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Predictive performance of established cardiovascular risk scores in the prediabetic population: external validation
Miaohong Li1,2, Yifen Lin1,2, Xiangbin Zhong1,2
1Cardiology Department, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan 2nd Road, Guangzhou 510080, China.
Insights
Existing cardiovascular disease (CVD) risk scores show limited accuracy for individuals with prediabetes. The PREDICT-1° Diabetes equation offers a potential, though imperfect, alternative for risk assessment in this population.
Area of Science:
- Cardiology
- Endocrinology
- Public Health
Background:
- Prediabetes is a metabolic state increasing cardiovascular disease (CVD) risk.
- Current guidelines lack specific CVD risk scoring recommendations for prediabetes.
- Accurate risk stratification is crucial for timely intervention.
Purpose of the Study:
- To systematically evaluate the performance of 11 CVD risk prediction models in a prediabetes cohort.
- To compare general population-based and diabetes-specific risk scores.
- To determine the clinical utility of existing models for prediabetes.
Main Methods:
- Utilized UK Biobank data from 56,831 individuals aged 40-69 with prediabetes.
- Validated 11 CVD risk scores for 5- or 10-year risk prediction.
- Assessed model discrimination (C-statistic) and calibration, alongside decision curve analysis.
Main Results:
- All 11 models demonstrated modest discrimination (C-statistics 0.647-0.680) in prediabetes.
- No significant difference in performance between general and diabetes-specific scores.
- The PREDICT-1° Diabetes equation showed the best performance and acceptable calibration post-recalibration.
Conclusions:
- Existing CVD risk scores, both general and Type 2 diabetes-specific, perform inadequately in prediabetes.
- The PREDICT-1° Diabetes equation may serve as a temporary solution.
- Development of tailored risk assessment tools for prediabetes is essential.
Aims:
Prediabetes is a highly heterogenous metabolic state with increased risk of cardiovascular disease (CVD). Current guidelines raised the necessity of CVD risk scoring for prediabetes without clear recommendations. Thus, this study aimed to systematically assess the performance of 11 models, including five general population-based and six diabetes-specific CVD risk scores, in prediabetes.
Methods And Results:
A cohort of individuals aged 40-69 years with prediabetes (HbA1c ≥ 5.7 and <6.5%) and without baseline CVD or known diabetes was identified from the UK Biobank, which was used to validate 11 prediction models for estimating 10- or 5-year risk of CVD. Model discrimination and calibration were evaluated by Harrell's C-statistic and calibration plots, respectively. We further performed decision curve analyses to assess the clinical usefulness.Overall, 56 831 prediabetic individuals were included, of which 4303 incident CVD events occurred within a median follow-up of 8.9 years. All the 11 risk scores assessed had modest C-statistics for discrimination ranging from 0.647 to 0.680 in prediabetes. Scores developed in the general population did not outperform those diabetes-specific models (C-statistics, 0.647-0.675 vs. 0.647-0.680), while the PREDICT-1° Diabetes equation developed for Type 2 diabetes performed best [0.680 (95% confidence interval, 0.672-0.689)]. The calibration plots suggested overall poor calibration except that the PREDICT-1° Diabetes equation calibrated well after recalibration. The decision curves generally indicated moderate clinical usefulness of each model, especially worse within high threshold probabilities.
Conclusion:
Neither risk stratification schemes for the general population nor those specific for Type 2 diabetes performed well in the prediabetic population. The PREDICT-1° Diabetes equation could be a substitute in the absence of better alternatives, rather than the general population-based scores. More precise and targeted risk assessment tools for this population remain to be established.
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