Key result
A risk prediction model using routine clinical and laboratory variables predicted new-onset diabetes with a C-statistic of 0.757 in the development cohort and 0.643 in the external validation cohort.
Why the study?
Robust diabetes risk estimates in Asian patients with impaired glucose tolerance and coronary heart disease are lacking.
Can a risk prediction model using routinely available clinical variables accurately estimate the risk of developing type 2 diabetes in Chinese people with coronary heart disease and impaired glucose tolerance?
Cohort (n=4,193)
Can a risk prediction model using routinely available clinical variables accurately estimate the risk of developing type 2 diabetes in Chinese people with coronary heart disease and impaired glucose tolerance?
A risk prediction model incorporating routinely available clinical and laboratory variables can estimate the 5-year risk of developing type 2 diabetes in Chinese patients with coronary heart disease and impaired glucose tolerance.
May support risk stratification for diabetes prevention; extends routine-variable models to external validation settings.
AIMS: Robust diabetes risk estimates in Asian patients with impaired glucose tolerance (IGT) and coronary heart disease (CHD) are lacking. We developed a Chinese type 2 diabetes risk calculator using Acarbose Cardiovascular Evaluation (ACE) trial data. METHODS: There were 3105 placebo-treated ACE participants with requisite data for model development. Clinically relevant variables, and those showing nominal univariate association with new-onset diabetes (P < .10), were entered into BASIC (clinical variables only), EXTENDED (clinical variables plus routinely available laboratory results), and FULL (all candidate variables) logistic regression models. External validation was performed using the Luzhou prospective cohort of 1088 Chinese patients with IGT. RESULTS: Over median 5.0 years, 493 (15.9%) ACE participants developed diabetes. Lower age, higher body mass index, and use of corticosteroids or thiazide diuretics were associated with higher diabetes risk. C-statistics for the BASIC (using these variables), EXTENDED (adding male sex, fasting plasma glucose, 2-hour glucose, and HbA1c), and FULL models were 0.610, 0.757, and 0.761 respectively. The EXTENDED model predicted a lower 13.9% 5-year diabetes risk in the Luzhou cohort than observed (35.2%, 95% confidence interval 31.3%-39.5%, C-statistic 0.643). CONCLUSION: A risk prediction model using routinely available clinical variables can be used to estimate diabetes risk in Chinese people with CHD and IGT.
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Xu et al. (2021) conducted a cohort in impaired glucose tolerance and coronary heart disease (n=4,193). Risk prediction model was evaluated on new-onset diabetes (C-statistic 0.757). A risk prediction model using routine clinical and laboratory variables predicted new-onset diabetes with a C-statistic of 0.757 in the development cohort and 0.643 in the external validation cohort.
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