Incidence and Determinants of Dental Implant Failure: A Review of Electronic Health Records in a U.S. Dental School

Abstract

The aim of this study was to use electronic health care records (EHRs) to examine retrospectively the incidence of and attributes associated with dental implant failures necessitating implant removal in a large cohort of patients treated in the student clinics of a U.S. dental school over three and a half years. EHRs were searched for all patients who received dental implants between July 1, 2011, and December 31, 2014. Characteristics of patients and implants that were actively removed due to irrevocable failure of any etiology ("failure cohort") during this period were compared to those of all other patients who received dental implants during the same time frame ("reference cohort"). Differences in the frequency distribution of various characteristics between the failure and reference cohorts were compared. Of a total 6,129 implants placed in 2,127 patients during the study period, 179 implants (2.9%) in 120 patients (5.6%) were removed. In the multivariate analysis, presence of a removable (OR=2.86) or fixed temporary prosthesis (OR=3.71) was statistically significantly associated with increased risk for implant failure. In contrast, antibiotic coverage (pre- and post-surgery OR=0.16; post-surgery only OR=0.38) and implants of certain manufacturers were associated with lower risk of implant failure. In this sizeable cohort of patients receiving care in dental student clinics, the review of EHRs facilitated identification of multiple variables associated with implant failure resulting in removal; however, these findings do not suggest causative relationships. The adopted analytical approach can enhance quality assurance measures and may contribute to the identification of true risk factors for dental implant failure.

Peri‐implantitis prevalence, incidence rate, and risk factors: A study of electronic health records at a U.S. dental school

“…Since our first aim was to establish peri‐implantitis prevalence and incidence rate, we followed the recommendations by Casey et al () to utilize a random subset of all available records within a pre‐defined timeline. Thus, for this part of the study, we used the validated reference cohort in our earlier study of dental implant failures (Hickin et al, ) representing a random 10% sample of all patients in our EHR database that had received dental implants between a pre‐determined period of time, and for whom implant placement and characteristics were already confirmed in our previous study. Additional review of subsequent radiographs and EHR notes of these patients allowed “establish or reject” diagnosis of peri‐implantitis, enabling the data from this random sample to be used for estimates of prevalence and incidence rate.…”

“…To address the first aim (prevalence and incidence rate of peri‐implantitis), we used the validated reference cohort reported in a previous publication by our group (Hickin et al, ) that comprised all patients receiving dental implants at the Clinics of the Columbia University College of Dental Medicine (CDM), between July 1, 2011 (the inception of the EHR use at CDM) and December 31, 2014. A total of 2,127 patients received 6,129 dental implants over the above 3.5‐year period.…”

“…Although studies have successfully explored electronic health records (EHRs) for assessing the prevalence and risk factors of various oral health conditions (Acharya et al, ; Söderström, Johansson, & Sunnegårdh‐Grönberg, ), EHRs have not been widely used in the study of the prevalence and risk factors for peri‐implantitis. In a previous publication, we demonstrated the utility of EHRs in the assessment of the incidence and determinants of dental implant failure (Hickin, Shariff, Jennette, Finkelstein, & Papapanou, ). In the present work, we expand our earlier observations and use EHRs in the setting of clinical care at a US academic institution of higher education to (a) determine the prevalence and incidence rate of peri‐implantitis over a period of up to 6 years after surgical implant placement, and (b) identify risk factors for peri‐implantitis.…”

How old is old for implant therapy in terms of early implant losses?

“…In this context, a variety of factors have been associated with an increased EIL rate; examples are—but not limited to—smoking, maxillary site, male gender, short implant length, implant type/brand, number of implants, immediate placement, need of bone grafting, non‐submerged healing, history of periodontitis, the clinician and specific medication intake (Alsaadi, Quirynen, Komárek, & van Steenberghe, ; Antoun, Karouni, Abitbol, Zouiten, & Jemt, ; Berglundh, Persson, & Klinge, ; Bryant, ; Chrcanovic, Kisch, Albrektsson, & Wennerberg, ; Derks et al, ; Esposito, Grusovin, Loli, Coulthard, & Worthington, ; Hickin, Shariff, Jennette, Finkelstein, & Papapanou, ; Jemt, ; Manzano et al, ; Olate, Lyrio, de Moraes, Mazzonetto, & Moreira, ; Olmedo‐Gaya, Manzano‐Moreno, Cañaveral‐Cavero, Dios Luna‐del Castillo, & Vallecillo‐Capilla, ; Palma‐Carrió, Maestre‐Ferrín, Peñarrocha‐Oltra, Peñarrocha‐Diago, & Peñarrocha‐Diago, ; Pommer et al, ; Troiano et al, ). For example, PPI (Al Subaie et al, ; Chrcanovic, Kisch, Albrektsson, & Wennerberg, ; Wu et al, ), SSRI (Wu et al, ) and antidepressants in general (Chrcanovic et al ), which are all rather common in the elderly, have been associated with an increased risk for implant failure.…”

Using big data to promote precision oral health in the context of a learning healthcare system

“…Yoon et al used big data and deep learning algorithms on a large sample of Latino patients to identify demographic, behavioral, and psychological factors associated with tooth mobility and other indicators of oral health status in older adults . Other studies utilized big data in order to develop risk prediction profiles for development of periodontal disease, implant failure, peri‐implantitis, and alveolar osteitis . Using big EHR data, Boehm A et al uncovered patient determinants of care utilization compliance in a student dental clinic .…”