Prediction Model for High Glycated Hemoglobin Concentration among Ethnic Chinese in Taiwan

This study aimed to construct a prediction model to identify subjects with high glycated hemoglobin (HbA1c) levels by incorporating anthropometric, lifestyle, clinical, and biochemical information in a large cross-sectional ethnic Chinese population in Taiwan from a health checkup center. Methods: The prediction model was derived from multivariate logistic regression, and we evaluated the performance of the model in identifying the cases with high HbA1c levels (> = 7.0%). In total 17,773 participants (age > = 30 years) were recruited and 323 participants (1.8%) had high HbA1c levels. The study population was divided randomly into two parts, with 80% as the derivation data and 20% as the validation data. Results: The point-based clinical model, including age (maximal 8 points), sex (1 point), family history (3 points) , body mass index (2 points), waist circumference (4 points) , and systolic blood pressure (3 points) reached an area under the receiver operating characteristic curve (AUC) of 0 .723 (95% confidence interval, 0.677-0.769 ) in the validation data. Adding biochemical measures such as triglycerides and HDL cholesterol improved the prediction power (AUC, 0. 770 [0.723 - 0.817], P = < 0.001 compared with the clinical model). A cutoff point of 7 had a sensitivity of 0.76 to 0.96 and a specificity of 0 .39 to 0.63 for the prediction model. Conclusions: A prediction model was constructed for the prevalent risk of high HbA1c, which could be useful in identifying high risk subjects for diabetes among ethnic Chinese in Taiwan