The aforementioned research group has successfully developed and internally validated a new prognostic tool in the field of periodontology using the University of Michigan cohort: the nomogram. This tool is based on a multivariate model that incorporates the staging and grading components of the 2018 periodontitis classification. Its goal is to guide clinicians in the early detection of patients at high risk for periodontitis-related tooth loss.
A nomogram is a user-friendly graphical interface that, in this case, evaluates individual risk factors for periodontal tooth loss in an additive manner. It comprises upper and lower components. The upper portion contains the "Score" scale used to compute the weight of each variable (stage, grade, extent, and age). The lower section calculates the aggregate "total score" for the patient-level risk, which is categorized as 'Low' if the score is below the cut-off of 12.5, or 'High' if it is above.
In this context, 'Low risk' or 'High risk' specifically indicate that the patient has a risk of losing ≤ 1 tooth or ≥2 teeth due to periodontal reasons over a 10-year follow-up period. This model applies only to individuals who attend maintenance visits at least once a year, as it was built on a cohort of compliant patients. For more information on the development, specification, and performance of the nomogram model, please visit: Wiley Online Library.
In a subsequent project by the same research group, the performance of the original nomogram was tested and successfully externally validated across four new cohorts (University of Pittsburgh, University of Louisville, Tufts University, and King’s College London), with their datasets analyzed both individually and collectively. In both cases, a stable and robust discriminative ability of the model was demonstrated, indicating consistent predictive power comparable to that observed during its original development phase.
Differences in demographic characteristics and clinical factors among patients from different centers could, however, compromise the model's performance. To address this issue, a recalibration process was undertaken, which enhanced the model's predictive accuracy and the reliability of its application across diverse populations worldwide.
To perform predictions for your patients using the updated nomogram, just click on the button at the bottom of the page!!