RT Journal Article
T1 Development of a new tool for predicting the behavior of individuals with intellectual disability in the dental office: A pilot study
A1 Varela Aneiros, Iván
A1 Fernández Feijoo, Javier
A1 García Mato, Eliane
A1 Diniz Freitas, Márcio
A1 Martínez, Isabel
A1 Roca Pardiñas, Javier
A1 Diz Dios, Pedro
A1 Limeres Posse, Jacobo
K1 Disability
K1 Behavior
K1 Dentistry
K1 Dental management
K1 General anesthesia
K1 Predictive models
AB Background: The dental treatment of individuals with intellectual disability can represent a considerable professional challenge. Objective: To develop a model for predicting the behavior of patients with intellectual disability in the dental office. Methods: The study group comprised 250 patients with Down syndrome (DS), autism spectrum disorder (ASD), cerebral palsy (CP), idiopathic cognitive impairment or rare disorders. We collected their demographic, medical, social and behavioral information and identified potential predictors (chi-squared test). We developed stratified models (Akaike information criterion) to anticipate the patients'behavior during intraoral examinations and to discern whether the dental treatment should be performed under general anesthesia. These models were validated in a new study group consisting of 80 patients. Goodness of fit was quantified with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the receiver operating characteristic curve (AUC). We developed a mathematical algorithm for executing the models and developed software for its practical implementation (PREdictors of BEhavior in Dentistry, “PREBED”).Results: For patients with DS, ASD and CP, the model predicting the need for physical restraint during examination achieved a PPV of 0.90, 0.85 and 1.00, respectively, and an NPV of 0.66, 0.76 and 1.00, respectively. The model predicting the need for performing treatment under general anesthesia achieved a PPV of 0.63, 1.00 and 1.00, respectively, and an NPV of 1.00, 1.00 and 0.73, respectively. However, when validating the stratified models, the percentage of poorly classified individuals (false negatives + false positives) ranged from 24% to 46.6%. Conclusions: The results of the PREBED tool open the door to establishing new models implementing other potentially predictive variables.
PB Elsevier
SN 1936-6574
YR 2022
FD 2022
LK http://hdl.handle.net/10347/32727
UL http://hdl.handle.net/10347/32727
LA eng
NO Disability and Health Journal, Volume 15, Issue 2, April 2022, 101229. https://doi.org/10.1016/j.dhjo.2021.101229
DS Minerva
RD 27 abr 2026