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Using "Real-World Data" to Study Cleft Lip/Palate Care: An Exploration of Speech Outcomes from a Multi-Center US
Kristina Dunworth1, Banafsheh Sharif-Askary1, Lynn Grames2
1Department of Surgery, Division of Plastic, Maxillofacial, and Oral Surgery, Duke University School of Medicine, Durham, NC, USA.
Summary
A cleft registry effectively captures real-world speech outcomes, showing variations by cleft type and age. This data supports cleft research and highlights differences in speech development for specific cleft phenotypes.
Area of Science:
- Craniofacial anomalies
- Speech pathology
- Health informatics
Background:
- Cleft lip and palate (CLP) treatment requires standardized outcome measurement.
- Learning health networks can aggregate real-world data for research.
- Understanding variations in CLP outcomes is crucial for improving care.
Purpose of the Study:
- To evaluate a cleft-specific registry's capacity to identify speech outcome variations.
- To compare outcomes across different cleft phenotypes, age groups, and treatment centers.
- To assess the registry data's coherence with existing research.
Main Methods:
- Cross-sectional analysis of prospectively collected data (2019-2022).
- Data from 714 children/adolescents with non-syndromic CLP across six treatment centers.
- Speech outcomes measured included Percent Consonants Correct (PCC), Velopharyngeal Competence (VPC-R), Intelligibility in Context Scale (ICS), and CLEFT-Q™ surveys.
Main Results:
- Older children (12 years) generally showed better speech outcomes (PCC, VPC, Speech Function) than younger peers.
- Patients with bilateral cleft lip, alveolus, and palate exhibited lower PCC and VPC at age 5.
- Intelligibility (ICS) and patient-reported speech function/distress showed consistency across phenotypes.
Conclusions:
- The cleft registry effectively provides real-world data for cleft research.
- The registry can describe variations in speech outcomes based on cleft type and age.
- Future research will focus on hypothesis-driven studies and causal inference using registry data.

