Derivation and external validation of a simple prediction model for the diagnosis of type 2 Diabetes Mellitus in the Brazilian urban population.
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Date
2009
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Abstract
A risk score model was developed based in a
population of 1,224 individuals from the general population
without known diabetes aging 35 years or more from an
urban Brazilian population sample in order to select individuals
who should be screened in subsequent testing and
improve the efficacy of public health assurance. External
validation was performed in a second, independent, population
from a different city ascertained through a similar
epidemiological protocol. The risk score was developed by
multiple logistic regression and model performance and
cutoff values were derived from a receiver operating characteristic
curve. Model’s capacity of predicting fasting
blood glucose levels was tested analyzing data from a 5-year
follow-up protocol conducted in the general population.
Items independently and significantly associated with diabetes
were age, BMI and known hypertension. Sensitivity,
specificity and proportion of further testing necessary for the
best cutoff value were 75.9, 66.9 and 37.2%, respectively.
External validation confirmed the model’s adequacy (AUC
equal to 0.72). Finally, model score was also capable of
predicting fasting blood glucose progression in non-diabetic
individuals in a 5-year follow-up period. In conclusion, this
simple diabetes risk score was able to identify individuals
with an increased likelihood of having diabetes and it can be
used to stratify subpopulations in which performing of
subsequent tests is necessary and probably cost-effective.
Description
Keywords
Diabetes prediction model, Risk score
Citation
SOUSA, A. G. P. de et al. Derivation and external validation of a simple prediction model for the diagnosis of type 2 Diabetes Mellitus in the Brazilian urban population. European Journal of Epidemiology, v. 24, p. 101-109, 2009. Disponível em: <http://link.springer.com/article/10.1007%2Fs10654-009-9314-2>. Acesso em: 20 jan. 2017.