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Predicting Implant Failure and Complications Using Cluster Analysis After Variable Selection: A Retrospective Study.

Jinlin Zhang1,2, Yufeng Gao3, Yannan Cao1,4

  • 1Department of Stomatology, Affiliated Hospital of Jiangnan University, Wuxi, People's Republic of China.

Clinical Implant Dentistry and Related Research
|June 24, 2025
PubMed
Summary

This study identified key risk factors for oral implant failure and complications using advanced statistical models. A two-step cluster analysis helps predict high-risk patients for personalized preventive care.

Keywords:
dental implantsearly failurepostoperative complicationspredictive learning modelsrisk factors

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Area of Science:

  • Dental Implantology
  • Biostatistics
  • Oral Surgery

Background:

  • Oral implant failure models are challenged by uneven data distribution and repeated measurements.
  • Developing precise risk prediction models for oral implants is crucial for clinical practice.

Purpose of the Study:

  • To explore variable selection methods for oral implant data.
  • To assess risk factors for early failure and postoperative complications.
  • To develop a risk prediction model for oral implant failure using two-step cluster analysis.

Main Methods:

  • Retrospective analysis of oral implant data.
  • Comparative analysis of Generalized Estimating Equations (GEE) and GEE with Firth penalization.
  • Application of two-step cluster analysis for subgroup identification and risk prediction.

Main Results:

  • Non-submerged healing, shorter implant length, and thinner diameter are risk factors for early failure.
  • Unhealed extraction sockets, bone substitutes, and periodontal disease history increase complication risk.
  • Two patient subgroups (high-risk and low-risk) were identified, with a predictive model showing good discrimination.

Conclusions:

  • Firth penalization improved analysis of imbalanced early failure data but was less effective for complication data.
  • A tailored approach to variable screening is necessary for different imbalanced datasets.
  • The developed two-step clustering model aids in predicting high-risk patients for early failures and complications, enabling personalized preventive strategies.