Development research for practical application of periodontal disease risk diagnosis algorithm by saliva test

Practical Application Research of Periodontal Disease Risk Diagnosis Algorithm by Salivary Component Analysis - Practical Application Research of Periodontal Disease Risk Diagnostic Algorithm

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000050150

Enrollment

100

Registered

2023-05-31

Start date

2023-06-02

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Periodontal disease

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: (1) Diseases and conditions: Patients who meet the following criteria for periodontal disease Periodontal disease: Patients with periodontal disease with at least one periodontal pocket of 4 mm or greater on periodontal histology. (2) Age: 20 years old or older and less than 80 years old (at the time of registration) (3) Gender: Any gender

Exclusion criteria

Exclusion criteria: Research subjects who, in the judgment of the dentist, would have difficulty participating in the study

Design outcomes

Primary

Measure	Time frame
Logistic regression equation with patient PISA actual values as the objective variable The diagnostic criteria by PISA is that 100 or less is healthy, 100 to 500 is pre periodontal disease, and 600 or more is moderate periodontal disease. Therefore, PISA values obtained from each subject were logit-transformed and used as the objective variable, with PISA values of 100 and 600 as the cutoff values. The quantitative values of periodontal pathogens (Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, and Filifactor alocis) by quantitative PCR and SiLL-Ha's hemoglobin, leukocyte esterase and protein values are placed as explanatory variables and logistic regression equations are calculated.	—

Measure

Time frame

Logistic regression equation with patient PISA actual values as the objective variable The diagnostic criteria by PISA is that 100 or less is healthy, 100 to 500 is pre periodontal disease, and 600 or more is moderate periodontal disease. Therefore, PISA values obtained from each subject were logit-transformed and used as the objective variable, with PISA values of 100 and 600 as the cutoff values. The quantitative values of periodontal pathogens (Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, and Filifactor alocis) by quantitative PCR and SiLL-Ha's hemoglobin, leukocyte esterase and protein values are placed as explanatory variables and logistic regression equations are calculated.

—

Secondary

Measure	Time frame
Validation of diagnostic accuracy of logistic regression equations The diagnostic accuracy of the logistic regression equation at each cutoff value will be tested by ROC analysis to detect diagnostic accuracy (accuracy, sensitivity, specificity, positive predictive value, and negative predictive value), by AUC determination, and by Hosmer-Lemeshow method to determine the fit of the regression model.	—

Measure

Time frame

Validation of diagnostic accuracy of logistic regression equations The diagnostic accuracy of the logistic regression equation at each cutoff value will be tested by ROC analysis to detect diagnostic accuracy (accuracy, sensitivity, specificity, positive predictive value, and negative predictive value), by AUC determination, and by Hosmer-Lemeshow method to determine the fit of the regression model.

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Countries

Japan

Contacts

Public ContactMasahiro Saito

Tohoku University Department of Ecological Dentistry Division of Operative Dentistry

masahiro.saito.c5@tohoku.ac.jp0227178340

Outcome results