Development of a new tool for predicting the behavior of individuals with intellectual disability in the dental office: A pilot study.
Behavior
Dental management
Dentistry
Disability
General anesthesia
Predictive models
Journal
Disability and health journal
ISSN: 1876-7583
Titre abrégé: Disabil Health J
Pays: United States
ID NLM: 101306633
Informations de publication
Date de publication:
04 2022
04 2022
Historique:
received:
15
07
2021
revised:
25
09
2021
accepted:
29
10
2021
pubmed:
16
11
2021
medline:
7
4
2022
entrez:
15
11
2021
Statut:
ppublish
Résumé
The dental treatment of individuals with intellectual disability can represent a considerable professional challenge. To develop a model for predicting the behavior of patients with intellectual disability in the dental office. 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"). 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%. The results of the PREBED tool open the door to establishing new models implementing other potentially predictive variables.
Sections du résumé
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.
Identifiants
pubmed: 34776386
pii: S1936-6574(21)00202-8
doi: 10.1016/j.dhjo.2021.101229
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
101229Informations de copyright
Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Conflicts of interest The authors declare no potential conflicts of interest with respect to the authorship and/or publication of this article.