Determination of the Stage of Periodontitis with 20 ng/mL Cut-Off aMMP-8 Mouth Rinse Test and Polynomial Functions in a Mobile Application
<b>Background:</b> We propose a framework for determining the stage of periodontitis in a personalized medicine context, building on our previously developed model for periodontitis detection. In this study, we improved the earlier model by incorporating additional components to form a c...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/15/11/1411 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | <b>Background:</b> We propose a framework for determining the stage of periodontitis in a personalized medicine context, building on our previously developed model for periodontitis detection. In this study, we improved the earlier model by incorporating additional components to form a comprehensive system for identifying both the presence and stage of periodontitis. Central to the home-use application is an active-matrix metalloproteinase-8 (aMMP-8) mouth rinse test (cut-off: 20 ng/mL), integrated with software delivered via a mobile application. <b>Methods:</b> First, using all the data, we modeled a single polynomial function to distinguish healthy and stage I periodontitis patients from stage II and III patients. Second, we used an already published periodontitis detection function to separate stage I patients from healthy patients. Third, one more function was created that divided stage II and III patients from each other. All functions were modeled by multiple logistic regression analysis from the patient data, which consisted of 149 adult patients visiting dental offices in Thessaloniki, Greece. <b>Results:</b> The complete model demonstrated a sensitivity of 95.8% (95% CI: 92.1–99.4%) and a specificity of 71.0% (95% CI: 55.0–86.9%) for detecting periodontitis. Among those identified with periodontitis, the correct stage was determined in 61.1% of cases, with stage-specific accuracies of 64.3% for stage I, 60.5% for stage II, and 60.9% for stage III. All testing was performed on patient data with which the complete model was formed. <b>Conclusions:</b> The results of this study showed that with sufficient data and using multiple logistic regression analysis, a model can be created to simultaneously identify the presence and stage of periodontitis. Overall, in the complete model generated, a mouth rinse aMMP-8 test result with a cut-off value of 20 ng/mL, Visible Plaque Index (VPI) and information of patient’s teeth number present were found to be important factors to determine the stage of periodontitis in a personalized medicine manner for everyone to use. |
|---|---|
| ISSN: | 2075-4418 |