An advanced prediction model for postoperative complications and early implant failure.

ABSTRACT:

Objectives

Risk 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 methods

We 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 (GE

SUBMITTER: Feher B 

PROVIDER: S-EPMC7589383 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature