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Related Experiment Video

Updated: Jun 14, 2025

Experimental Model of Ligature-Induced Peri-Implantitis in Mice
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Peri-implantitis Risk Assessment (PiRA) Part 2: Retrospective Study and Framework for an Evidence-Based Prediction

Marc Quirynen, Mihai Tarce, Manoetjer Siawasch

    The International Journal of Oral & Maxillofacial Implants
    |June 13, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Clinicians can now predict peri-implantitis risk using a new online tool. This evidence-based model aids in assessing dental implant candidates, improving patient outcomes and treatment planning.

    Keywords:
    biofilmbleeding on probingbruxismedentulismgeneral healthimplant locationmulticausalityonline tooloral hygieneperi-implantitisperiodontitispredictionpredictive modelrecall compliancesmokingsupportive periodontal therapysusceptibility to periodontitisteeth lost

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    Area of Science:

    • Dentistry
    • Biomedical Engineering
    • Public Health

    Background:

    • Peri-implantitis poses a significant risk to dental implant success.
    • Accurate pre-operative risk assessment is crucial for patient selection and treatment planning.

    Purpose of the Study:

    • To develop an evidence-based online tool for predicting peri-implantitis risk in dental implant candidates.
    • To create a reliable model for pre-operative risk assessment.

    Main Methods:

    • A retrospective study analyzed 460 patients with 1,432 implants.
    • Data on peri-implantitis and risk factors were collected and analyzed.
    • A predictive model was developed using logistic regression.

    Main Results:

    • Peri-implantitis affected 17% of patients.
    • For partially edentulous patients, periodontitis susceptibility, probing depth, and smoking were key risk factors.
    • For fully edentulous patients, maxillary implant placement increased risk.

    Conclusions:

    • The developed predictive model aids in pre-operative risk assessment for partially edentulous patients.
    • Further validation is needed to enhance the model's predictive power and reliability for fully edentulous patients.