Development of a nomogram for identifying periodontitis cases in Denmark

Area of Science:

• Periodontology
• Public Health
• Biostatistics

Background:

• Self-reported health outcomes are crucial but require validation with clinical data.
• Identifying periodontitis cases using accessible tools remains a challenge.
• Nomograms combining sociodemographic and self-reported data for periodontitis identification have not been previously validated.

Purpose of the Study:

• To develop and validate clinical applicable nomograms for identifying periodontitis cases.
• To combine sociodemographic and self-reported information for periodontitis risk assessment.
• To evaluate the discriminatory capability and calibration of the developed nomograms.

Main Methods:

• Collected clinical and self-reported periodontitis data, along with sociodemographic information, from 197 adults.
• Developed Akaike information criterion (AIC) models to identify periodontitis.
• Constructed nomograms based on AIC model regression coefficients and evaluated them using receiver-operating characteristic (ROC) curves and decision curve analysis.

Main Results:

• Smoking, poor/fair self-rated oral health, previous periodontal treatment, and tooth loss significantly predicted moderate/severe periodontitis.
• Age, low educational level, poor/fair self-rated oral health, and previous periodontal treatment predicted any periodontitis.
• Both nomograms exhibited excellent discriminatory capability (AUCs of 0.83 and 0.81) and good calibration.

Conclusions:

• The developed nomograms effectively identify periodontitis among adults.
• These tools can aid in the early detection and management of periodontitis in clinical settings.
• The nomograms offer a valuable approach to integrating patient-reported and demographic data for periodontitis risk assessment.