ArticleClinical oral implants research2020
An advanced prediction model for postoperative complications and early implant failure.
Article in Clinical oral implants research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Implant Survival in Patient Populations With a Mean Age of 65-75 Years Compared to Older Cohorts: A Systematic Review and Meta-Analysis.Clinical oral implants research · 2025Pooled it
- Article
- AI-assisted preoperative surgical planning for dental implant.Journal of translational medicine · 2025Article
- Machine learning-assisted prediction of clinical responses to periodontal treatment.Journal of periodontology · 2025Article
- Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps.Bioengineering (Basel, Switzerland) · 2024Review
- An investigation of penalization and data augmentation to improve convergence of generalized estimating equations for clustered binary outcomes.BMC medical research methodology · 2022Article
- The Effect of the COVID-19 Pandemic on Patient Selection, Surgical Procedures, and Postoperative Complications in a Specialized Dental Implant Clinic.Journal of clinical medicine · 2022Article
- Solutions to problems of nonexistence of parameter estimates and sparse data bias in Poisson regression.Statistical methods in medical research · 2022Article
- Dental Implants Inserted in Fresh Extraction Sockets versus Healed Sites: A Systematic Review and Meta-Analysis.Materials (Basel, Switzerland) · 2021Review
- The Effectiveness of Osseodensification Drilling Protocol for Implant Site Osteotomy: A Systematic Review of the Literature and Meta-Analysis.Materials (Basel, Switzerland) · 2021Review
- An advanced prediction model for postoperative complications and early implant failure.Clinical oral implants research · 2020Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesRisk prediction in implant dentistry presents specific challenges including the dependence of observations from patients with multiple implants and rare outcome events. The aim of this study was to use advanced statistical methods based on penalized regression to assess risk factors in implant dentistry. MATERIAL AND
methodsWe conducted a retrospective study from January 2016 to November 2018 recording postoperative complications including bleeding, hematoma, local infection, and nerve damage, as well as early implant failure. We further assessed patient- and implant-related risk factors including smoking and diabetes, as well as treatment parameters including types of gaps and surgical procedures. Univariable and multivariable generalized estimating equation (GEE) models were estimated to assess predictor effects, and a prediction model was fitted using L1 penalized estimation (lasso).
resultsIn a total of 1,132 patients (mean age: 50.6 ± 16.5 years, 55.4% female) and 2,413 implants, postoperative complications occurred in 71 patients. Sixteen implants were lost prior to loading. Multivariable GEE models showed a higher risk of any complication for diabetes mellitus (p = .006) and bone augmentation (p = .039). The models further revealed a higher risk of local infection for bone augmentation (p = .003), and a higher risk of hematoma formation for diabetes mellitus (p = .007) and edentulous jaws (p = .024). The lasso model did not select any risk factors into the prediction model.
conclusionsUsing novel methodology well-suited to tackle the specific challenges of risk prediction in implant dentistry, we were able to reliably estimate associations of risk factors with outcomes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.