Broader Evaluation of TMD Treatment Efficacy and Response (BETTER TMD)
openNIDCR - National Institute of Dental and Craniofacial Research
PROJECT SUMMARY/ ABSTRACT (Project 1 BETTER TMD)
C-TMD IMPACT Research Project 1 refers to Broader Evaluation of TMD Treatment Efficacy & Response
(BETTER-TMD) and will address the misdiagnosis and suboptimal treatment decision gaps in the TMDs. The
overall goal of this project is to define and predict which existing treatments are likely to be most effective for
specific patients from a whole-person perspective, considering their symptoms and psychosocial context. The
proposed study leverages a custom-developed app, MyDocNote, to collect real-world, structured, patientreported
treatment outcome data. These outcomes will be paired with de-identified, highly structured data on
signs, symptoms, and diagnosis. Our preliminary studies have demonstrated the dinical utility of machine
learning (ML) methodology to create a TMD orofacial pain {TMD-OFP) predictive diagnostic algorithm using
data collected from 1,020 patients seen at the USC TMD-OFP clinic. We also showed the potential
effectiveness of an algorithm to predict various TMD-OFP diagnoses based on clinical signs and symptoms.
For this U54 phase, we propose to scale up the pilot work described above and add longitudinal treatment
outcome data to our dataset as we seek to develop additional algorithms to predict optimal, individualized
treatment decisions. This scale-up will be achieved by expanding our data collection to 19 sites across the US,
including 4 university medical centers and 15 clinical private practice sites. All collected signs and symptoms
data from these patients will be converted into a structured database format and then made available through
the FaceBase Hub with appropriate human subject protections, so we and other interested researchers can
conduct further analyses. We project to collect data from up to 1,000 patients per year, for a target total of
5,000 over the 5-year performance period. Our guiding hypothesis is that collecting and analyzing a large,
longitudinal, highly structured, whole-person dataset associating clinical signs and symptoms with treatment
outcomes will lead to algorithms that can accurately stratify patients and identify the best individualized
treatments. To test this hypothesis, three specific aims are proposed. Aim 1 will establish a set of common
data elements {CDEs) to characterize the phenotypic traits and treatment outcomes of TMD-OFP patients. Aim
2 will collect a large longitudinal, structured dataset of patient treatment outcomes using our mobile app,
MyDocNote. Aim 3 will analyze patient data to improve diagnosis and treatment decision-making for TMDOFP.
Successful completion of these Specific Aims will deliver: 1) a large-scale, longitudinal, structured, and
whole-person database of TMD-OFP signs, symptoms, and treatment outcomes; 2) decision-making
algorithms for diagnosis and treatment based on data analyses including ML methodology. Ultimately, these
can form the basis of a real-time point-of-care system to support clinicians in diagnosing TMDs and planning
treatment.
Up to $897K
health research