Sequential Modeling for Prediction of Periodontal Diseases: an intra-Collaborative Practice-based Research study (ICPRS) - SUMMARY
The
Collaborative
ICPRS
clinically-oriented
predoctoral/postdoctoral
administered
development
current proposal titled “Sequential Modeling for Prediction of Periodontal Disea ses: An I ntra-
Practice-Based Research Study ( ICPRS)” is developed in response to RFA-DE-23-012 . The
will seek to develop various tasks in 4 component sections. In Component 1 , we seek to train 10
faculty from Temple University Kornberg School of Dentistry (TUKSoD) nd ten
students in Applied Clinica l Research by offering a graduate certificate program
by the College of Public Health (CPH) on facilitating skills
in applied patient-oriented research
a
. The 15-credit certificate will focus
. In Component 2, we aim to strengthen collaborations
between TUKSoD, CPH and the Medical School for faculty and student training, and overall research support
for the execution of Component 4 of this proposal. Our proposed project was collaboratively developed by
individuals with backgrounds in Periodontology, General and Oral Epidemiology, Oral Microbiome, Health
Informatics, Biostatistics and Public Health, and Physicians who will assist in extracting medical metrics relevant
to management of periodontal diseases (PD) from real-time medical records in the Pennsylvania Health Share
Exchange (PA HSX). In Component 3, we aim to expand clinically-oriented faculty participation in grant funded
research activities, in the annual research day, and in national research meetings by increasing the number of
faculty-student mentoring partnerships to conduct small-scale research projects. Finally, in Component 4, we
will develop longitudinal predictive models of PD incidence and progression using machine learning approaches
and microbiome/metagenomic sequencing strategies. This component has the following aims: Aim1 seeks to
develop clinical decision tools that utilize matched medical-dental patient datasets from TUKSoD and the PA
HSX EHR records. Aim1a will focus on developing a Medical Continuity of Care Record (CCR) that will provide
patients' up-to-date medical health metrics that can be used in the provision of care at the dental school. Using
dental patient's matched records, Aim1b will build an AI Empowered Prediction Model for PD. Aim2 will build
on findings from Aim1b to assess the potential of the subgingival and salivary microbiomes as additional
predictors of PD incidence and progression. Aim 2a will validate a dysbiosis index, applied to both subgingival
plaque and saliva samples, as a predictor of PD in a cohort study, while Aim2b will identify potential high
resolution, salivary/plaque metagenomic biomarkers of PD progression via a nested case-control study. The
proposed application builds on prior research conducted at TUKSoD and is directed towards increasing
participation of clinically-oriented faculty and pre/postdoctoral dental students in the school's research activities,
coupled with training in Applied Clinical Research including health informatics, opening a new pathway to further
research in dental education as well as promote faculty professional development, expand the research
academic workforce, and provide opportunities to conduct student-mentored practice-based research.
