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.
Development of a nomogram for the prediction of periodontal tooth loss using the staging and grading system: A long-term cohort study
Troiano G.;Lo Russo L.;
2020-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.