Santos, F.; Beato, F.; Machado, V.; Proença, L.; Mendes, J.J.; Botelho, J. Early Tooth Loss after Periodontal Diagnosis: Development and Validation of a Clinical Decision Model. Int. J. Environ. Res. Public Health2021, 18, 1363.
Santos, F.; Beato, F.; Machado, V.; Proença, L.; Mendes, J.J.; Botelho, J. Early Tooth Loss after Periodontal Diagnosis: Development and Validation of a Clinical Decision Model. Int. J. Environ. Res. Public Health 2021, 18, 1363.
Santos, F.; Beato, F.; Machado, V.; Proença, L.; Mendes, J.J.; Botelho, J. Early Tooth Loss after Periodontal Diagnosis: Development and Validation of a Clinical Decision Model. Int. J. Environ. Res. Public Health2021, 18, 1363.
Santos, F.; Beato, F.; Machado, V.; Proença, L.; Mendes, J.J.; Botelho, J. Early Tooth Loss after Periodontal Diagnosis: Development and Validation of a Clinical Decision Model. Int. J. Environ. Res. Public Health 2021, 18, 1363.
Abstract
The aim of this study was to develop and validate a predictive early tooth loss multivariable model for periodontitis patients before periodontal treatment. A total of 544 patients seeking periodontal care at a university dental hospital were enrolled in the study. Teeth extracted after periodontal diagnosis and due to periodontal reasons were recorded. Clinical and sociodemographic variables were analyzed, considering the risk of short-term tooth loss. This study followed the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines for development and validation, with two cohorts considered as follows: 455 patients in the development phase and 99 in the validation phase. As a result, it was possible to compute a predictive model based on tooth type and clinical attachment loss. The model explained 25.3% of the total variability and correctly ranked 98.9% of the cases. The final reduced model area under the curve (AUC) was 0.809 (95% Confidence Interval (95% CI): 0.629 - 0.989) for the validation sample and 0.920 (95% CI: 0.891 - 0.950) for the development cohort. The established model presented adequate prediction potential of early tooth loss due to periodontitis. This model may have clinical and epidemiologic relevance towards the prediction of tooth loss burden.
Keywords
periodontal disease; periodontitis; early tooth loss; predictive model; risk factors; oral health; public health; epidemiology
Subject
Medicine and Pharmacology, Immunology and Allergy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.