Percorrer por autor "Maffia, Francesco"
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- Association of malocclusion with temporomandibular disorders:a cross-sectional studyPublication . Ângelo, David Faustino; Teixeira, Maria Cristina Faria; Maffia, Francesco; Sanz, David; Sarkis, Marcella; Marques, Rute; Mota, Beatriz; São João, Ricardo; Cardoso, Henrique JoséBackground/Objectives: Temporomandibular disorders (TMD) encompass a range of musculoskeletal and neuromuscular conditions affecting the temporomandibular joint (TMJ) and associated structures. This cross-sectional study, conducted in a Portuguese TMD department, aimed to assess the relationship between malocclusion and TMD severity. Methods: Data on demographic variables, TMD clinical symptoms, and malocclusion classes were collected using the EUROTMJ database. The Chi-square test (χ2) identified associations, with their intensity measured by Cramér’s V (φc). Results: The study included 1170 patients (932 females and 238 males), with a mean age of 41.73 ± 16.80 years. Most patients exhibited Angle Class I malocclusion (85.5%), followed by Angle Class II (13.5%) and Angle Class III (1.1%). Class II malocclusion was associated with increased TMD severity (p < 0.001), higher myalgia levels (p = 0.002), more frequent disc displacement without reduction (p = 0.002) and lower maximum mouth opening values (Class II: 38.13 ± 7.78 mm, Class I: 39.93 ± 8.67 mm). Significant associations were also found between malocclusion type and arthralgia (p = 0.021), mouth-opening limitation (p = 0.016), and TMJ crepitus (p = 0.017). In cases of malocclusion, the presence of oral signs of bruxism explained the degree of myalgia, disc displacement, and severity (p = 0.003; p = 0.048; p = 0.045). Conclusions: This study highlights that (1) the most common type of dental malocclusion in TMD patients was Class I; (2) Class II malocclusion was associated with increased TMD severity and oral signs of bruxism; and (3) Class III was rarely observed in TMD consultation. The findings suggest that bruxism behavior in cases of malocclusion may be significant in TMD.
- A predictive model for arthrogenous temporomandibular disorders based on clinical signs and symptomsPublication . Angelo, David Faustino; Cardoso, Henrique José; Geraldes, Carlos; São João, Ricardo; Maffia, Francesco; Sanz, David; Salvado, FranciscoThis study aimed to develop and internally evaluate a multivariable statistical model to identify arthrogenous temporomandibular disorders (TMD) using routinely collected clinical data. The model's performance was compared with the Fonseca Anamnestic Index (FAI) alone, using an imaging-based classification as the reference standard. This cross-sectional observational study included 1170 consecutive patients attending their first consultation at a tertiary TMD center between August 2019 and August 2024. Arthrogenous TMD was deter mined using combined clinical and imaging assessment according to the Dimitroulis classification. Clinical variables, including age, maximum mouth opening (MMO), individual FAI items, and joint-related complaints, were extracted from the EUROTMJ database. Generalized additive models (GAMs) were used to develop pre dictive models. Performance was assessed using receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, and specificity in training (60%) and test (40%) datasets. The final Fonseca–Dimitroulis (FD-Class) model incorporated age, MMO, selected FAI items (Q2, Q6, Q7), crepitus, and temporomandibular joint (TMJ) locking. The model achieved an AUC of 0.761 in the training dataset and 0.742 in the test dataset, outperforming the FAI alone (AUC = 0.662). This model may support the early identification of arthrogenous TMJ disease and improve decision-making regarding referral for advanced imaging in maxillofacial practice.This study aimed to develop and internally evaluate a multivariable statistical model to identify arthrogenous temporomandibular disorders (TMD) using routinely collected clinical data. The model's performance was compared with the Fonseca Anamnestic Index (FAI) alone, using an imaging-based classification as the reference standard. This cross-sectional observational study included 1170 consecutive patients attending their first consultation at a tertiary TMD center between August 2019 and August 2024. Arthrogenous TMD was deter mined using combined clinical and imaging assessment according to the Dimitroulis classification. Clinical variables, including age, maximum mouth opening (MMO), individual FAI items, and joint-related complaints, were extracted from the EUROTMJ database. Generalized additive models (GAMs) were used to develop pre dictive models. Performance was assessed using receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, and specificity in training (60%) and test (40%) datasets. The final Fonseca–Dimitroulis (FD-Class) model incorporated age, MMO, selected FAI items (Q2, Q6, Q7), crepitus, and temporomandibular joint (TMJ) locking. The model achieved an AUC of 0.761 in the training dataset and 0.742 in the test dataset, outperforming the FAI alone (AUC = 0.662). This model may support the early identification of arthrogenous TMJ disease and improve decision-making regarding referral for advanced imaging in maxillofacial practice.