Summary
This study developed a clinical screening model to identify undiagnosed diabetes mellitus and prediabetes in patients with periodontitis presenting to dental settings. Using patient socio-demographic, health, and periodontal characteristics from 204 adult patients (17% with diabetes, 47% with prediabetes), multinomial logistic regression identified seven significant predictors. The resulting model demonstrated moderate-to-good discrimination (AUC 0.67–0.80) and acceptable calibration, suggesting utility as a reliable chairside screening tool for diabetic status in dental populations.
UK applicability
This screening model, developed in a Dutch dental clinic setting, may be applicable to UK dental practice as a point-of-care screening approach for undiagnosed diabetes and prediabetes. The model's performance and clinical utility would benefit from external validation in UK dental populations before routine implementation into NHS dental care pathways.
Key measures
Area under the curve (AUC) values (0.67–0.80); predictive values for ruling in diabetes (0.42) and prediabetes (0.11); predictive values for ruling out diabetes (0.05) and prediabetes (0.17); calibration and discrimination performance
Outcomes reported
The study developed and internally validated a multinomial logistic regression screening model to predict diabetic status (no diabetes, prediabetes, or diabetes) in patients with periodontitis attending dental settings. The model identified predictors including age, BMI, European background, cholesterol levels, previous periodontal treatment, tooth mobility, and gingival recession.
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