Development of a nomogram for the prediction of periodontal tooth loss using the staging and grading system: A long‐term cohort study. (28th September 2020)

• Record Type: Journal Article
• Authors: Ravidà, Andrea; Troiano, Giuseppe; Qazi, Musa; Saleh, Muhammad H.A.; Lo Russo, Lucio; Greenwell, Henry; Giannobile, William V.; Wang, Hom‐Lay
• Is Part Of: Journal of clinical periodontology. Volume 47:Number 11(2020)
• Page Start: 1362
• Page End: 1370
• Publication Date: 2020-09-28
• Subjects: disease risk -- patient stratification -- periodontitis -- supportive periodontal therapy -- tooth loss; Periodontics -- Periodicals; 617.6
• DOI: 10.1111/jcpe.13362
• Languages: English
• ISSNs: 0303-6979

Abstract

Abstract: Aim: To develop and internally validate a nomogram built on a multivariate prediction model including parameters from the new classification of periodontal diseases, able to predict, at baseline, the occurrence of tooth loss due to periodontal reason (TLP). Materials and Methods: A total of 315 individuals diagnosed with periodontal disease and receiving a minimum of one annual supportive periodontal therapy visit were included in the study. Patients were staged and graded based upon baseline data. The population was divided into a development (254 patients) and a validation (61 patients) cohort to allow subsequent temporal validation of the model. According to the TLP at the 10‐year follow‐up, patients were categorized as "low tooth loss" (≤ 1 TLP) or "high tooth loss" (≥ 2 TLP). Bootstrap internal validation was performed on the whole data set to calculate an optimism‐corrected estimate of performance. Results: The generated nomogram showed a strong predictive capability (AUC = 0.81) and good calibration with an intercept = 0 and slope = 1. These findings were confirmed by internal validation using bootstrapping (average bootstrap AUC = 0.83). Conclusions: The clinical implementation of the present nomogram guides the prediction of patients with high risk of disease progression and subsequent tooth loss for personalized care.