External validation of a rapid, non-invasive tool for periodontitis screening in a medical care setting.

Autor: Nijland N; Department of Periodontology, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Gustav Mahlerlaan 3004, 1081, LA, Amsterdam, The Netherlands. ninanijland@hotmail.com., Overtoom F; Department of Periodontology, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Gustav Mahlerlaan 3004, 1081, LA, Amsterdam, The Netherlands., Gerdes VEA; Department of Vascular Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands., Verhulst MJL; Department of Periodontology, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Gustav Mahlerlaan 3004, 1081, LA, Amsterdam, The Netherlands.; Sunstar Suisse, Etoy, Switzerland., Su N; Department of Oral Public Health, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit, Amsterdam, The Netherlands., Loos BG; Department of Periodontology, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Gustav Mahlerlaan 3004, 1081, LA, Amsterdam, The Netherlands.
Jazyk: angličtina
Zdroj: Clinical oral investigations [Clin Oral Investig] 2021 Dec; Vol. 25 (12), pp. 6661-6669. Date of Electronic Publication: 2021 May 12.
DOI: 10.1007/s00784-021-03952-2
Abstrakt: Objectives: Medical professionals should advise their patients to visit a dentist if necessary. Due to the lack of time and knowledge, screening for periodontitis is often not done. To alleviate this problem, a screening model for total (own teeth/gum health, gum treatment, loose teeth, mouthwash use, and age)/severe periodontitis (gum treatment, loose teeth, tooth appearance, mouthwash use, age, and sex) in a medical care setting was developed in the Academic Center of Dentistry Amsterdam (ACTA) [1]. The purpose of the present study was to externally validate this tool in an outpatient medical setting.
Materials and Methods: Patients were requited in an outpatient medical setting as the validation cohort. The self-reported oral health questionnaire was conducted, demographic data were collected, and periodontal examination was performed. Algorithm discrimination was expressed as the area under the receiver operating characteristic curve (AUROCC). Sensitivity, specificity, and positive and negative predictive values were calculated. Calibration plots were made.
Results: For predicting total periodontitis, the AUROCC was 0.59 with a sensitivity of 49% and specificity of 68%. The PPV was 57% and the NPV scored 55%. For predicting severe periodontitis, the AUROCC was 0.73 with a sensitivity of 71% and specificity of 63%. The PPV was 39% and the NPV 87%.
Conclusions: The performance of the algorithm for severe periodontitis is found to be sufficient in the current medical study population. Further external validation of periodontitis algorithms in non-dental school populations is recommended.
Clinical Relevance: Because general physicians are obligated to screen patients for periodontitis, it is our general goal that they can use a prediction model in medical settings without an oral examination.
(© 2021. The Author(s).)
Databáze: MEDLINE
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