TY - JOUR
T1 - External validation of a rapid, non-invasive tool for periodontitis screening in a medical care setting
AU - Nijland, N.
AU - Overtoom, F.
AU - Gerdes, V. E.A.
AU - Verhulst, M. J.L.
AU - Su, N.
AU - Loos, B. G.
N1 - Funding Information: We thank the Amsterdam University Medical Center (Location AMC) for their hospitality and collaboration. Also, we thank ExamVision (Rotterdam, The Netherland), who provided us with a pair of LED-light system glasses. Publisher Copyright: © 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Medical care
KW - Periodontitis
KW - Questionnaire
KW - Screening
KW - Self-reported oral health
KW - Validation
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U2 - https://doi.org/10.1007/s00784-021-03952-2
DO - https://doi.org/10.1007/s00784-021-03952-2
M3 - Article
C2 - 33978832
SN - 1432-6981
VL - 25
SP - 6661
EP - 6669
JO - Clinical Oral Investigations
JF - Clinical Oral Investigations
IS - 12
ER -