群中得出的系统性冠状动脉风险评估（SCORE） CVD规则对CVD （C统计值0.67 [95% CI 0.67, 0.67]）和CVD+ （C统计值0.69 [95% CI 0.69, 0.70]）均表现最佳。CVD组C统计量为0.62 ~ 0.67，CVD+组C统计量为0.64 ~ 0.69。CVD的校准斜率（1表示完全校准）范围从0.38 （95% CI 0.37, 0.39）到0.74 (95% CI 0.72, 0.76)， CVD+的校准斜率范围从0.41 （95% CI 0.40, 0.42）到0.88 （95% CI 0.86, 0.90）。一个简单的重新校准过程大大提高了分数的性能，CVD的校准斜率现在在0.96到1.04之间。预测因子较多的评分并不优于预测因子较少的评分：对于CVD+， QRISK3（19个变量）的C统计量为0.68 (95% CI 0.68, 0.69)，而SCORE CVD（6个变量）的C统计量为0.69 （95% CI 0.69, 0.70）。糖尿病患者的评分并不比一般人群的评分有更好的区别：英国前瞻性糖尿病研究（UKPDS）评分明显低于CVD评分（p值<0.001）。CVD风险预测评分不能准确识别在10年随访中发生CVD事件的2型糖尿病患者。所有22个被评估的模型具有可比性和适度的判别能力。在线版本包含同行评审但未经编辑的补充材料，可在10.1007/s00125-021-05640-y获得。
We aimed to compare the performance of risk prediction scores for CVD (i.e., coronary heart disease and stroke), and a broader definition of CVD including atrial fibrillation and heart failure (CVD+), in individuals with type 2 diabetes. Scores were identified through a literature review and were included irrespective of the type of predicted cardiovascular outcome or the inclusion of individuals with type 2 diabetes. Performance was assessed in a contemporary, representative sample of 168,871 UK-based individuals with type 2 diabetes (age ≥18 years without pre-existing CVD+). Missing observations were addressed using multiple imputation. We evaluated 22 scores: 13 derived in the general population and nine in individuals with type 2 diabetes. The Systemic Coronary Risk Evaluation (SCORE) CVD rule derived in the general population performed best for both CVD (C statistic 0.67 [95% CI 0.67, 0.67]) and CVD+ (C statistic 0.69 [95% CI 0.69, 0.70]). The C statistic of the remaining scores ranged from 0.62 to 0.67 for CVD, and from 0.64 to 0.69 for CVD+. Calibration slopes (1 indicates perfect calibration) ranged from 0.38 (95% CI 0.37, 0.39) to 0.74 (95% CI 0.72, 0.76) for CVD, and from 0.41 (95% CI 0.40, 0.42) to 0.88 (95% CI 0.86, 0.90) for CVD+. A simple recalibration process considerably improved the performance of the scores, with calibration slopes now ranging between 0.96 and 1.04 for CVD. Scores with more predictors did not outperform scores with fewer predictors: for CVD+, QRISK3 (19 variables) had a C statistic of 0.68 (95% CI 0.68, 0.69), compared with SCORE CVD (six variables) which had a C statistic of 0.69 (95% CI 0.69, 0.70). Scores specific to individuals with diabetes did not discriminate better than scores derived in the general population: the UK Prospective Diabetes Study (UKPDS) scores performed significantly worse than SCORE CVD (p value <0.001). CVD risk prediction scores could not accurately identify individuals with type 2 diabetes who experienced a CVD event in the 10 years of follow-up. All 22 evaluated models had a comparable and modest discriminative ability. The online version contains peer-reviewed but unedited supplementary material available at 10.1007/s00125-021-05640-y.
登录
查看更多内容
影响因子：
5.7
作者：
Dorresteijn, Johannes A. N.;Visseren, Frank L. J.;van der Graaf, Yolanda
通讯作者：
van der Graaf, Yolanda
影响因子：
37.8
作者：
Ridker PM;Paynter NP;Rifai N;Gaziano JM;Cook NR
通讯作者：
Cook NR
影响因子：
3.4
作者：
Herrett, Emily;Thomas, Sara L.;Hall, Andrew J.
通讯作者：
Hall, Andrew J.
影响因子：
16.2
作者：
Donnan, Peter T.;Donnelly, Louise;Morris, Andrew D.
通讯作者：
Morris, Andrew D.
影响因子：
105.7
作者：
Hippisley-Cox, Julia;Coupland, Carol;Brindle, Peter
通讯作者：
Brindle, Peter