Clinical Prediction of Chronic Periodontitis
1 Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
2 Department of Periodontology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
3 Center of Excellence in Periodontal Disease and Dental Implant, Chulalongkorn University, Bangkok, Thailand
4 Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
Abstract
Diagnosis of chronic periodontitis is a time and resource-consuming procedure that require exhaustive periodontal probing for each present tooth. Risk prediction models are developed to remove the need for such parameter. Those models are commonly cross-sectional logistic regression models, enhanced by applying demographic and risk factors data. With present study, we further augment the performance by applying longitudinal data to develop the model. Considering population average and subject specific effects from longitudinal data, mixed effects model result in higher performance. Sensitivity and specificity are 89.5% and 92.5% while performing upon validation data. The model is 91.5% accurate with 0.91 discriminative power. Positive predictive value and negative predictive value are 86.2% and 94.4%. The positive likelihood ratio of the model is 11.9.
