Big data could mean better outcomes for implantology

Dr Chatzopoulos, could you share with our readers what led you to explore the failure rates of dental implants on such a large scale?
My research interest in exploring dental implant complications began when I was a postgraduate student at the University of Minnesota, where we started analysing electronic health records of patients who visited the dental clinics for dental implants. Studies with small sample sizes cannot be used, as their findings are not generalisable. There, I realised that it is crucial to evaluate data on a larger scale.

What advice could you offer to researchers and clinicians involved in developing preventive care models but who are not sure where to start with working with large volumes of data for research?
It is crucial to collaborate with people who have different expertise, for example statisticians, data analysts and research coordinators. Working with other research groups that have similar research interests may also add value to a research project. In addition, before someone starts working with large data sets, they will need to identify a research question and determine the type of data that will be needed to answer it. A protocol for data collection, for inclusion and exclusion criteria, and for data analysis must be developed prior to the initiation of the study.

It is also critical to consider the clinical impact of the research question and how the research findings may improve everyday clinical practice. Therefore, the clinical implication of research using large volumes of data is of paramount importance.

In this regard, have you gathered any specific insights on collaborating with a multitude of organisations to obtain that data?
In our research, we utilised data from the BigMouth Dental Data Repository, which is an oral health database developed by dental schools that are part of the Consortium for Oral Health Research and Informatics. This multi-institutional repository is derived from electronic health records of dental institutions in the US and is aimed at improving oral health research, education and treatment.

Currently, 11 dental schools share de-identified electronic health records and collaborate closely. The participating dental schools are those of the University of Texas Health Science Center at Houston, Harvard University, Tufts University, the University of California, San Francisco, the University of Pittsburgh, the University of Michigan, the University of Colorado, Loma Linda University, the University at Buffalo, the University of Iowa and the University of Minnesota.

“The clinical implication of research using large volumes of data is of paramount importance.”

Were you surprised by any of the findings or interactions during your research?
We aimed to retrospectively evaluate the long-term implant loss rates and to identify associations between implant failure and patient-related factors in a sample of patients who had consecutively received implant therapy at ten dental universities in the US using a large database. A total of 20,842 patients who received 50,333 dental implants over a 12-year period were included in the analysis, and we concluded that the implant failure rate was 2.7% at the patient level and 1.4% at the implant level. Ethnicity and race were significantly associated with implant failure, and none of the examined systemic conditions were associated with implant loss. The low implant failure rate is in agreement with previous research, but that none of the examined systemic factors and smoking were significantly associated with implant failure was a surprise.

Possibly the quality and quantity of tobacco intake plays a key role and may explain this finding. Different findings have been reported in the literature regarding the effect of systemic diseases and implant treatment outcomes and should be re-examined in the future to establish cause-and-effect relationships. The impact of ethnicity and race on implant outcome has not been evaluated in the past, and our findings cannot be compared with others. The increased prevalence of peri-implantitis, the higher incidence of tooth loss and possibly a genetic predisposition to implant failure may explain our reported significant differences.

Do you have anything else you would like our readers to know about your research or practice or the future of implantology?
We continue to work with large-scale data sets, and we aim to answer clinically relevant questions in the field of implant dentistry and periodontics. Identifying factors that may impact implant and tooth prognosis can minimise the risk of failure and tooth loss, as well as improve the treatment outcome. Understanding potential risk factors and indicators of implant failure prior to the commencement of the treatment may assist clinicians in planning treatment accordingly and facilitate a successful long-term treatment outcome. There is still a lack of consensus on the effect of implant-, patient- and bone-related parameters on the risk of implant failure. One of the key factors for the success of implant therapy is appropriate patient selection.

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