External Validation of a Periodontal Prediction Model for Identification of Diabetes among Saudi Adults
Arwa A Talakey, Francis Hughes, Eduardo Bernabé
Citation Information :
Talakey AA, Hughes F, Bernabé E. External Validation of a Periodontal Prediction Model for Identification of Diabetes among Saudi Adults. J Contemp Dent Pract 2020; 21 (10):1176-1181.
Aim and objective: To externally validate the performance of a novel periodontal prediction model (PPM) for identification of diabetes among Saudi adults. Materials and methods: The study was carried out among 150 adults attending primary care clinics in Riyadh (Saudi Arabia). The study adopted a temporal external validation approach, where the performance of the PPM was evaluated in the same location as the development study, but at a later time to allow for some variation between samples. A case-control approach was adopted, where diabetes status was first ascertained, followed by the completion of the Finnish Diabetes Risk Score (FINDRISC), Canadian Diabetes Risk (CANRISK) tools, and periodontal examinations. Results: The area under the curve (AUC) of the PPM (based on the number of missing teeth, the proportion of sites with pocket probing depth ≥6 mm, and mean pocket probing depth) was 0.514 (95% CI: 0.385, 0.642). The FINDRISC and CANRISK tools had AUC values of 0.871 (95% CI: 0.811–0.931) and 0.927 (95% CI: 0.884–0.971), respectively. The addition of the PPM did not improve the AUC of FINDRISC (p = 0.479) or CANRISK (p = 0.920). The decision curve analysis showed that there was no clinical benefit in adding the PPM to either tool. The PPM was updated with an overall adjustment factor for all existing predictors and three more periodontal measures. Conclusion: In an external sample, the PPM had poor performance for identification of diabetes and no added value when combined with FINDRISC and CANRISK. The performance of the PPM improved after recalibration and extension. Clinical significance: The results underscore the value of externally validating prediction models before applying them in clinical dental practice.
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