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Description

Objective: To build and validate a practical, orthodontics-specific adverse event taxonomy and severity triage framework using publicly available regulatory narratives, enabling rapid surveillance of high-risk event patterns across clear aligners and common orthodontic appliances.
Methods: A retrospective computational audit was conducted using U.S. FDA MAUDE medical device report narratives related to orthodontic appliances, with a focus on clear aligners (including Invisalign and direct-to-consumer sequential aligners). Narrative reports were normalized and grouped into clinically interpretable event families (airway compromise, hypersensitivity/allergic reactions, ingestion/aspiration, soft-tissue injury, periodontal/tooth injury, and occlusal/bite changes). A lightweight natural-language processing pipeline (keyword+pattern rules with clinician-informed adjudication) was used to assign event family labels and a 3-tier severity tag (non-serious, serious, life-threatening) consistent with report descriptors. Aggregate signal profiles were summarized at the device-category level.
Results: In MAUDE reports referencing a widely used clear aligner system reports include a clinically meaningful fraction of serious events. For clear aligners, 173 MAUDE device reports were identified, 169 designated as adverse events, and 45 categorized as serious or life-threatening, with airway-related symptoms frequently represented (e.g., difficulty breathing reported in 56 events). For broader orthodontic appliances over a 5-year window, 175 adverse event reports were identified in MAUDE, with most being mandatory reports, supporting the feasibility of systematic regulatory surveillance in orthodontics. For direct to consumer sequential aligners, 104 MAUDE reports were analyzed in a dedicated study; injuries constituted the dominant adverse-event class (86.5%), and reporting was largely patient-driven (about 60% patient-submitted), highlighting a distinct safety-reporting signature compared with clinician-mediated pathways.
Conclusion: A MAUDE-derived orthodontic event taxonomy supports rapid, publishable safety surveillance without imaging or biomechanics. Available MAUDE evidence suggests that (i) airway/allergy-related narratives represent key “high-acuity” signals for supervised clear aligner systems, while (ii) injury/occlusal complaint clusters dominate in DTC aligner reporting, motivating targeted patient counseling, device design risk controls, and standardized adverse event documentation in orthodontic practice.

Disciplines

Orthodontics and Orthodontology

Keywords

Orthodontics, clear aligners, MAUDE, medical device reports, adverse events, pharmacovigilance, natural language processing One sentence summary: Public regulatory narratives can be mined within days to produce an orthodontics-specific adverse event taxonomy that highlights device-category specific safety signal patterns for clear aligners and orthodontic appliances

Document Type

Poster

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Regulatory Signal Mining of Orthodontic Device Adverse Events: A MAUDE-Derived Taxonomy and Severity Triage for Clear Aligners and Fixed Appliances

Objective: To build and validate a practical, orthodontics-specific adverse event taxonomy and severity triage framework using publicly available regulatory narratives, enabling rapid surveillance of high-risk event patterns across clear aligners and common orthodontic appliances.
Methods: A retrospective computational audit was conducted using U.S. FDA MAUDE medical device report narratives related to orthodontic appliances, with a focus on clear aligners (including Invisalign and direct-to-consumer sequential aligners). Narrative reports were normalized and grouped into clinically interpretable event families (airway compromise, hypersensitivity/allergic reactions, ingestion/aspiration, soft-tissue injury, periodontal/tooth injury, and occlusal/bite changes). A lightweight natural-language processing pipeline (keyword+pattern rules with clinician-informed adjudication) was used to assign event family labels and a 3-tier severity tag (non-serious, serious, life-threatening) consistent with report descriptors. Aggregate signal profiles were summarized at the device-category level.
Results: In MAUDE reports referencing a widely used clear aligner system reports include a clinically meaningful fraction of serious events. For clear aligners, 173 MAUDE device reports were identified, 169 designated as adverse events, and 45 categorized as serious or life-threatening, with airway-related symptoms frequently represented (e.g., difficulty breathing reported in 56 events). For broader orthodontic appliances over a 5-year window, 175 adverse event reports were identified in MAUDE, with most being mandatory reports, supporting the feasibility of systematic regulatory surveillance in orthodontics. For direct to consumer sequential aligners, 104 MAUDE reports were analyzed in a dedicated study; injuries constituted the dominant adverse-event class (86.5%), and reporting was largely patient-driven (about 60% patient-submitted), highlighting a distinct safety-reporting signature compared with clinician-mediated pathways.
Conclusion: A MAUDE-derived orthodontic event taxonomy supports rapid, publishable safety surveillance without imaging or biomechanics. Available MAUDE evidence suggests that (i) airway/allergy-related narratives represent key “high-acuity” signals for supervised clear aligner systems, while (ii) injury/occlusal complaint clusters dominate in DTC aligner reporting, motivating targeted patient counseling, device design risk controls, and standardized adverse event documentation in orthodontic practice.