Abstract
Aims/hypothesisWe 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.MethodsScores 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.ResultsWe 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).Conclusions/interpretationCVD 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.Graphical abstract
Highlights
CVD treatment initiation and intensification in clinical practice are guided by risk prediction algorithms
The scores that included a proportion of individuals with diabetes typically included type 2 diabetes as a predictor, but did not include diabetes-specific risk factors such as diabetes duration and glycaemic status
We validated 22 cardiovascular risk scores for their ability to predict a range of macrovascular endpoints in a cohort of 168,871 people with type 2 diabetes
Summary
CVD treatment initiation and intensification in clinical practice are guided by risk prediction algorithms. The European Society of Cardiology (ESC) does not recommend a specific CVD risk prediction tool, and instead stratifies individuals into three categories based on risk factors including: presence of target organ damage, number of risk factors, diabetes duration and age [2]. With over 300 published CVD risk prediction tools [5], many of which have not been validated in individuals with type 2 diabetes, nor directly compared within the same patient population, it is unclear which CVD score performs best in people with diabetes. Quite apart from the greater CVD risk, even at a given level of individual risk factors, it is evident that the initial presentation of CVD in individuals with diabetes differs from that of the general population, with greater representation of heart failure (HF) and of peripheral artery disease (PAD), while haemorrhagic strokes are less frequent [9]. Many designed for people with diabetes, have focused largely on the prediction of CHD and stroke only
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