Biologic modelling of periodontal disease progression.
Aim: To investigate the synergistic role of biologic markers from saliva, serum and plaque in modelling periodontitis disease progression. Material and Methods: This longitudinal study evaluated characteristics of disease progression in 114 patients with generalized moderate to severe periodontitis....
| Publicado en: | Journal of Clinical Periodontology Vol. 46; no. 2; pp. 160 - 170 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134801753&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134801753 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03036979 8E3 jtl: Journal of Clinical Periodontology issn: 03036979 maglogo: Y pubinfo: dt: Feb2019 vid: 46 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 134801753 134801753 134801753 10.1111/jcpe.13064 134801753 ppf: 160 ppct: 10 formats: tig: atl: Biologic modelling of periodontal disease progression. aug: au: Nagarajan, Radhakrishnan Miller, Craig S. Dawson, Dolph Ebersole, Jeffrey L. affil: Division of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington Kentucky sug: subj: Periodontitis Diagnosis Disease Progression Biological Markers Blood Saliva Analysis Dental Plaque Human Prospective Studies Disease Attributes Severity of Illness Treatment Outcomes Periodontal Attachment Loss Therapy Periodontitis Therapy Dental Scaling Root Planing Oral Hygiene Algorithms Probability Discriminant Analysis Conceptual Framework Sensitivity and Specificity Matrix Metalloproteinases Analysis Amyloids Blood Arachidonic Acids Blood Gram-Negative Anaerobic Bacteria Analysis Tannerella Analysis ab: Aim: To investigate the synergistic role of biologic markers from saliva, serum and plaque in modelling periodontitis disease progression. Material and Methods: This longitudinal study evaluated characteristics of disease progression in 114 patients with generalized moderate to severe periodontitis. The primary outcome was detection of sites with progressing attachment loss sites over 6 months in patients who received scaling and root planing or oral hygiene only. The predictive potential of 27 biomarkers in serum, whole saliva and subgingival plaque was evaluated using three classification algorithms (Support Vector Machines; Naïve Bayes Classifier; and Linear Discriminant Analysis) within an ensemble predictive modelling framework. Results: Disease progression occurred in 24.6% of subjects (28/114). Predictive modelling using Naïve Bayes Classifier identified progressors best with sensitivity of ~89%. The use of the three classification algorithms revealed the concerted role of salivary matrix metalloproteinase‐8, serum biomarkers (serum amyloid P, matrix metalloproteinase 1, bactericidal permeability‐increasing protein, isoprostane) along with levels of Porphryomonas gingivalis and Tannerella forsythia in plaque in predicting progressors. Conclusions: Synergistic utility of baseline bacterial and inflammatory biomarkers from saliva, serum and plaque predicted disease progression. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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